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Gerhard Lakemeyer

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81 papers
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81

AAAI Conference 2026 Conference Paper

A Framework for Belief-based Programs and Their Verification (Abstract Reprint)

  • Daxin Liu
  • Gerhard Lakemeyer

Belief-based programming is a probabilistic extension of the GOLOG program family where every action and sensing result can be noisy and every test condition refers to the agent’s subjective beliefs. Inherited from GOLOG programs, the action-centered feature makes belief programs fairly suitable for high-level robot control under uncertainty. An important step before deploying such a program is to verify whether it satisfies certain properties. At least two problems exist in verifying such programs: how to formally specify program properties and what is the complexity of the verification problem. In this paper, we propose a formalism for belief programs based on a modal logic of actions and beliefs which allows us to conveniently express PCTL-like temporal properties. We also investigate the decidability and undecidability of the verification problem.

AAAI Conference 2026 Conference Paper

Decidable Multi-agent Epistemic Planning: A Situation Calculus Approach

  • Qihui Feng
  • Gerhard Lakemeyer

Multi-agent epistemic planning (MEP) is the task of generating action sequences that achieve goals specified over both the physical world and agents’ mental states. It plays an important role in research domains such as game theory, computational economics, and cognitive science. While dynamic epistemic logic (DEL) provides an expressive framework for MEP, it requires complete, model-based specifications of the initial state and action effects, and suffers from undecidability due to the unbounded nesting of beliefs. In this work, we propose a modal variant of the situation calculus that captures much of the expressive power of the DEL approach. Inspired by the cognitive concept Theory of Mind (ToM), we introduce action theories with hierarchical structures, allowing agents to reason about other agents' action theories up to bounded depths. We develop a regression method that reduces reasoning about future states to reasoning about the initial state. By preserving bounded-order ToM throughout the regression process, our approach ensures the decidability of the planning problem. Finally, we propose an algorithm to find the optimal solution, namely, to find the shortest action sequence that achieves the goal.

JAIR Journal 2025 Journal Article

A Framework for Belief-based Programs and Their Verification

  • Daxin Liu
  • Gerhard Lakemeyer

Belief-based programming is a probabilistic extension of the GOLOG program family where every action and sensing result can be noisy and every test condition refers to the agent’s subjective beliefs. Inherited from GOLOG programs, the action-centered feature makes belief programs fairly suitable for high-level robot control under uncertainty. An important step before deploying such a program is to verify whether it satisfies certain properties. At least two problems exist in verifying such programs: how to formally specify program properties and what is the complexity of the verification problem. In this paper, we propose a formalism for belief programs based on a modal logic of actions and beliefs which allows us to conveniently express PCTL-like temporal properties. We also investigate the decidability and undecidability of the verification problem.

KR Conference 2025 Conference Paper

Belief Revision in a Probabilistic Setting

  • James P. Delgrande
  • Gerhard Lakemeyer
  • Maurice Pagnucco
  • Joshua Sack

This work develops an approach to qualitative belief revision in a fully probabilistic setting. We begin with a logic where possible worlds are assigned probabilities. In this logic an agent may believe a formula is true even though the subjective probability of the formula is less than 1. 0. Similarly, after revision by a formula ϕ, the agent will believe ϕ is true, even though the agent’s subjective probability of ϕ may be less than 1. 0. We establish a correspondence with the hallmark AGM postulates for belief revision. Moreover, we use Jeffrey Conditionalisation to establish a link with iterated belief change. To this end, we develop an approach that satisfies appropriately modified Darwiche-Pearl postulates (with clear justification). Thus, we provide a connection between quantitative probabilistic approaches on the one hand and the qualitative formulation of belief change, on the other. This work holds potential for the development of practical belief revision systems by applying a (qualitative) approach to belief change in probabilistic, uncertain domains.

AAAI Conference 2025 Conference Paper

LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction

  • Er Jin
  • Qihui Feng
  • Yongli Mou
  • Gerhard Lakemeyer
  • Stefan Decker
  • Oliver Simons
  • Johannes Stegmaier

Logical image understanding involves interpreting and reasoning about the relationships and consistency within an image's visual content. This capability is essential in applications such as industrial inspection, where logical anomaly detection is critical for maintaining high-quality standards and minimizing costly recalls. Previous research in anomaly detection (AD) has relied on prior knowledge for designing algorithms, which often requires extensive manual annotations, significant computing power, and large amounts of data for training. Autoregressive, multimodal Vision Language Models (AVLMs) offer a promising alternative due to their exceptional performance in visual reasoning across various domains. Despite this, their application to logical AD remains unexplored. In this work, we investigate using AVLMs for logical AD and demonstrate that they are well-suited to the task. Combining AVLMs with format embedding and a logic reasoner, we achieve SOTA performance on public benchmarks, MVTec LOCO AD, with an AUROC of 86.0% and an F1-max of 83.7% along with explanations of the anomalies. This significantly outperforms the existing SOTA method by 18.1% in AUROC and 4.6% in F1-max score.

ECAI Conference 2025 Conference Paper

Simple Numeric Planning with Two Variables Is Decidable

  • Hayyan Helal
  • Gerhard Lakemeyer

It is known that a simple numeric planning problem (SNP) with one numeric variable is decidable but undecidable with three (Helmert 2002). A more recent result (Gnad et. al 2023) showed undecidability for two numeric and one propositional variable. In this paper, we show the decidability of SNP with exactly two numeric variables. For this, we first partition the state space into a finite number of regions and demonstrate the decidability of SNP when restricted to any of these regions. Afterwards, we develop a correct search algorithm that abstracts from these regions by tracking an infinite number of states following an arithmetic progression pattern. Finally, we prove termination of the search and draw conclusions about the reasons for undecidability for general SNP.

AAMAS Conference 2025 Conference Paper

Translating Multi-Agent Modal Logics of Knowledge and Belief into Decidable First-Order Fragments

  • Qihui Feng
  • Hannah Wilk
  • Shakil M. Khan
  • Gerhard Lakemeyer

Translation-based modal theorem proving has been studied for decades. By reducing modal formulae to fragments of first-order logic, methods developed for first-order reasoning can be applied to modal inference problems. However, the existing translation approaches are insufficient for modal systems with specific frame properties, such as transitivity or Euclideanity, since they result in formulae not in a decidable first-order fragment. With a revisit of the set-based possible-worlds semantics, we propose a new translation for multi-agent modal systems of knowledge and belief, such as 𝐾(𝐷)45𝑛 and 𝑆5𝑛. We prove that the resulting formulae of the translation are in the two-variable guarded fragment. Therefore the decidability of the general satisfiability problem is preserved and it paves the way for translation-based reasoning in these modal systems. We also extend our approach to first-order modal logic and consider a decidable fragment.

AAMAS Conference 2024 Conference Paper

Probabilistic Multi-agent Only-Believing

  • Qihui Feng
  • Gerhard Lakemeyer

Levesque introduced the notion of only-knowing to precisely capture the beliefs of a knowledge base. While numerous studies of only-knowing have emerged, such as the representation of probabilistic beliefs or reasoning about beliefs in an uncertain dynamical system, most remain confined to single-agent contexts. This limitation predominantly stems from an absence of a logical framework, which faithfully extends Levesque’s intuition of only-knowing to multi-agent, probabilistic scenarios. In this paper, we introduce a first-order logical account with probabilistic beliefs and only-believing of many agents. We demonstrate that the categorical fragment of our account forms a 𝐾𝐷45𝑛 modal system, and the notion of belief has behavior following the laws of probability. We also show how an agent’s beliefs and non-beliefs about the environment or other agents’ beliefs are precisely captured through the modalities of only-believing, which paves the way to generalize tools for interfacing with symbolic, probabilistic knowledge bases. By way of example, we demonstrate how nonmonotonic conclusions including default reasoning can be handled by our account.

AAMAS Conference 2023 Conference Paper

A Logic of Only-Believing over Arbitrary Probability Distributions

  • Qihui Feng
  • Daxin Liu
  • Vaishak Belle
  • Gerhard Lakemeyer

When it comes to robotic agents operating in an uncertain world, a major concern in knowledge representation is to better relate high-level logical accounts of beliefs and actions to the low-level probabilistic sensorimotor data. Perhaps the most general formalism for dealing with degrees of belief in formulas, and in particular, with how that should evolve in the presence of noisy sensing and acting is the first-order logical account by Bacchus, Halpern, and Levesque. The main advantage of such a logical account is that it allows a specification of beliefs that can be partial or incomplete, in keeping with whatever information is available about the domain, making it particularly attractive for general-purpose cognitive robotics. Recently, this model was extended to handle continuous probability distributions. However, it is limited to finitely many nullary fluents and defines beliefs and integration axiomatically, the latter making semantic proofs about beliefs and meta-beliefs difficult. In this paper, we revisit the continuous model and cast it in a modal language. We will go beyond nullary fluents and allow fluents of arbitrary arity as is usual in the standard situation calculus. This necessitates a new and general treatment of probabilities on possible worlds, where we define measures on uncountably many worlds that interpret infinitely many fluents. We then show how this leads to a fairly simple definition of knowing, degrees of belief, and onlyknowing. Properties thereof will also be analyzed. In this paper, we focus on the static setting and conclude with some thoughts about extending this account to actions as the next step and what challenges might arise.

KR Conference 2023 Conference Paper

Concerning Measures in a First-order Logic with Actions and Meta-beliefs

  • Daxin Liu
  • Qihui Feng
  • Vaishak Belle
  • Gerhard Lakemeyer

The unification of logic and probability has been seen as a long-standing concern in philosophy and mathematical logic. In this paper, we propose a new general probabilistic modal logic of belief and only-believing in the situation calculus. Our logic can express both continuous and discrete degrees of belief. More importantly, expressing degrees of belief for arbitrary first-order formulas in a dynamic setting is possible for the first time, going well beyond previous proposals where fluents are assumed to be nullary or discrete. We show that our notion of belief retains many of the properties known from the previous related work.

ECAI Conference 2023 Conference Paper

Verifying Belief-Based Programs via Symbolic Dynamic Programming

  • Daxin Liu 0002
  • Qinfei Huang
  • Vaishak Belle
  • Gerhard Lakemeyer

Belief-based programming is a probabilistic extension of the Golog programming language family, where every action and sensing could be noisy and every test refers to the subjective beliefs of the agent. Such characteristics make it rather suitable for robot control in a partial-observable uncertain environment. Recently, efforts have been made in providing formal semantics for belief programs and investigating the hardness of verifying belief programs. Nevertheless, a general algorithm that actually conducts the verification is missing. In this paper, we propose an algorithm based on symbolic dynamic programming to verify belief programs, an approach that generalizes the dynamic programming technique for solving (partially observable) Markov decision processes, i. e. (PO)MDP, by exploiting the symbolic structure in the solution of first-order (PO)MDPs induced by belief program execution.

IJCAI Conference 2022 Conference Paper

Epistemic Logic of Likelihood and Belief

  • James P. Delgrande
  • Joshua Sack
  • Gerhard Lakemeyer
  • Maurice Pagnucco

A major challenge in AI is dealing with uncertain information. While probabilistic approaches have been employed to address this issue, in many situations probabilities may not be available or may be unsuitable. As an alternative, qualitative approaches have been introduced to express that one event is no more probable than another. We provide an approach where an agent may reason deductively about notions of likelihood, and may hold beliefs where the subjective probability for a belief is less than 1. Thus, an agent can believe that p holds (with probability <1); and if the agent believes that q is more likely than p, then the agent will also believe q. Our language allows for arbitrary nesting of beliefs and qualitative likelihoods. We provide a sound and complete proof system for the logic with respect to an underlying probabilistic semantics, and show that the language is equivalent to a sublanguage with no nested modalities.

IROS Conference 2021 Conference Paper

Ontology-Assisted Generalisation of Robot Action Execution Knowledge

  • Alex Mitrevski
  • Paul G. Plöger
  • Gerhard Lakemeyer

When an autonomous robot learns how to execute actions, it is of interest to know if and when the execution policy can be generalised to variations of the learning scenarios. This can inform the robot about the necessity of additional learning, as using incomplete or unsuitable policies can lead to execution failures. Generalisation is particularly relevant when a robot has to deal with a large variety of objects and in different contexts. In this paper, we propose and analyse a strategy for generalising parameterised execution models of manipulation actions over different objects based on an object ontology. In particular, a robot transfers a known execution model to objects of related classes according to the ontology, but only if there is no other evidence that the model may be unsuitable. This allows using ontological knowledge as prior information that is then refined by the robot’s own experiences. We verify our algorithm for two actions - grasping and stowing everyday objects - such that we show that the robot can deduce cases in which an existing policy can generalise to other objects and when additional execution knowledge has to be acquired.

IJCAI Conference 2021 Conference Paper

Reasoning about Beliefs and Meta-Beliefs by Regression in an Expressive Probabilistic Action Logic

  • Daxin Liu
  • Gerhard Lakemeyer

In a recent paper Belle and Lakemeyer proposed the logic DS, a probabilistic extension of a modal variant of the situation calculus with a model of belief based on weighted possible worlds. Among other things, they were able to precisely capture the beliefs of a probabilistic knowledge base in terms of the concept of only-believing. While intuitively appealing, the logic has a number of shortcomings. Perhaps the most severe is the limited expressiveness in that degrees of belief are restricted to constant rational numbers, which makes it impossible to express arbitrary belief distributions. In this paper we will address this and other shortcomings by extending the language and modifying the semantics of belief and only-believing. Among other things, we will show that belief retains many but not all of the properties of DS. Moreover, it turns out that only-believing arbitrary sentences, including those mentioning belief, is uniquely satisfiable in our logic. For an interesting class of knowledge bases we also show how reasoning about beliefs and meta-beliefs after performing noisy actions and sensing can be reduced to reasoning about the initial beliefs of an agent using a form of regression.

ICRA Conference 2021 Conference Paper

Robot Action Diagnosis and Experience Correction by Falsifying Parameterised Execution Models

  • Alex Mitrevski
  • Paul G. Plöger
  • Gerhard Lakemeyer

When faced with an execution failure, an intelligent robot should be able to identify the likely reasons for the failure and adapt its execution policy accordingly. This paper addresses the question of how to utilise knowledge about the execution process, expressed in terms of learned constraints, in order to direct the diagnosis and experience acquisition process. In particular, we present two methods for creating a synergy between failure diagnosis and execution model learning. We first propose a method for diagnosing execution failures of parameterised action execution models, which searches for action parameters that violate a learned precondition model. We then develop a strategy that uses the results of the diagnosis process for generating synthetic data that are more likely to lead to successful execution, thereby increasing the set of available experiences to learn from. The diagnosis and experience correction methods are evaluated for the problem of handle grasping, such that we experimentally demonstrate the effectiveness of the diagnosis algorithm and show that corrected failed experiences can contribute towards improving the execution success of a robot.

IJCAI Conference 2021 Conference Paper

Transforming Robotic Plans with Timed Automata to Solve Temporal Platform Constraints

  • Tarik Viehmann
  • Till Hofmann
  • Gerhard Lakemeyer

Task planning for mobile robots typically uses an abstract planning domain that ignores the low-level details of the specific robot platform. Therefore, executing a plan on an actual robot often requires additional steps to deal with the specifics of the robot platform. Such a platform can be modeled with timed automata and a set of temporal constraints that need to be satisfied during execution. In this paper, we describe how to transform an abstract plan into a platform-specific action sequence that satisfies all platform constraints. The transformation procedure first transforms the plan into a timed automaton, which is then combined with the platform automata while removing all transitions that violate any constraint. We then apply reachability analysis on the resulting automaton. From any solution trace one can obtain the abstract plan extended by additional platform actions such that all platform constraints are satisfied. We describe the transformation procedure in detail and provide an evaluation in two real-world robotics scenarios.

IJCAI Conference 2021 Conference Paper

Using Platform Models for a Guided Explanatory Diagnosis Generation for Mobile Robots

  • Daniel Habering
  • Till Hofmann
  • Gerhard Lakemeyer

Plan execution on a mobile robot is inherently error-prone, as the robot needs to act in a physical world which can never be completely controlled by the robot. If an error occurs during execution, the true world state is unknown, as a failure may have unobservable consequences. One approach to deal with such failures is diagnosis, where the true world state is determined by identifying a set of faults based on sensed observations. In this paper, we present a novel approach to explanatory diagnosis, based on the assumption that most failures occur due to some robot hardware failure. We model the robot platform components with state machines and formulate action variants for the robots' actions, modelling different fault modes. We apply diagnosis as planning with a top-k planning approach to determine possible diagnosis candidates and then use active diagnosis to find out which of those candidates is the true diagnosis. Finally, based on the platform model, we recover from the occurred failure such that the robot can continue to operate. We evaluate our approach in a logistics robots scenario by comparing it to having no diagnosis and diagnosis without platform models, showing a significant improvement to both alternatives.

KR Conference 2020 Conference Paper

A First-Order Logic of Limited Belief Based on Possible Worlds

  • Gerhard Lakemeyer
  • Hector J. Levesque

In a recent paper Lakemeyer and Levesque proposed a first-order logic of limited belief to characterize the beliefs of a knowledge base (\KB). Among other things, they show that their model of belief is expressive, eventually complete, and tractable. This means, roughly, that a \KB\ may consist of arbitrary first-order sentences, that any sentence which is logically entailed by the \KB\ is eventually believed, given enough reasoning effort, and that reasoning is tractable under reasonable assumptions. One downside of the proposal is that epistemic states are defined in terms of sets of clauses, possibly containing variables, giving the logic a distinct syntactic flavour compared to the more traditional possible-world semantics found in the literature on epistemic logic. In this paper we show that the same properties as above can be obtained by defining epistemic states as sets of three-valued possible worlds. This way we are able to shed new light on those properties by recasting them using the more familiar notion of truth over possible worlds.

ECAI Conference 2020 Conference Paper

Macro Operator Synthesis for ADL Domains

  • Till Hofmann
  • Tim Niemueller
  • Gerhard Lakemeyer

A macro operator is a planning operator that is generated from a sequence of actions. Macros have mostly been used for macro planning, where the planner considers the macro as a single action and expands it into the original sequence during execution, but they can also be applied to other problems, such as maintaining a plan library. There are several approaches to macro operator generation, which differ in restrictions on the original actions and in the way they represent macros. However, all existing approaches are either restricted to STRIPS domains, only work on grounded actions, or they do not synthesize macros but consider the original sequence instead. We study the synthesis of macro operators for ADL domains. We describe how to compute the parameterized preconditions and effects of a macro operator such that they are equivalent to the preconditions and effects of the respective action sequence and prove the correctness of the synthesized macro operators based on a Situation Calculus semantics for ADL. We use the synthesis method for ADL macro planning and evaluate it on a number of domains from the IPC. As a second application, we describe how macro operator synthesis can be useful for maintaining a plan library by computing the precondition and effects of the parameterized library plans.

IROS Conference 2020 Conference Paper

Representation and Experience-Based Learning of Explainable Models for Robot Action Execution

  • Alex Mitrevski
  • Paul G. Plöger
  • Gerhard Lakemeyer

For robots acting in human-centered environments, the ability to improve based on experience is essential for reliable and adaptive operation; however, particularly in the context of robot failure analysis, experience-based improvement is practically useful only if robots are also able to reason about and explain the decisions they make during execution. In this paper, we describe and analyse a representation of execution-specific knowledge that combines (i) a relational model in the form of qualitative attributes that describe the conditions under which actions can be executed successfully and (ii) a continuous model in the form of a Gaussian process that can be used for generating parameters for action execution, but also for evaluating the expected execution success given a particular action parameterisation. The proposed representation is based on prior, modelled knowledge about actions and is combined with a learning process that is supervised by a teacher. We analyse the benefits of this representation in the context of two actions - grasping handles and pulling an object on a table -such that the experiments demonstrate that the joint relational-continuous model allows a robot to improve its execution based on experience, while reducing the severity of failures experienced during execution.

IJCAI Conference 2019 Conference Paper

A Tractable, Expressive, and Eventually Complete First-Order Logic of Limited Belief

  • Gerhard Lakemeyer
  • Hector J. Levesque

In knowledge representation, obtaining a notion of belief which is tractable, expressive, and eventually complete has been a somewhat elusive goal. Expressivity here means that an agent should be able to hold arbitrary beliefs in a very expressive language like that of first-order logic, but without being required to perform full logical reasoning on those beliefs. Eventual completeness means that any logical consequence of what is believed will eventually come to be believed, given enough reasoning effort. Tractability in a first-order setting has been a research topic for many years, but in most cases limitations were needed on the form of what was believed, and eventual completeness was so far restricted to the propositional case. In this paper, we propose a novel logic of limited belief, which has all three desired properties.

ICAPS Conference 2019 Conference Paper

Goal Reasoning in the CLIPS Executive for Integrated Planning and Execution

  • Tim Niemueller
  • Till Hofmann
  • Gerhard Lakemeyer

The close integration of planning and execution is a challenging problem. Key questions are how to organize and explicitly represent the program flow to enable reasoning about it, how to dynamically create goals from run-time information and decide on-line which to pursue, and how to unify representations used during planning and execution. In this work, we present an integrated system that uses a goal reasoning model which represents this flow and supports dynamic goal generation. With an explicit world model representation, it enables reasoning about the current state of the world, the progress of the execution flow, and what goals should be pursued – or postponed or abandoned. Our executive implements a specific goal lifecycle with compound goal types that combine sub-goals by conjunctions, disjunctions, concurrency, or that impose temporal constraints. Goals also provide a frame of reference for execution monitoring. The current system can utilize PDDL as the underlying modeling language with extensions to aid execution, and it contains well-defined extension points for domain-specific code. It has been used successfully in several scenarios.

ICAPS Conference 2018 Conference Paper

ASP-Based Time-Bounded Planning for Logistics Robots

  • Björn Schäpers
  • Tim Niemueller
  • Gerhard Lakemeyer
  • Martin Gebser
  • Torsten Schaub

Manufacturing industries are undergoing a major paradigm shift towards more autonomy. Automated planning and scheduling then becomes a necessity. The Planning and Execution Competition for Logistics Robots in Simulation held at ICAPS is based on this scenario and provides an interesting testbed. However, the posed problem is challenging as also demonstrated by the somewhat weak results in 2017. The domain requires temporal reasoning and dealing with uncertainty. We propose a novel planning system based on Answer Set Programming and the Clingo solver to tackle these problems and incentivize robot cooperation. Our results show a significant performance improvement, both, in terms of lowering computational requirements and better game metrics.

ICAPS Conference 2017 Conference Paper

Initial Results on Generating Macro Actions from a Plan Database for Planning on Autonomous Mobile Robots

  • Till Hofmann
  • Tim Niemueller
  • Gerhard Lakemeyer

Planning in an on-line robotics context has the specific requirement of a short planning duration. A property of typical contemporary scenarios is that (mobile) robots perform similar or even repeating tasks during operation. With these robot domains in mind, we propose database-driven macroplanning for STRIPS (DBMP/S) that learns macros – action sequences that frequently appear in plans – from experience for PDDL-based planners. Planning duration is improved over time by off-line processing of seed plans using a scalable database. The approach is indifferent about the specific planner by representing the resulting macros again as actions with preconditions and effects determined based on the actions contained in the macro. For some domains we have used separate planners for learning and execution exploiting their respective strengths. Initial results based on some IPC domains and a logistic robot scenario show significantly improved (over non-macro planners) or slightly better and comparable (to existing macro planners) performance.

IJCAI Conference 2017 Conference Paper

Reasoning about Probabilities in Unbounded First-Order Dynamical Domains

  • Vaishak Belle
  • Gerhard Lakemeyer

When it comes to robotic agents operating in an uncertain world, a major concern in knowledge representation is to better relate high-level logical accounts of belief and action to the low-level probabilistic sensorimotor data. Perhaps the most general formalism for dealing with degrees of belief and, in particular, how such beliefs should evolve in the presence of noisy sensing and acting is the account by Bacchus, Halpern, and Levesque. In this paper, we reconsider that model of belief, and propose a new logical variant that has much of the expressive power of the original, but goes beyond it in novel ways. In particular, by moving to a semantical account of a modal variant of the situation calculus based on possible worlds with unbounded domains and probabilistic distributions over them, we are able to capture the beliefs of a fully introspective knowledge base with uncertainty by way of an only-believing operator. The paper introduces the new logic and discusses key properties as well as examples that demonstrate how the beliefs of a knowledge base change as a result of noisy actions.

AAAI Conference 2016 Conference Paper

A First-Order Logic of Probability and Only Knowing in Unbounded Domains

  • Vaishak Belle
  • Gerhard Lakemeyer
  • Hector Levesque

Only knowing captures the intuitive notion that the beliefs of an agent are precisely those that follow from its knowledge base. It has previously been shown to be useful in characterizing knowledge-based reasoners, especially in a quanti- fied setting. While this allows us to reason about incomplete knowledge in the sense of not knowing whether a formula is true or not, there are many applications where one would like to reason about the degree of belief in a formula. In this work, we propose a new general first-order account of probability and only knowing that admits knowledge bases with incomplete and probabilistic specifications. Beliefs and non-beliefs are then shown to emerge as a direct logical consequence of the sentences of the knowledge base at a corresponding level of specificity.

AAAI Conference 2016 Conference Paper

Continual Planning in Golog

  • Till Hofmann
  • Tim Niemueller
  • Jens Claßen
  • Gerhard Lakemeyer

To solve ever more complex and longer tasks, mobile robots need to generate more elaborate plans and must handle dynamic environments and incomplete knowledge. We address this challenge by integrating two seemingly different approaches – PDDL-based planning for efficient plan generation and GOLOG for highly expressive behavior specification – in a coherent framework that supports continual planning. The latter allows to interleave plan generation and execution through assertions, which are placeholder actions that are dynamically expanded into conditional sub-plans (using classical planners) once a replanning condition is satisfied. We formalize and implement continual planning in GOLOG which was so far only supported in PDDL-based systems. This enables combining the execution of generated plans with regular GOLOG programs and execution monitoring. Experiments on autonomous mobile robots show that the approach supports expressive behavior specification combined with ef- ficient sub-plan generation to handle dynamic environments and incomplete knowledge in a unified way.

ECAI Conference 2016 Conference Paper

Decidable Reasoning in a First-Order Logic of Limited Conditional Belief

  • Christoph Schwering
  • Gerhard Lakemeyer

In a series of papers, Liu, Lakemeyer, and Levesque address the problem of decidable reasoning in expressive first-order knowledge bases. Here, we extend their ideas to accommodate conditional beliefs, as in "if she is Australian, then she presumably eats Kangaroo meat. " Perhaps the most prevalent semantics of a conditional belief is to evaluate the consequent in the most-plausible worlds consistent with the premise. In this paper, we devise a technique to approximate this notion of plausibility, and complement it with Liu, Lakemeyer, and Levesque's weak inference. Based on these ideas, we develop a logic of limited conditional belief, and provide soundness, decidability, and (for the propositional case) tractability results.

KR Conference 2016 Conference Paper

Decidable Reasoning in a Logic of Limited Belief with Function Symbols

  • Gerhard Lakemeyer
  • Hector Levesque

A principled way to study limited forms of reasoning for expressive knowledge bases is to specify the reasoning problem within a suitable logic of limited belief. Ideally such a logic comes equipped with a perspicuous semantics, which provides insights into the nature of the belief model and facilitates the study of the reasoning problem. While a number of such logics were proposed in the past, none of them is able to deal with function symbols except perhaps for the special case of logical constants. In this paper we propose a logic of limited belief with arbitrary function symbols. Among other things, we demonstrate that this form of limited belief has desirable properties such as eventual completeness for a large class of formulas and that it serves as a specification of a form of decidable reasoning for very expressive knowledge bases. s = {(p ∨ q), (¬p ∨ q)}. Here q is not believed at level 0, but it is believed at level 1 because we can split on p, which amounts to checking that q is believed at level 0 in the setups s ∪ {p} and s ∪ {¬p}, which is the case by unit propagation and subsumption. With this model of belief in hand, reasoning in the context of a knowledge base can then be phrased in terms of belief implications, that is, as the question which beliefs at any level k follow logically from believing the sentences in the knowledge base at level 0. LLL showed that this problem is decidable and often tractable for a large class of first-order disjunctive knowledge bases called proper+. In our new logic we keep the ideas of setups as semantic primitive and levels of belief corresponding to how much case analysis is allowed. Different from the earlier work we do not deal with predicates except for =, but consider arbitrary functions instead. Moreover, we introduce a new form of case analysis. To get an idea of what we are after, consider the following set of sentences KB, which can be thought of both as the explicit beliefs of an agent and a setup:

ECAI Conference 2016 Conference Paper

Interruptible Task Execution with Resumption in Golog

  • Gesche Gierse
  • Tim Niemueller
  • Jens Claßen
  • Gerhard Lakemeyer

Mobile robots should perform a growing number of tasks and react to time-critical events. Thus, the ability to interrupt a task and resume it later is crucial. While interleaved execution occurs often in robotics, existing approaches do not consider the fact that interrupting a task and resuming an interrupted task often requires intermediate steps. In this paper we present an approach to interruptible task execution with resumption. We propose INTRGOLOG which extends INDIGOLOG by task interruption and resumption through introducing new constructs to determine and fulfill the requirements of tasks. Our experiments on a service robot and in simulation show that the ability to switch to another task enables a robot to react in a swift and reliable fashion to new events.

IJCAI Conference 2015 Conference Paper

Belief Revision and Progression of Knowledge Bases in the Epistemic Situation Calculus

  • Christoph Schwering
  • Gerhard Lakemeyer
  • Maurice Pagnucco

Fundamental to reasoning about actions and beliefs is the projection problem: to decide what is believed after a sequence of actions is performed. Progression is one widely applied technique to solve this problem. In this paper we propose a novel framework for computing progression in the epistemic situation calculus. In particular, we model an agent’s preferential belief structure using conditional statements and provide a technique for updating these conditional statements as actions are performed and sensing information is received. Moreover, we show, by using the concepts of natural revision and only-believing, that the progression of a conditional knowledge base can be represented by only-believing the revised set of conditional statements. These results lay the foundations for feasible belief progression due to the uniquemodel property of only-believing.

IJCAI Conference 2015 Conference Paper

Only Knowing Meets Common Knowledge

  • Vaishak Belle
  • Gerhard Lakemeyer

Only knowing captures the intuitive notion that the beliefs of an agent are precisely those that follow from its knowledge base. While only knowing has a simple possible-world semantics in a single agent setting, the many agent case has turned out to be much more challenging. In a recent paper, we proposed an account which arguably extends only knowing to multiple agents in a natural way. However, the approach was limited in that the semantics cannot deal with infinitary notions such as common knowledge. In this work, we lift that serious limitation to obtain a first-order language with only knowing and common knowledge, allowing us to study the interaction between these notions for the very first time. By adding a simple form of public announcement, we then demonstrate how the muddy children puzzle can be cast in terms of logical implications given what is only known initially.

AAAI Conference 2015 Conference Paper

Projection in the Epistemic Situation Calculus with Belief Conditionals

  • Christoph Schwering
  • Gerhard Lakemeyer

A fundamental task in reasoning about action and change is projection, which refers to determining what holds after a number of actions have occurred. A powerful method for solving the projection problem is regression, which reduces reasoning about the future to reasoning about the initial state. In particular, regression has played an important role in the situation calculus and its epistemic extensions. Recently, a modal variant of the situation calculus was proposed, which allows an agent to revise its beliefs based on so-called belief conditionals as part of its knowledge base. In this paper, we show how regression can be extended to reduce beliefs about the future to initial beliefs in the presence of belief conditionals. Moreover, we show how any remaining belief operators can be eliminated as well, thus reducing the belief projection problem to ordinary first-order entailments.

ECAI Conference 2014 Conference Paper

A Semantic Account of Iterated Belief Revision in the Situation Calculus

  • Christoph Schwering
  • Gerhard Lakemeyer

Recently Shapiro et al. explored the notion of iterated belief revision within Reiter's version of the situation calculus. In particular, they consider a notion of belief defined as truth in the most plausible situations. To specify what an agent is willing to believe at different levels of plausibility they make use of so-called belief conditionals, which themselves neither refer to situations or plausibilities explicitly. Reasoning about such belief conditionals turns out to be complex because there may be too many models satisfying them and negative belief conditionals are also needed to obtain the desired conclusions. In this paper we show that, by adopting a notion of only-believing, these problems can be overcome. The work is carried out within a modal variant of the situation calculus with a possible-world semantics which features levels of plausibility. Among other things, we show that only-believing a knowledge base together with belief conditionals always leads to a unique model, which allows characterizing the beliefs of an agent, after any number of revisions, in terms of entailments within the logic.

KR Conference 2014 Conference Paper

Decidable Reasoning in a Fragment of the Epistemic Situation Calculus

  • Gerhard Lakemeyer
  • Hector J. Levesque

further restrictions on the inference mechanism to retain decidability. The approach by Liu and Levesque (2005) restricts the representation of what holds in the current situation to literals. Such knowledge bases can then be queried efficiently using a sound but incomplete evaluation-based reasoner proposed in (Levesque 1998). When actions have only local effects, the result of an action can be represented by progressing the current knowledge base to a new set of literals. In (Claßen and Lakemeyer 2009), the restriction to literals is relaxed by allowing disjunctions in the representation of the initial situation. Queries about what holds after actions have been performed are evaluated by first regressing them to a query about the initial situation and then appealing to a logic of limited belief for static knowledge bases with disjunctions developed by Liu et al. (2004). Here a major limitation is that sensing actions cannot be dealt with. The last two approaches have in common that they separate the reasoning task into two parts: one which addresses the dynamics of actions using progression and regression, respectively, and another which deals with querying a static knowledge base using existing techniques. A downside of this separation is that certain desirable features seem to get lost. For example, it is not at all clear how to handle sensing actions in the case of (Claßen and Lakemeyer 2009). In order to avoid such limitations, we propose a new model of limited belief, which avoids the above separation by incorporating actions directly into the model. The starting point is the model of limited belief for the static case proposed in (Lakemeyer and Levesque 2013), which itself is based on (Liu et al. 2004). The general idea behind logics of limited belief is to capture, in a semantically perspicuous way, weaker forms of logical entailment. Other examples of such approaches are (Levesque 1984b; Konolige 1986; Vardi 1986; Fagin and Halpern 1988; Fagin et al. 1990; Lakemeyer 1996; Cadoli and Schaerf 1992; Delgrande 1995). In the case of (Liu et al. 2004; Lakemeyer and Levesque 2013), the semantic primitive is a setup, a possibly infinite set of ground clauses closed under unit propagation. Roughly, the clauses in a setup can be viewed as those which the agent believes explicitly. Setups are used to give meaning to a sequence of modalities Bk, for k ≥ 0, where Bk φ should be read as “φ is believed at level k. ” For example, given a clause c, B0 c is satisfied by a setup s just in case The situation calculus is a popular formalism for reasoning about actions and change. Since the language is first-order, reasoning in the situation calculus is undecidable in general. An important question then is how to weaken reasoning in a principled way to guarantee decidability. Existing approaches either drastically limit the representation of the action theory or neglect important aspects such as sensing. In this paper we propose a model of limited belief for the epistemic situation calculus, which allows very expressive knowledge bases and handles both physical and sensing actions. The model builds on an existing approach to limited belief in the static case. We show that the resulting form of limited reasoning is sound with respect to the original epistemic situation calculus and eventually complete for a large class of formulas. Moreover, reasoning is decidable.

AAAI Conference 2014 Conference Paper

Exploring the Boundaries of Decidable Verification of Non-Terminating Golog Programs

  • Jens Classen
  • Martin Liebenberg
  • Gerhard Lakemeyer
  • Benjamin Zarriess

The action programming language GOLOG has been found useful for the control of autonomous agents such as mobile robots. In scenarios like these, tasks are often open-ended so that the respective control programs are non-terminating. Before deploying such programs on a robot, it is often desirable to verify that they meet certain requirements. For this purpose, Claßen and Lakemeyer recently introduced algorithms for the verification of temporal properties of GOLOG programs. However, given the expressiveness of GOLOG, their verification procedures are not guaranteed to terminate. In this paper, we show how decidability can be obtained by suitably restricting the underlying base logic, the effect axioms for primitive actions, and the use of actions within GOLOG programs. Moreover, we show that dropping any of these restrictions immediately leads to undecidability of the verification problem.

KR Conference 2014 Short Paper

On the Progression of Knowledge in Multiagent Systems

  • Vaishak Belle
  • Gerhard Lakemeyer

#2: Empty #1: Gold In a seminal paper, Lin and Reiter introduced the progression of basic action theories in the situation calculus. In this paper, we study the progression of knowledge in multiagent settings, where after actions, an agent updates her beliefs but also updates what she believes other agents know given what has occurred. By appealing to the notion of only knowing, we are able to avoid limitations of earlier work on multiagent progression, and obtain a new general account: we show that after an action, knowledge bases are updated in a Lin and Reiter fashion at every nesting of modalities. Consequently, recent results on the first-order definability of progression carry over to a multiagent setting without too much effort. a b Figure 1: Agents a and b look for a room with gold. given that these actions have occurred. 1 Imagine, for example, a simple domain, involving agents a and b looking for gold (Figure 1). When a observes b moving to Room 2 and looking through its window, her beliefs would be as follows: • she still does not know where the gold is; • a knows that b knows whether Room 2 has the gold, but cannot say what b now knows. In general, then, progression should say precisely how a’s knowledge and a’s beliefs about b’s knowledge would be updated after any sequence of physical and sensing actions. 2 The progression of knowledge in the single agent case is considered in (Liu and Wen 2011), but under syntactic restrictions to the initial theory, such as limitations on quantifying-in, and that knowledge does not appear negatively. The progression of knowledge is also considered in (Lakemeyer and Levesque 2009). The results here are general. By appealing to Levesque’s (1990) logic of only knowing, they obtain a simple specification: given an agent who only knows an action theory, the result of doing an action is that the agent only knows the LR progression of this theory. The progression of knowledge in the multiagent case is also considered in (Liu and Wen 2011). In addition to the above limitations, formulas such as Ki p, where p is objective, can be progressed, but not Ki Kj Ki p. That is, among other things, they cannot deal with an agent’s beliefs about what others believe about her. In this work, we substantially improve and extend these results. We provide a new general account of the progression of knowledge in multiagent systems that is essentially equivalent to the results in (Lakemeyer and Levesque 2009) when only a single agent is concerned. As a simple consequence of this work, earlier advances on tractable progression apply easily to our results.

ICAPS Conference 2014 Conference Paper

Towards Robust Task Execution for Domestic Service Robots

  • Anastassia Küstenmacher
  • Naveed Akhtar
  • Paul G. Plöger
  • Gerhard Lakemeyer

In the field of domestic service robots, recovery from faults is crucial to promote user acceptance. In this context we focus in particular on some specific faults, which arise from the interaction of a robot with its real world environment. Even a well-modelled robot may fail to perform its tasks successfully due to unexpected situations, which occur while interacting. These situations occur as deviations of properties of the objects (manipulated by the robot) from their expected values. Hence, they are experienced by the robot as external faults. In this paper we present two approaches to handle external faults which result from inadequate descriptions of a planner operator. In both approaches we assume that the robot is able to detect the occurrence of the fault at the planning level by monitoring the effects of an executed action. In our work we limit the scope of the sources of external faults to natural physical phenomena. Hence, we do not consider cases in which an external agent (e. g. another robot, a human being) is the cause of a detected fault. We apply the proposed approaches to scenarios in which the robot performs a manipulation task (pick and place).

IJCAI Conference 2013 Conference Paper

Decidable Reasoning in a Logic of Limited Belief with Introspection and Unknown Individuals

  • Gerhard Lakemeyer
  • Hector J. Levesque

There are not very many existing logics of belief which have both a perspicuous semantics and are computationally attractive. An exception is the logic SL, proposed by Liu, Lakemeyer, and Levesque, which allows for a decidable and often even tractable form of reasoning. While the language is first-order and hence quite expressive, it still has a number of shortcomings. For one, beliefs about beliefs are not addressed at all. For another, the names of individuals are rigid, that is, their identity is assumed to be known. In this paper, we show how both shortcomings can be overcome by suitably extending the language and its semantics. Among other things, we show that determining the beliefs of a certain kind of fully introspective knowledge bases is decidable and that unknown individuals in the knowledge base can be accommodated in a decidable manner as well.

IROS Conference 2012 Conference Paper

A generic robot database and its application in fault analysis and performance evaluation

  • Tim Niemueller
  • Gerhard Lakemeyer
  • Siddhartha S. Srinivasa

During operation of robots large amounts of data are produced and processed for instance in perception, actuation, or decision making. Nowadays this data is typically volatile and disposed right after use. But this data can be valuable and useful later. Therefore we propose a database system that taps into common robot middleware to record any and all data produced at run-time. We present two examples using this data in fault analysis and performance evaluation and describe real-world experiments run on the domestic service robot HERB.

ECAI Conference 2012 Conference Paper

Efficient Reasoning in Multiagent Epistemic Logics

  • Gerhard Lakemeyer
  • Yves Lespérance

In many applications, agents must reason about what other agents know, whether to coordinate with them or to come out on top in a competitive situation. However in general, reasoning in a multiagent epistemic logic such as Kn has high complexity. In this paper, we look at a restricted class of knowledge bases that are sets of modal literals. We call these proper epistemic knowledge bases (PEKBs). We show that after a PEKB has been put in prime implicate normal form (PINF), an efficient database-like query evaluation procedure can be used to check whether an arbitrary query is entailed by the PEKB. The evaluation procedure is always sound and sometimes complete. We also develop a procedure to convert a PEKB into PINF. As well, we extend our approach to deal with introspection.

KR Conference 2012 Conference Paper

Only-Knowing Meets Nonmonotonic Modal Logic

  • Gerhard Lakemeyer
  • Hector J. Levesque

made it possible to study autoepistemic reasoning within a classical monotonic logic, leading, among other things, to an axiomatic characterization of the logic in the propositional case, and a first-order account that handles quantifying-in. In subsequent work by Lakemeyer and Levesque, henceforth called LL, [2005; 2006], only-knowing was extended to capture other forms of nonmonotonic reasoning, and in particular, the default logic (DL) proposed by Reiter [1980] and a variant of AEL due to Konolige [1988]. As described by Reiter, a default rule α: β / γ has an intuitive reading of “if α is believed and it is consistent to believe β then infer that γ is true. ” Hence Konolige proposed translating the default rule into a sentence of AEL of the form Only-knowing was originally introduced by Levesque to capture the beliefs of an agent in the sense that its knowledge base is all the agent knows. When a knowledge base contains defaults Levesque also showed an exact correspondence between only-knowing and autoepistemic logic. Later these results were extended by Lakemeyer and Levesque to also capture a variant of autoepistemic logic proposed by Konolige and Reiter’s default logic. One of the benefits of such an approach is that various nonmonotonic formalisms can be compared within a single monotonic logic leading, among other things, to the first axiom system for default logic. In this paper, we will bring another large class of nonmonotonic systems, which were first studied by McDermott and Doyle, into the only-knowing fold. Among other things, we will provide the first possible-world semantics for such systems, providing a new perspective on the nature of modal approaches to nonmonotonic reasoning. Kα ∧ Mβ ⊃ γ. is valid, which can be read as “if we only know that P (a) or P (b), then we know that something is a P, but not what. ” Levesque also showed that, when the KB itself is allowed to mention K, then O captures the autoepistemic logic (AEL) proposed by Moore [1985], in the sense that the beliefs entailed by only-knowing KB are precisely those which are in all stable expansions of KB. This connection In the simplest case, M is understood as the dual of K in the sense that Mβ stands for ¬K¬β. To properly characterize DL, however, a more complex treatment of the M is needed. Nonetheless, LL were able to present a variant of only-knowing that did the job and allowed the properties of DL to be understood in terms of an underlying model of belief in a classical monotonic logic: a model theory based on possible worlds, and later, a proof theory based on axioms and rules of inference [Lakemeyer and Levesque, 2005; 2006]. 2 In this paper, we continue this work and bring another large class of nonmonotonic systems into the only-knowing fold. We investigate the so-called nonmonotonic modal systems (NMS) first introduced by McDermott and Doyle [1980], and reconstructed by Marek et al. [1993]. Roughly speaking, an NMS starts with a classical modal system of belief (like the system K or K45 or T, in the terminology of Chellas [1980]), and declares a set of formulas to be an expansion of α in the NMS if it consists of the formulas that can be derived in the modal system from α together with the assumptions ¬Kβ for those β that cannot be derived. Marek et al. show various properties of these NMS based on a variety of modal logics, including how different modal systems λ1 and λ2 can sometimes give rise to the same NMS (that is, where the λ1 -expansions coincide with the λ2 -expansions). Copyright c 2012, Association for the Advancement of Artificial Intelligence (www. aaai. org). All rights reserved. 1 In this paper, we use the terms “knowledge” and “belief” interchangeably to mean belief. 2 We remark that other nonmonotonic logics with two distinct modalities were proposed that also capture DL such as [Lin and Shoham, 1990; Lifschitz, 1994]. See [Lakemeyer and Levesque, 2005] for a discussion how these relate to LL’s work.

ECAI Conference 2012 Conference Paper

Representing Value Functions with Recurrent Binary Decision Diagrams

  • Daniel Beck
  • Gerhard Lakemeyer

The agent programming language Golog features nondeterministic constructs such as nondeterministic branching. Given an optimization theory the nondeterminism can be resolved optimally. There are techniques that allow to derive an abstract first-order description of a value function which is valid across all possible domain instances. The size of the domain may be unknown or even infinite. A finite horizon is assumed, though. That is, although the value function makes no assumptions about the size of the domain, the plans generated on the basis of the value functions are restricted to a certain length. In this paper we present a solution for this dilemma for a specific class of programs. In particular, we present a solution that allows to compute a representation of the value function for non-nested loops without requiring any restrictions on the number of loop iterations. A pleasing side effect is that our new representation of the value function is usually smaller than the representations for fixed horizons.

AAAI Conference 2011 Conference Paper

A Semantical Account of Progression in the Presence of Uncertainty

  • Vaishak Belle
  • Gerhard Lakemeyer

Building on a general theory of action by Reiter and his colleagues, Bacchus et al. give an account for formalizing degrees of belief and noisy actions in the situation calculus. Unfortunately, there is no clear solution to the projection problem for the formalism. And, while the model has epistemic features, it is not obvious what the agent’s knowledge base should look like. Also, reasoning about uncertainty essentially resorts to second-order logic. In recent work, Gabaldon and Lakemeyer remedy these shortcomings somewhat, but here too the utility seems to be restricted to queries (with action operators) about the initial theory. In this paper, we propose a fresh amalgamation of a modal fragment of the situation calculus and uncertainty, where the idea will be to update the initial knowledge base, containing both ordinary and (certain kinds of) probabilistic beliefs, when noisy actions are performed. We show that the new semantics has the right properties, and study a special case where updating probabilistic beliefs is computable. Our ideas are closely related to the Lin and Reiter notion of progression.

IJCAI Conference 2011 Conference Paper

On Progression and Query Evaluation in First-Order Knowledge Bases with Function Symbols

  • Vaishak Belle
  • Gerhard Lakemeyer

In a seminal paper, Lin and Reiter introduced the notion of progression of basic action theories. Unfortunately, progression is second-order in general. Recently, Liu and Lakemeyer improve on earlier results and show that for the local-effect and normal actions case, progression is computable but may lead to an exponential blow-up. Nevertheless, they show that for certain kinds of expressive first-order knowledge bases with disjunctive information, called proper+, it is efficient. However, answering queries about the resulting state is still undecidable. In this paper, we continue this line of research and extend proper+ to include functions. We prove that their progression wrt local-effect, normal actions, and range-restricted theories, is first-order definable and efficiently computable. We then provide a new logically sound and complete decision procedure for certain kinds of queries.

KR Conference 2010 Conference Paper

) Multi-Agent Only-Knowing Revisited

  • Vaishak Belle
  • Gerhard Lakemeyer

Levesque introduced the notion of only-knowing to precisely capture the beliefs of a knowledge base. He also showed how only-knowing can be used to formalize non-monotonic behavior within a monotonic logic. Despite its appeal, all attempts to extend only-knowing to the many agent case have undesirable properties. A belief model by Halpern and Lakemeyer, for instance, appeals to proof-theoretic constructs in the semantics and needs to axiomatize validity as part of the logic. It is also not clear how to generalize their ideas to a first-order case. In this paper, we propose a new account of multi-agent only-knowing which, for the first time, has a natural possible-world semantics for a quantified language with equality. We then provide, for the propositional fragment, a sound and complete axiomatization that faithfully lifts Levesque’s proof theory to the many agent case. We also discuss comparisons to the earlier approach by Halpern and Lakemeyer.

ECAI Conference 2010 Conference Paper

On the Verification of Very Expressive Temporal Properties of Non-terminating Golog Programs

  • Jens Claßen
  • Gerhard Lakemeyer

The agent programming language GOLOG and the underlying Situation Calculus have become popular means for the modelling and control of autonomous agents such as mobile robots. Although such agents' tasks are typically open-ended, little attention has been paid so far to the analysis of non-terminating GOLOG control programs. Recently we therefore introduced a logic that allows to express properties of Golog programs using operators from temporal logics while retaining the full first-order expressiveness of the Situation Calculus. Combining ideas from classical symbolic model checking with first-order theorem proving we presented a verification method for a restricted subclass of temporal properties. In this paper, we extend this work by considering arbitrary temporal formulas. Our algorithm is inspired by classical &Cscr; &Tscr; &Lscr; * model checking, but introduces techniques to cope with arbitrary first-order quantification.

AAAI Conference 2010 Conference Paper

Reasoning about Imperfect Information Games in the Epistemic Situation Calculus

  • Vaishak Belle
  • Gerhard Lakemeyer

Approaches to reasoning about knowledge in imperfect information games typically involve an exhaustive description of the game, the dynamics characterized by a tree and the incompleteness in knowledge by information sets. Such specifications depend on a modeler’s intuition, are tedious to draft and vague on where the knowledge comes from. Also, formalisms proposed so far are essentially propositional, which, at the very least, makes them cumbersome to use in realistic scenarios. In this paper, we propose to model imperfect information games in a new multi-agent epistemic variant of the situation calculus. By using the concept of only-knowing, the beliefs and non-beliefs of players after any sequence of actions, sensing or otherwise, can be characterized as entailments in this logic. We show how de re vs. de dicto belief distinctions come about in the framework. We also obtain a regression theorem for multi-agent beliefs, which reduces reasoning about beliefs after actions to reasoning about beliefs in the initial situation.

IJCAI Conference 2009 Conference Paper

  • Gerhard Lakemeyer
  • Hector J. Levesque

In previous work, we proposed a modal fragment of the situation calculus called ES, which fully captures Reiter’s basic action theories. ES also has epistemic features, including only-knowing, which refers to all that an agent knows in the sense of having a knowledge base. While our model of onlyknowing has appealing properties in the static case, it appears to be problematic when actions come into play. First of all, its utility seems to be restricted to an agent’s initial knowledge base. Second, while it has been shown that only-knowing correctly captures default inferences, this was only in the static case, and undesirable properties appear to arise in the presence of actions. In this paper, we remedy both of these shortcomings and propose a new dynamic semantics of only-knowing, which is closely related to Lin and Reiter’s notion of progression when actions are performed and where defaults behave properly.

IJCAI Conference 2009 Conference Paper

  • Yongmei Liu
  • Gerhard Lakemeyer

In a seminal paper, Lin and Reiter introduced the notion of progression for basic action theories in the situation calculus. Unfortunately, progression is not first-order definable in general. Recently, Vassos, Lakemeyer, and Levesque showed that in case actions have only local effects, progression is firstorder representable. However, they could show computability of the first-order representation only for a restricted class. Also, their proofs were quite involved. In this paper, we present a result stronger than theirs that for local-effect actions, progression is always first-order definable and computable. We give a very simple proof for this via the concept of forgetting. We also show first-order definability and computability results for a class of knowledge bases and actions with non-local effects. Moreover, for a certain class of local-effect actions and knowledge bases for representing disjunctive information, we show that progression is not only firstorder definable but also efficiently computable.

KR Conference 2008 Conference Paper

A Logic for Non-Terminating Golog Programs

  • Jens Classen
  • Gerhard Lakemeyer

Typical Golog programs for robot control are non-terminating. Analyzing such programs so far requires meta-theoretic arguments involving complex fix-point constructions. In this paper we propose a logic based on the situation calculus variant ES, which includes elements from branching time, dynamic and process logics and where the meaning of programs is modelled as possibly infinite sequences of actions. We show how properties of non-terminating programs can be formulated in the logic and, for a subset of it, how existing ideas from symbolic model checking in temporal logic can be applied to automatically verify program properties.

KR Conference 2008 Conference Paper

First-Order Strong Progression for Local-Effect Basic Action Theories

  • Stavros Vassos
  • Gerhard Lakemeyer
  • Hector J. Levesque

In a seminal paper Lin and Reiter introduced the notion of progression for basic action theories in the situation calculus. The idea is to replace an initial database by a new set of sentences which reflect the changes due to an action. Unfortunately, progression requires second-order logic in general. In this paper, we introduce the notion of strong progression, a slight variant of Lin and Reiter that has the intended properties, and we show that in case actions have only local effects, progression is always first-order representable. Moreover, for a restricted class of local-effect axioms we show how to construct a new database that is finite.

IJCAI Conference 2007 Conference Paper

  • Jens Cla
  • szlig; en
  • Patrick Eyerich
  • Gerhard Lakemeyer
  • Bernhard Nebel

The action language Golog has been applied successfully to the control of robots, among other things. Perhaps its greatest advantage is that a user can write programs which constrain the search for an executable plan in a flexible manner. However, when general planning is needed, Golog supports this only in principle, but does not measure up with state-of-the-art planners. In this paper we propose an integration of Golog and planning in the sense that planning problems, formulated as part of a Golog program, are solved by a modern planner during the execution of the program. Here we focus on the ADL subset of the plan language PDDL. First we show that the semantics of ADL can be understood as progression in the situation calculus, which underlies Golog, thus providing us with a correct embedding of ADL within Golog. We then show how Golog can be integrated with an existing ADL planner for closed-world initial databases and compare the performance of the resulting system with the original Golog.

KR Conference 2006 Conference Paper

Foundations for Knowledge-Based Programs using ES

  • Gerhard Lakemeyer
  • Jens Cla�en

Reiter proposed a semantics for knowledge-based Golog programs with sensing where program execution can be conditioned on tests involving explicit references to what the agent knows and does not know. An important result of this work is that reasoning about knowledge after the execution of actions can be reduced to classical reasoning from an initial first-order theory. However, it is limited in that tests can only refer to what is known about the current state, knowledge about knowledge is not considered, and the reduction does not apply to formulas with quantifying-in. This is in large part due to the choice of the underlying formalism, which is Reiter's version of the situation calculus. In this paper we show that, by moving to a new situation calculus recently proposed by Lakemeyer and Levesque, we cannot only reconstruct Reiter's foundations for knowledge-based programs but we can significantly go beyond them, which includes removing the above restrictions and more.

AAAI Conference 2006 Conference Paper

Towards an Axiom System for Default Logic

  • Gerhard Lakemeyer

Recently, Lakemeyer and Levesque proposed a logic of onlyknowing which precisely captures three forms of nonmonotonic reasoning: Moore’s Autoepistemic Logic, Konolige’s variant based on moderately grounded expansions, and Reiter’s default logic. Defaults have a uniform representation under all three interpretations in the new logic. Moreover, the logic itself is monotonic, that is, nonmonotonic reasoning is cast in terms of validity in the classical sense. While Lakemeyer and Levesque gave a model-theoretic account of their logic, a proof-theoretic characterization remained open. This paper fills that gap for the propositional subset: a sound and complete axiom system in the new logic for all three varieties of default reasoning. We also present formal derivations for some examples of default reasoning. Finally we present evidence that it is unlikely that a complete axiom system exists in the first-order case, even when restricted to the simplest forms of default reasoning.

AAAI Conference 2005 Conference Paper

Only-Knowing: Taking It Beyond Autoepistemic Reasoning

  • Gerhard Lakemeyer

The idea of only-knowing a collection of sentences has been previously shown to have a close connection with autoepistemic logic. Here we propose a more general account of only-knowing that captures not only autoepistemic logic but default logic as well. This allows us not only to study the properties of default logic in terms of an underlying model of belief, but also the relationship among different forms of nonmonotonic reasoning, all within a classical monotonic logic characterized semantically in terms of possible worlds.

IJCAI Conference 2005 Conference Paper

Semantics for a useful fragment of the situation calculus

  • Gerhard Lakemeyer
  • Hector J

In a recent paper, we presented a new logic called ES for reasoning about the knowledge, action, and perception of an agent. Although formulated using modal operators, we argued that the language was in fact a dialect of the situation calculus but with the situation terms suppressed. This allowed us to develop a clean and workable semantics for the language without piggybacking on the generic Tarski semantics for first-order logic. In this paper, we reconsider the relation between ES and the situation calculus and show how to map sentences of ES into the situation calculus. We argue that the fragment of the situation calculus represented by ES is rich enough to handle the basic action theories defined by Reiter as well as Golog. Finally, we show that in the full second-order version of ES, almost all of the situation calculus can be accommodated.

KR Conference 2004 Conference Paper

A Logic of Limited Belief for Reasoning with Disjunctive Informatiion

  • Gerhard Lakemeyer
  • Hector Levesque
  • Yongmei Liu

The goal of producing a general purpose, semantically motivated, and computationally tractable deductive reasoning service remains surprisingly elusive. By and large, approaches that come equipped with a perspicuous model theory either result in reasoners that are too limited from a practical point of view or fall off the computational cliff. In this paper, we propose a new logic of belief called SL which lies between the two extremes. We show that query evaluation based on SL for a certain form of knowledge bases with disjunctive information is tractable in the propositional case and decidable in the first-order case. Also, we present a sound and complete axiomatization for propositional SL.

KR Conference 2004 Conference Paper

Situations, si! Situation terms, no!

  • Gerhard Lakemeyer
  • Hector Levesque

The situation calculus, as proposed by McCarthy and Hayes, and developed over the last decade by Reiter and co-workers, is reconsidered. A new logical variant is proposed that captures much of the expressive power of the original, but where certain technical results are much more easily proven. This is illustrated using two existing non-trivial results: the regression theorem and the determinacy of knowledge theorem of Reiter. We also obtain a regression theorem for knowledge, and show how to reduce reasoning about knowledge and action to non-epistemic non-dynamic reasoning about the initial situation.

IJCAI Conference 1999 Conference Paper

Query Evaluation and Progression in AOL Knowledge Bases

  • Gerhard Lakemeyer
  • Hector J. Levesque

Recently Lakemeyer and Levesque proposed the logic AOC, which amalgamates both the situation calculus and Levesque\s logic of only knowing. While very expressive the practical relevance of the formalism is unclear because it heavily relies on second-order logic. In this paper we demonstrate that the picture is not as bleak as it may seem. In particular, we show that for large classes of AOL knowledge bases and queries, including epistemic ones, query evaluation requires first-order reasoning only. We also provide a simple semantic definition of progressing a knowledge base. For a particular class of knowledge bases, adapted from earlier results by Lin and Reiter, we show that progression is first-order representable and easy to compute.

AAAI Conference 1998 Conference Paper

The Interactive Museum Tour-Guide Robot

  • Wolfram Burgard
  • Dieter Fox
  • Gerhard Lakemeyer
  • Walter Steiner

This paper describes the software architecture of an autonomous tour-guide/tutor robot. This robot was recently deployed in the “Deutsches Museum Bonn, ” were it guided hundreds of visitors through the museum during a six-day deployment period. The robot’s control software integrates low-level probabilistic reasoning with high-level problem solving embedded in first order logic. A collection of software innovations, described in this paper, enabled the robot to navigate at high speeds through dense crowds, while reliably avoiding collisions with obstacles—some of which could not even be perceived. Also described in this paper is a user interface tailored towards non-expert users, which was essential for the robot’s success in the museum. Based on these experiences, this paper argues that time is ripe for the development of AI-based commercial service robots that assist people in everyday life.

TARK Conference 1996 Conference Paper

Multi-Agent Only Knowing

  • Joseph Y. Halpern
  • Gerhard Lakemeyer

Levesque introduced a notion of "only knowing", with the goal of capturing certain types of nonmonotonic reasoning. Levesque's logic dealt with only the case of a single agent. Recently, both Halpern and Lakemeyer independently attempted to extend Levesque's logic to the multi-agent case. Although there are a number of similarities in their approaches, there are some significant differences. In this paper, we reexamine the notion of only knowing, going back to first principles. In the process, we point out some problems with the earlier definitions. This leads us to reconsider what the properties of only knowing ought to be. We provide an axiom system that captures our desiderata, and show that it has a semantics that corresponds to it. The axiom system has an added feature of interest: it includes a modal operator for satisfiability, and thus provides a complete axiomatization for satisfiability in the logic K45.

IJCAI Conference 1995 Conference Paper

A Logical Account of Relevance

  • Gerhard Lakemeyer

The study of relevance has gained considerable attention recently in areas as diverse as machine learning and knowledge representation. In this paper, we focus on one particular area, namely relevance in the context of logical theories. We are interested in answering questions like: when is a sentence (or theory) relevant to a set of propositions, or, when is one set of propositions relevant to another given some background theory? The answers are given semantically in terms of a logic of belief and syntactically in terms of prime implicates. Furthermore, rather than merely adding yet another set of definitions of relevance to the many that already exist, we also show close connections to two others that were recently proposed, thus pointing to some common ground at least as far as logical relevance is concerned.

AAAI Conference 1993 Conference Paper

All They Know About

  • Gerhard Lakemeyer

We address the issue of agents reasoning about other agents’ nonmonotonic reasoning ability in the framework of a multi-agent autoepistemic logic (AEL). In single-agent AEL, nonmonotonic inferences are drawn based on all the agent knows. In a multi-agent context such as Jill reasoning about Jack’ s nonmonotonic inferences, this assumption must be abandoned since it cannot be assumed that Jill knows everything Jack knows. Given a specific subject matter like Tweety the bird, it is more realistic and sufficient if Jill only assumes to know all Jack knows about Tweety in order to arrive at Jack’ s nonmonotonic inferences about Tweety. This paper provides a formalization of all an agent knows about a certain subject mutter based on possible-world semantics in a multi-agent AEL. Besides discussing various properties of the new notion, we use it to characterize formulas that are about a subject matter in a very strong sense. While our main focus is on subject matters that consist of atomic propositions, we also address the case where agents are the subject matter.

TARK Conference 1992 Conference Paper

On Perfect Introspection With Quantifying-in

  • Gerhard Lakemeyer

Agents with perfect introspection may have incomplete beliefs about the world, but they possess complete knowledge about their own beliefs. This fact suggests that the beliefs of introspective agents should be completely determined by their objective beliefs, that is, those beliefs that are only about the domain in question and not about other beliefs. Introspection and logical reasoning alone should suffice to reconstruct all other beliefs from the objective ones. While this property has been shown to hold for propositional belief logics, there have so far only been negative results in the case of first-order belief logics with quantifying-in. In this paper we present a logic of belief with quantifying-in, where the beliefs of a perfectly introspective agent are indeed uniquely determined by the objective beliefs. The result is obtained by weakening the notion of belief of an existing logic that does not have this property.

AAAI Conference 1990 Conference Paper

Decidable Reasoning in First-Order Knowledge Bases with Perfect Introspection

  • Gerhard Lakemeyer

Since knowledge bases (KBs) are usually incomplete, they should be able to provide information regarding their own incompleteness, which requires them to introspect on what they know and do not know. An important area of research is to devise models of introspective reasoning that take into account resource limitations. Under the view that a KB is completely characterized by the set of beliefs it represents (its epistemic state), it seems natural to model KBs in terms of belief. Reasoning can then be understood as the problem of computing membership in the epistemic state of a KB. The best understood models of belief are based on possible-world semantics. However, their computational properties are unacceptable. In particular, they render reasoning in firstorder KBs undecidable. In this paper, we propose a novel model of belief, which preserves many of the advantages of possible-world semantics yet, at the same time, guarantees reasoning to be decidable, where a KB may contain sentences in full first-order logic. Moreover, such KBs have perfect knowledge about their own beliefs even though their beliefs about the world are limited.

TARK Conference 1988 Conference Paper

A Tractable Knowledge Representation Service with Full Introspection

  • Gerhard Lakemeyer
  • Hector J. Levesque

A Knowledge Representation service for a knowledge-based system (or agent) can be viewed as providing, at the very least, two operations that (a) give precise information about what is and is not believed (ASg) and (b) add new facts to the knowledge base when they become available (TELL). An appropriate model of belief for such operations should support the notion that onlycertain facts are believed, in particular those that have been added to a knowledge base via TELL. For logically omniscient and fully introspective agents, models of this kind lead to intractable ASKand TELLoperations. In this paper, we show that tractability can be retained by giving up logical omniscience, but without sacrificing full introspection. This is done within the framework of a propositional logic of belief. In particular, the logic allows us to express that onlya sentence (or finite set of them) is believed. We show that the validity of certain classes of sentences involving belief can be decided efficiently. These results are then applied to the specification of efficient TELL and ASKoperations. aFellowof The Canadian Institute for AdvancedResearch

IJCAI Conference 1987 Conference Paper

Tractable Meta-Reasoning in Propositional Logics of Belie

  • Gerhard Lakemeyer

Finding adequate semantic models of deductive reasoning is a difficult problem, if deductions are to be performed efficiently and in a semantically appropriate way. The model of reasoning provided by possible-worlds semantics has been found deficient both for computational and intuitive reasons. Existing semantic approaches that were proposed as alternatives to possible-worlds semantics either suffer from computational intractability or do not allow agents to have meta-beliefs. This work, based on relevance logic, proposes a model of belief where an agent can hold met a-beliefs and reason about them and other world knowledge efficiently. It is also shown how the model can be extended to include positive introspection without losing efficiency.

TARK Conference 1986 Conference Paper

Steps Towards a First-Order Logic of Explicit and Implicit Belief

  • Gerhard Lakemeyer

Modelling the beliefs of an agent who lacks logical omniscience has been a major concern recently. While most of the work has concentrated on propositional logics of belief, this paper primarily addresses issues raised by adding quantifiers to such logics. In particular, we are focusing on quantifying in and the distinction between "knowing what" and "knowing that". After arguing why a model of limited reasoning should preserve this distinction, we show how this can be accomplished by a semantics based on a restricted form of tautological entailment.