Patrick Doherty

AAMAS Conference 2024 Conference Paper

A Summary of the RGS ⊕ : an RDF Graph Synchronization System for Collaborative Robotics

• Cyrille Berger
• Patrick Doherty
• Piotr Rudol
• Mariusz Wzorek

In the context of collaborative robotics, distributed situation awareness is essential for supporting collective intelligence in teams of robots and human agents, where it can be used for both individual and collective decision support. This is particularly important in applications pertaining to emergency rescue and crisis management. During operational missions, data and knowledge is gathered incrementally and in different ways by heterogeneous robots and humans. This paper aims to describe an RDF Graph Synchronization System called RGS⊕. It is assumed that a dynamic set of agents provide or retrieve knowledge stored in their local RDF Graphs which are continuously synchronized between agents. The RGS⊕ System was designed to handle unreliable communication and does not depend on a static centralized infrastructure.

AIJ Journal 2024 Journal Article

Dual forgetting operators in the context of weakest sufficient and strongest necessary conditions

• Patrick Doherty
• Andrzej Szałas

Forgetting is an important concept in knowledge representation and automated reasoning with widespread applications across a number of disciplines. A standard forgetting operator, characterized in [26] in terms of model-theoretic semantics and primarily focusing on the propositional case, opened up a new research subarea. In this paper, a new operator called weak forgetting, dual to standard forgetting, is introduced and both together are shown to offer a new more uniform perspective on forgetting operators in general. Both the weak and standard forgetting operators are characterized in terms of entailment and inference, rather than a model theoretic semantics. This naturally leads to a useful algorithmic perspective based on quantifier elimination and the use of Ackermann's Lemma and its fixpoint generalization. The strong formal relationship between standard forgetting and strongest necessary conditions and weak forgetting and weakest sufficient conditions is also characterized quite naturally through the entailment-based, inferential perspective used. The framework used to characterize the dual forgetting operators is also generalized to the first-order case and includes useful algorithms for computing first-order forgetting operators in special cases. Practical examples are also included to show the importance of both weak and standard forgetting in modeling and representation.

JAAMAS Journal 2023 Journal Article

RGS \(^\oplus \): RDF graph synchronization for collaborative robotics

• Cyrille Berger
• Patrick Doherty
• Mariusz Wzorek

Abstract In the context of collaborative robotics, distributed situation awareness is essential for supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective decision support. This is particularly important in applications pertaining to emergency rescue and crisis management. During operational missions, data and knowledge is gathered incrementally and in different ways by heterogeneous robots and humans. The purpose of this paper is to describe an RDF Graph Synchronization System called RGS \(^\oplus \). It is assumed that a dynamic set of agents provide or retrieve knowledge stored in their local RDF Graphs which are continuously synchronized between agents. The RGS \(^\oplus \) System was designed to handle unreliable communication and does not rely on a static centralized infrastructure. It is capable of synchronizing knowledge as timely as possible and allows agents to access knowledge while it is incrementally acquired. A deeper empirical analysis of the RGS \(^\oplus \) System is provided that shows both its efficiency and efficacy.

AAAI Conference 2017 Conference Paper

Deep Learning Quadcopter Control via Risk-Aware Active Learning

• Olov Andersson
• Mariusz Wzorek
• Patrick Doherty

Modern optimization-based approaches to control increasingly allow automatic generation of complex behavior from only a model and an objective. Recent years has seen growing interest in fast solvers to also allow real-time operation on robots, but the computational cost of such trajectory optimization remains prohibitive for many applications. In this paper we examine a novel deep neural network approximation and validate it on a safe navigation problem with a real nano-quadcopter. As the risk of costly failures is a major concern with real robots, we propose a risk-aware resampling technique. Contrary to prior work this active learning approach is easy to use with existing solvers for trajectory optimization, as well as deep learning. We demonstrate the efficacy of the approach on a difficult collision avoidance problem with non-cooperative moving obstacles. Our findings indicate that the resulting neural network approximations are least 50 times faster than the trajectory optimizer while still satisfying the safety requirements. We demonstrate the potential of the approach by implementing a synthesized deep neural network policy on the nano-quadcopter microcontroller.

AAAI Conference 2015 Conference Paper

Model-Based Reinforcement Learning in Continuous Environments Using Real-Time Constrained Optimization

• Olov Andersson
• Fredrik Heintz
• Patrick Doherty

Reinforcement learning for robot control tasks in continuous environments is a challenging problem due to the dimensionality of the state and action spaces, time and resource costs for learning with a real robot as well as constraints imposed for its safe operation. In this paper we propose a modelbased reinforcement learning approach for continuous environments with constraints. The approach combines modelbased reinforcement learning with recent advances in approximate optimal control. This results in a bounded-rationality agent that makes decisions in real-time by efficiently solving a sequence of constrained optimization problems on learned sparse Gaussian process models. Such a combination has several advantages. No high-dimensional policy needs to be computed or stored while the learning problem often reduces to a set of lower-dimensional models of the dynamics. In addition, hard constraints can easily be included and objectives can also be changed in real-time to allow for multiple or dynamic tasks. The efficacy of the approach is demonstrated on both an extended cart pole domain and a challenging quadcopter navigation task using real data.

KR Conference 2012 Conference Paper

Temporal Composite Actions with Constraints

• Patrick Doherty
• Jonas Kvarnström
• Andrzej Szalas

Fukushima, one might restrict the use of UAVs in this mission to only those with radiation sensors and military grade hardware to minimize radiation damage to the hardware. A specification of such a mission would include a temporally constrained sequence of tasks, [t1, t2 ]monitor-radiation(area1); find-injured(area2), where ”; ” is used as a sequence operator. In terms of representation, the use of temporal duration introduces some interesting specification issues even for this simple case. For instance, it is obvious that monitor-radiation(area1) has to end before find-injured(area2). It is less obvious whether monitor-radiation(area1) has to start precisely at t1, whether find-injured(area2) has to end precisely at t2, or whether gaps should be allowed between actions in a sequence. Similar questions arise for concurrency, conditionals, loops and their combinations when specifying complex temporal tasks. One of the main issues of focus in this paper is the development of a suitable formal semantics for temporally constrained composite actions that is flexible enough to be used in the field with deployable robotic systems interacting with themselves and with human operators. In this context, tasks may be distributed among multiple robotic systems and their execution is often contextualized with constraints associated with the environmental situations in which they operate. The approach taken is to formally define temporal composite actions with constraints in Temporal Action Logic (TAL) (Doherty and Kvarnström 2008), a well established logic of action and change. It is then shown how such actions can be mapped into an executable declarative specification of tasks used in a number of deployable UAV systems using Task Specification Trees (TSTs) (Doherty, Landén, and Heintz 2010). TSTs are used in current research with delegation frameworks for cooperative robotics (Doherty and Meyer 2012; Doherty, Heintz, and Landén 2011). They provide a flexible and formal means for representing robotic behaviors, (distributed) tasks, compiled plans, etc. One research goal is to be able to specify such tasks in TAL as composite actions, verify their properties formally using the logical framework and then compile them into executable versions as TSTs. One can also reverse compile a TST into a composite action in TAL and verify its properties formally. The main representational approach taken is the following. A composite action specification will be characterized recursively using the following construct: Complex mission or task specification languages play a fundamentally important role in human/robotic interaction. In realistic scenarios such as emergency response, specifying temporal, resource and other constraints on a mission is an essential component due to the dynamic and contingent nature of the operational environments. It is also desirable that in addition to having a formal semantics, the language should be sufficiently expressive, pragmatic and abstract. The main goal of this paper is to propose a mission specification language that meets these requirements. It is based on extending both the syntax and semantics of a well-established formalism for reasoning about action and change, Temporal Action Logic (TAL), in order to represent temporal composite actions with constraints. Fixpoints are required to specify loops and recursion in the extended language. The results include a sound and complete proof theory for this extension. To ensure that the composite language constructs are adequately grounded in the pragmatic operation of robotic systems, Task Specification Trees (TSTs) and their mapping to these constructs are proposed. The expressive and pragmatic adequacy of this approach is demonstrated using an emergency response scenario.

AAMAS Conference 2011 Conference Paper

Tractable Model Checking for Fragments of Higher-Order Coalition Logic

• Patrick Doherty
• Barbara Dunin-K
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• Andrzej Sza
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A number of popular logical formalisms for representing and reasoning about the abilities of teams or coalitions of agents have been proposed beginning with the Coalition Logic (CL) of Pauly. Agotnes et al. introduced a means of succinctly expressing quantification over coalitions without compromising the computational complexity of model checking in CL by introducing Quantified Coalition Logic (QCL). QCL introduces a separate logical language for characterizing coalitions in the modal operators used in QCL. Boella et al. , increased the representational expressibility of such formalisms by introducing Higher-Order Coalition Logic (HCL), a monadic second-order logic with special set grouping operators. Tractable fragments of HCL suitable for efficient model checking have yet to be identified. In this paper, we relax the monadic restriction used in HCL and restrict ourselves to the diamond operator. We show how formulas using the diamond operator are logically equivalent to second-order formulas. This permits us to isolate and define well-behaved expressive fragments of second-order logic amenable to model-checking in PTIME. To do this, we appeal to techniques used in deductive databases and quantifier elimination. In addition, we take advantage of the monotonicity of the effectivity function resulting in exponentially more succinct representation of models. The net result is identification of highly expressible fragments of a generalized HCL where model checking can be done efficiently in PTIME.

JAAMAS Journal 2009 Journal Article

A temporal logic-based planning and execution monitoring framework for unmanned aircraft systems

• Patrick Doherty
• Jonas Kvarnström
• Fredrik Heintz

Abstract Research with autonomous unmanned aircraft systems is reaching a new degree of sophistication where targeted missions require complex types of deliberative capability integrated in a practical manner in such systems. Due to these pragmatic constraints, integration is just as important as theoretical and applied work in developing the actual deliberative functionalities. In this article, we present a temporal logic-based task planning and execution monitoring framework and its integration into a fully deployed rotor-based unmanned aircraft system developed in our laboratory. We use a very challenging emergency services application involving body identification and supply delivery as a vehicle for showing the potential use of such a framework in real-world applications. TALplanner, a temporal logic-based task planner, is used to generate mission plans. Building further on the use of TAL (Temporal Action Logic), we show how knowledge gathered from the appropriate sensors during plan execution can be used to create state structures, incrementally building a partial logical model representing the actual development of the system and its environment over time. We then show how formulas in the same logic can be used to specify the desired behavior of the system and its environment and how violations of such formulas can be detected in a timely manner in an execution monitor subsystem. The pervasive use of logic throughout the higher level deliberative layers of the system architecture provides a solid shared declarative semantics that facilitates the transfer of knowledge between different modules.

KR Conference 2008 Conference Paper

Deductive Planning with Inductive Loops

• Martin Magnusson
• Patrick Doherty

Agents plan to achieve and maintain goals. Maintenance that requires continuous action excludes the representation of plans as finite sequences of actions. If there is no upper bound on the number of actions, a simple list of actions would be infinitely long. Instead, a compact representation requires some form of looping construct. We look at a specific temporally extended maintenance goal, multiple target video surveillance, and formalize it in Temporal Action Logic. The logic's representation of time as the natural numbers suggests using mathematical induction to deductively plan to satisfy temporally extended goals. Such planning makes use of a sound and useful, but incomplete, induction rule that compactly represents the solution as a recursive fixpoint formula. Two heuristic rules overcome the problem of identifying a sufficiently strong induction hypothesis and enable an automated solution to the surveillance problem that satisfies the goal indefinitely.

KR Conference 2008 Conference Paper

Reasoning with Qualitative Preferences and Cardinalities using Generalized Circumscription

• Patrick Doherty
• Andrzej Szalas

The topic of preference modeling has recently attracted the interest of a number of sub-disciplines in artificial intelligence such as the nonmonotonic reasoning and action and change communities. The approach in these communities focuses on qualitative preferences and preference models which provide more natural representations from a~commonsense perspective. In this paper, we show how generalized circumscription can be used as a highly expressive framework for qualitative preference modeling. Generalized circumscription proposed by Lifschitz allows for predicates (and thus formulas) to be minimized relative to arbitrary pre-orders (reflexive and transitive). Although it has received little attention, we show how it may be used to model and reason about elaborate qualitative preference relations. One of the perceived weaknesses with any type of circumscription is the 2nd-order nature of the representation. The paper shows how a large variety of preference theories represented using generalized circumscription can in fact be reduced to logically equivalent first-order theories in a constructive way. Finally, we also show how preference relations represented using general circumscription can be extended with cardinality constraints and when these extensions can also be reduced to logically equivalent first-order theories.

KR Conference 2004 Conference Paper

Approximative Query Techniques for Agents using Heterogeneous Ontologies

• Patrick Doherty
• Witold Lukaszewicz
• Andrzej Szalas