Eva Onaindia

IJCAI Conference 2025 Conference Paper

Handling Infinite Domain Parameters in Planning Through Best-First Search with Delayed Partial Expansions

• Ángel Aso-Mollar
• Diego Aineto
• Enrico Scala
• Eva Onaindia

In automated planning, control parameters extend standard action representations through the introduction of continuous numeric decision variables. Existing state-of-the-art approaches have primarily handled control parameters as embedded constraints alongside other temporal and numeric restrictions, and thus have implicitly treated them as additional constraints rather than as decision points in the search space. In this paper, we propose an efficient alternative that explicitly handles control parameters as true decision points within a systematic search scheme. We develop a best-first, heuristic search algorithm that operates over infinite decision spaces defined by control parameters and prove a notion of completeness in the limit under certain conditions. Our algorithm leverages the concept of delayed partial expansion, where a state is not fully expanded but instead incrementally expands a subset of its successors. Our results demonstrate that this novel search algorithm is a competitive alternative to existing approaches for solving planning problems involving control parameters.

PRL Workshop 2024 Workshop Paper

Exploring Simultaneity: Learning Earliest-time Semantics for Automated Planning

• Ángel Aso-Mollar
• Óscar Sapena
• Eva Onaindia

In this paper, we aim to explore the potential of learning parallel plans and handle the execution of simultaneous actions in Automated Planning, in the form of Markov Decision Processes. Our objective is to investigate the upsides and downsides when learning a policy that not only involves the action to apply but also when to apply it. Concretely, we focus on guiding an agent to learn earliest-time semantics, wherein actions are to be executed as early as possible. Our solution approximates this theoretical normal form of parallel plans with an MDP that models the execution of actions along with their time steps, and rewards actions to be executed as early as possible. We solved the MDP with several methods and proved that RL adheres very well to the earliest-time semantics in the solved problems for a variety of domains, instead of other classical learning techniques.

ICAPS Conference 2023 Conference Paper

Falsification of Cyber-Physical Systems Using PDDL+ Planning

• Diego Aineto
• Enrico Scala
• Eva Onaindia
• Ivan Serina

This work explores the capabilities of current planning technologies to tackle the falsification of safety requirements for cyber-physical systems. Cyber-physical systems are systems where software and physical processes interact over time, and their requirements are commonly specified in temporal logic with time bounds. Roughly, falsification is the process of finding a trajectory of the cyber-physical system that violates the safety requirements, and it is a task typically tackled with black-box algorithms. We analyse the challenges posed by industry-driven falsification benchmarks taken from the ARCH-COMP competition, and propose a first attempt to deal with these problems through PDDL+ planning instead. Our experimental analysis on a selection of these problems provides empirical evidence on the feasibility and effectiveness of planning-based approaches, whilst also identifying the main areas of improvement.

HAXP Workshop 2023 Workshop Paper

Learning and Recognizing Human Behaviour with Relational Decision Trees

• Stanislav Sitanskiy
• Laura Sebastia
• Eva Onaindia

The recognition of activities performed by humans is crucial in human-robot interaction. However, assuming humans always follow rational behaviour in executing activities may not be accurate since individual preferences influence their decision-making. This paper proposes a method for learning human behaviour that involves capturing how humans select actions to solve problems. This behaviour is represented by a Relational Decision Tree. We define two sets of features that can be automatically extracted from the planning domain. A behaviour library is created and used to identify the behaviour followed by a person when executing a plan in a new situation. This approach allows to anticipate the person’s needs and act accordingly. The method was tested in three different domains, showing its validity.

PRL Workshop 2023 Workshop Paper

Meta-operators for Enabling Parallel Planning Using Deep Reinforcement Learning

• Ángel Aso Mollar
• Eva Onaindia

Many approaches that incorporate Reinforcement Learning to address the Planning problem typically assume a one-to-one mapping between Planning operators and RL actions. In this paper, we introduce the concept of meta-operator as a novel operator resulting from the simultaneous application of multiple planning operators, and we show that including meta-operators in the RL action space yields superior performance compared to purely sequential models. Our research aims to evaluate the performance of these models in domains where satisfactory outcomes have not been previously achieved, and to provide a thorough analysis of how the incorporation of meta-operators, and in general how the enrichment of the RL action space, enhances existing architectures. The main objective of this article is then to establish a precedent in the Planning and Reinforcement Learning community by proposing new approaches that redefine the RL action space in a manner that is more closely aligned with the Planning perspective.

EAAI Journal 2023 Journal Article

Plan commitment: Replanning versus plan repair

• Mohannad Babli
• Óscar Sapena
• Eva Onaindia

While executing its plan in a dynamic environment where multiple agents are operating, an autonomous agent may suffer a failure due to discrepancies between the expected and actual context and thus must replace its obsolete plan. In its endeavour to fix the failure and reach its original goals, the agent may unknowingly disrupt other agents executing their plans in the same environment. We present a property for plan repair called plan commitment to ensure a responsible repair policy among agents that aims to minimise the negative impact on others. We present arguments to support the claim that plan commitment is a valuable property when an agent may have made bookings and commitments to others. We then propose C-TFLAP, an implementation of a plan repair heuristic that allows adapting a failed plan to the new context while committing as much as possible to the original plan. We demonstrate empirically that: (1) our plan repair achieves more committed plans than plan-stability repair when an agent has made bookings and commitments to others, and (2) compared to typical replanning and plan-stability repair, it can reduce the revisions among agents when failures are avoidable and can decrease the time-loss otherwise. In addition, to demonstrate extensibility, we integrate context-aware knowledge extension with committed repairing to increase the agent’s chances of repairing.

JAIR Journal 2022 Journal Article

A Comprehensive Framework for Learning Declarative Action Models

• Diego Aineto
• Sergio Jiménez
• Eva Onaindia

A declarative action model is a compact representation of the state transitions of dynamic systems that generalizes over world objects. The specification of declarative action models is often a complex hand-crafted task. In this paper we formulate declarative action models via state constraints, and present the learning of such models as a combinatorial search. The comprehensive framework presented here allows us to connect the learning of declarative action models to well-known problem solving tasks. In addition, our framework allows us to characterize the existing work in the literature according to four dimensions: (1) the target action models, in terms of the state transitions they define; (2) the available learning examples; (3) the functions used to guide the learning process, and to evaluate the quality of the learned action models; (4) the learning algorithm. Last, the paper lists relevant successful applications of the learning of declarative actions models and discusses some open challenges with the aim of encouraging future research work.

IJCAI Conference 2022 Conference Paper

Explaining the Behaviour of Hybrid Systems with PDDL+ Planning

• Diego Aineto
• Eva Onaindia
• Miquel Ramirez
• Enrico Scala
• Ivan Serina

The aim of this work is to explain the observed behaviour of a hybrid system (HS). The explanation problem is cast as finding a trajectory of the HS that matches some observations. By using the formalism of hybrid automata (HA), we characterize the explanations as the language of a network of HA that comprises one automaton for the HS and another one for the observations, thus restricting the behaviour of the HS exclusively to trajectories that explain the observations. We observe that this problem corresponds to a reachability problem in model-checking, but that state-of-the-art model checkers struggle to find concrete trajectories. To overcome this issue we provide a formal mapping from HA to PDDL+ and show how to use an off-the-shelf automated planner. An experimental analysis over domains with piece-wise constant, linear and nonlinear dynamics reveals that the proposed PDDL+ approach is much more efficient than solving directly the explanation problem with model-checking solvers.

KR Conference 2021 Conference Paper

Generalized Temporal Inference via Planning

• Diego Aineto
• Sergio Jimenez
• Eva Onaindia

This paper introduces the Temporal Inference Problem (TIP), a general formulation for a family of inference problems that reason about the past, present or future state of some observed agent. A TIP builds on the models of an actor and of an observer. Observations of the actor are gathered at arbitrary times and a TIP encodes hypothesis on unobserved segments of the actor's trajectory. Regarding the last observation as the present time, a TIP enables to hypothesize about the past trajectory, future trajectory or current state of the actor. We use LTL as a language for expressing hypotheses and reduce a TIP to a planning problem which is solved with an off-the-shelf classical planner. The output of the TIP is the most likely hypothesis, the minimal cost trajectory under the assumption that the actor is rational. Our proposal is evaluated on a wide range of TIP instances defined over different planning domains.

ICAPS Conference 2020 Conference Paper

Observation Decoding with Sensor Models: Recognition Tasks via Classical Planning

• Diego Aineto
• Sergio Jiménez Celorrio
• Eva Onaindia

Observation decoding aims at discovering the underlying state trajectory of an acting agent from a sequence of observations. This task is at the core of various recognition activities that exploit planning as resolution method but there is a general lack of formal approaches that reason about the partial information received by the observer or leverage the distribution of the observations emitted by the sensors. In this paper, we formalize the observation decoding task exploiting a probabilistic sensor model to build more accurate hypothesis about the behaviour of the acting agent. Our proposal extends the expressiveness of former recognition approaches by accepting observation sequences where one observation of the sequence can represent the reading of more than one variable, thus enabling observations over actions and partially observable states simultaneously. We formulate the probability distribution of the observations perceived when the agent performs an action or visits a state as a classical cost planning task that is solved with an optimal planner. The experiments will show that exploiting a sensor model increases the accuracy of predicting the agent behaviour in four different contexts.

AIJ Journal 2019 Journal Article

Learning action models with minimal observability

• Diego Aineto
• Sergio Jiménez Celorrio
• Eva Onaindia

This paper presents FAMA, a novel approach for learning Strips action models from observations of plan executions that compiles the learning task into a classical planning task. Unlike all existing learning systems, FAMA is able to learn when the actions of the plan executions are partially or totally unobservable and information on intermediate states is partially provided. This flexibility makes FAMA an ideal learning approach in domains where only sensoring data are accessible. Additionally, we leverage the compilation scheme and extend it to come up with an evaluation method that allows us to assess the quality of a learned model syntactically, that is, with respect to the actual model; and, semantically, that is, with respect to a set of observations of plan executions. We also show that the extended compilation scheme can be used to lay the foundations of a framework for action model comparison. FAMA is exhaustively evaluated over a wide range of IPC domains and its performance is compared to ARMS, a state-of-the-art benchmark in action model learning.

ICAPS Conference 2019 Conference Paper

Model Recognition as Planning

• Diego Aineto
• Sergio Jiménez Celorrio
• Eva Onaindia
• Miquel Ramírez

Given a partially observed plan execution, and a set of possible planning models (models that share the same state variables but different action schemata), model recognition is the task of identifying the model that explains the observation. The paper formalizes this task and introduces a novel method that estimates the probability of a STRIPS model to produce an observation of a plan execution. This method builds on top of off-the-shelf classical planning algorithms and it is robust to missing actions and intermediate states in the observation. The effectiveness of the method is tested in three experiments, each encoding a set of different STRIPS models and all using empty-action observations: (1) a classical string classification task; (2) identification of the model that encodes a failure present in an observation; and (3) recognition of a robot navigation policy.

AIIM Journal 2019 Journal Article

Personalized conciliation of clinical guidelines for comorbid patients through multi-agent planning

• Juan Fdez-Olivares
• Eva Onaindia
• Luis Castillo
• Jaume Jordán
• Juan Cózar

The conciliation of multiple single-disease guidelines for comorbid patients entails solving potential clinical interactions, discovering synergies in the diagnosis and the recommendations, and managing clinical equipoise situations. Personalized conciliation of multiple guidelines considering additionally patient preferences brings some further difficulties. Recently, several works have explored distinct techniques to come up with an automated process for the conciliation of clinical guidelines for comorbid patients but very little attention has been put in integrating the patient preferences into this process. In this work, a Multi-Agent Planning (MAP) framework that extends previous work on single-disease temporal Hierarchical Task Networks (HTN) is proposed for the automated conciliation of clinical guidelines with patient-centered preferences. Each agent encapsulates a single-disease Computer Interpretable Guideline (CIG) formalized as an HTN domain and conciliates the decision procedures that encode the clinical recommendations of its CIG with the decision procedures of the other agents’ CIGs. During conciliation, drug-related interactions, scheduling constraints as well as redundant actions and multiple support interactions are solved by an automated planning process. Moreover, the simultaneous application of the patient preferences in multiple diseases may potentially bring about contradictory clinical decisions and more interactions. As a final step, the most adequate personalized treatment plan according to the patient preferences is selected by a Multi-Criteria Decision Making (MCDM) process. The MAP approach is tested on a case study that builds upon a simplified representation of two real clinical guidelines for Diabetes Mellitus and Arterial Hypertension.

ICAPS Conference 2018 Conference Paper

Learning STRIPS Action Models with Classical Planning

• Diego Aineto
• Sergio Jiménez Celorrio
• Eva Onaindia

This paper presents a novel approach for learning strips action models from examples that compiles this inductive learning task into a classical planning task. Interestingly, the compilation approach is flexible to different amounts of available input knowledge; the learning examples can range from a set of plans (with their corresponding initial and final states) to just a pair of initial and final states (no intermediate action or state is given). Moreover, the compilation accepts partially specified action models and it can be used to validate whether the observation of a plan execution follows a given strips action model, even if this model is not fully specified.

ECAI Conference 2016 Conference Paper

Planning Tourist Agendas for Different Travel Styles

• Jesús Ibáñez-Ruiz
• Laura Sebastiá
• Eva Onaindia

This paper describes e-Tourism2. 0, a web-based recommendation and planning system for tourism activities that takes into account the preferences that define the travel style of the user. e-Tourism2. 0 features a recommender system with access to various web services in order to obtain updated information about locations, monuments, opening hours, or transportation modes. The planning system of e-Tourism2. 0 models the taste and travel style preferences of the user and creates a planning problem which is later solved by a planner, returning a personalized plan (agenda) for the tourist. e-Tourism2. 0 contributes with a special module that calculates the recommendable duration of a visit for a user and the modeling of preferences into a planning problem.

AAAI Conference 2015 Conference Paper

Game-Theoretic Approach for Non-Cooperative Planning

• Jaume Jordán
• Eva Onaindia

When two or more self-interested agents put their plans to execution in the same environment, conflicts may arise as a consequence, for instance, of a common utilization of resources. In this case, an agent can postpone the execution of a particular action, if this punctually solves the conflict, or it can resort to execute a different plan if the agent’s payoff significantly diminishes due to the action deferral. In this paper, we present a game-theoretic approach to non-cooperative planning that helps predict before execution what plan schedules agents will adopt so that the set of strategies of all agents constitute a Nash equilibrium. We perform some experiments and discuss the solutions obtained with our game-theoretical approach, analyzing how the conflicts between the plans determine the strategic behavior of the agents.

ICAPS Conference 2015 Conference Paper

Global Heuristics for Distributed Cooperative Multi-Agent Planning

• Alejandro Torreño
• Óscar Sapena
• Eva Onaindia

Almost every planner needs good heuristics to be efficient. Heuristic planning has experienced an impressive progress over the last years thanks to the emergence of more and more powerful estimators. However, this progress has not been translated to multi-agent planning (MAP) due to the difficulty of applying classical heuristics in distributed environments. The application of local search heuristics in each agent has been the most widely adopted approach in MAP but there exist some recent attempts to use global heuristics. In this paper we show that the success of global heuristics in MAP depends on a proper selection of heuristics for a distributed environment as well as on their adequate combination.

ICAPS Conference 2014 Conference Paper

On the Use of Temporal Landmarks for Planning with Deadlines

• Eliseo Marzal
• Laura Sebastiá
• Eva Onaindia

In this paper we present a temporal planning approach for handling problems with deadlines. The model relies on the extraction of temporal landmarks from the problem and the construction of a landmarks graph as a skeleton of the solution plan. A temporal landmark is a proposition that must be achieved in a solution plan to satisfy the problem deadline constraints. Each temporal landmark is associated to three temporal intervals, which are updated and propagated according to the landmarks orders and the deadline constraints. Then, the partial plans in the search tree that are not compliant with the information comprised in this graph are pruned. The experimental results will show that this approach is helpful to quickly detect unsolvable problems and it is also very effective to solve problems with deadlines in comparison to other state-of-the-art planners.

IS Journal 2013 Journal Article

Assembling Learning Objects for Personalized Learning: An AI Planning Perspective

• Antonio Garrido
• Eva Onaindia

The aim of educational systems is to assemble learning objects on a set of topics tailored to the goals and individual students' styles. Given the amount of available Learning Objects, the challenge of e-learning is to select the proper objects, define their relationships, and adapt their sequencing to the specific needs, objectives, and background of the student. This article describes the general requirements for course adaptation, the full potential of applying planning techniques on the construction of personalized e-learning routes, and how to accommodate the temporal and resource constraints to make the course applicable in a real scenario.

ECAI Conference 2012 Conference Paper

An approach to multi-agent planning with incomplete information

• Alejandro Torreño
• Eva Onaindia
• Óscar Sapena

Multi-agent planning (MAP) approaches have been typically conceived for independent or loosely-coupled problems to enhance the benefits of distributed planning between autonomous agents as solving this type of problems require less coordination between the agents' sub-plans. However, when it comes to tightly-coupled agents' tasks, MAP has been relegated in favour of centralized approaches and little work has been done in this direction. In this paper, we present a general-purpose MAP capable to efficiently handle planning problems with any level of coupling between agents. We propose a cooperative refinement planning approach, built upon the partial-order planning paradigm, that allows agents to work with incomplete information and to have incomplete views of the world, i. e. being ignorant of other agents' information, as well as maintaining their own private information. We show various experiments to compare the performance of our system with a distributed CSP-based MAP approach over a suite of problems.

AAMAS Conference 2012 Conference Paper

Defeasible Argumentation for Multi-Agent Planning in Ambient Intelligence Applications

• Sergio Pajares Ferrando
• Eva Onaindia

This contribution presents a practical extension of a theoretical model for multi-agent planning based upon DeLP, an argumentation-based defeasible logic. Our framework, named DeLP-MAPOP, is implemented on a platform for open multi-agent systems and has been experimentally tested, among others, in applications of ambient intelligence in the field of health-care. DeLP-MAPOP is based on a multi-agent partial order planning paradigm in which agents have diverse abilities, use an argumentation-based defeasible reasoning to support their own beliefs and refute the beliefs of the others according to their knowledge during the plan search process. The requirements of Ambient Intelligence (AmI) environments featured by the imperfect nature of the context information and heterogeneity of the involved agents make defeasible argumentation be an ideal approach to resolve potential conflicts caused by the contradictory information coming from the ambient agents. Moreover, the ability of AmI systems to build a course of action to achieve the user's needs is also a claiming capability in such systems. DeLP-MAPOP shows to be an adequate approach to tackle AmI problems as it gathers together in a single framework the ability of planning while it allows agents to put forward arguments that support or argue upon the accuracy, unambiguity and reliability of the context-aware information.

AAMAS Conference 2011 Conference Paper

Multiagent Argumentation for Cooperative Planning in DeLP-POP

• Pere Pardo
• Sergio Pajares
• Eva Onaindia
• Pilar Dellunde
• Llu
• iacute; s Godo

This contribution proposes a model for argumentation-based multi-agent planning, with a focus on cooperative scenarios. It consists in a multi-agent extension of DeLP-POP, partial order planning on top of argumentation-based defeasible logic programming. In DeLP-POP, actions and arguments (combinations of rules and facts) may be used to enforce some goal, if their conditions (are known to) apply and arguments are not defeated by other arguments applying. In a cooperative planning problem a team of agents share a set of goals but have diverse abilities and beliefs. In order to plan for these goals, agents start a stepwise dialogue consisting of exchanges of plan proposals, plus arguments against them. Since these dialogues instantiate an A search algorithm, these agents will find a solution if some solution exists, and moreover, it will be provably optimal (according to their knowledge).

AAMAS Conference 2011 Conference Paper

On the Construction of Joint Plans through Argumentation Schemes

• Oscar Sapena
• Alejandro Torre
• ntilde; o
• Eva Onaindia

The term Multi-Agent Planning (MAP) refers to any kind of planning in domains in which several independent agents plan and act together. In this paper, we present a novel argumentation-based approach for multiple agents that plan cooperatively while having different abilities, different (and possibly conflicting) views of the world, and different rationalities. The argumentation-based partial-order planning model allows agents to solve MAP problems by proposing partial solutions, giving out opinions on the adequacy of these proposals and modifying them to the benefit of the overall process. We propose the use of a presumptive argumentation model based on the instantiation of argument schemes and associated critical questions to a MAP context.

IJCAI Conference 2011 Conference Paper

Temporal Defeasible Argumentation in Multi-Agent Planning

• Sergio Pajares
• Eva Onaindia

In this paper, I present my ongoing research on temporal defeasible argumentation-based multi-agent planning. In multi-agent planning a team of agents share a set of goals but have diverse abilities and temporal beliefs, which vary over time. In order to plan for these goals, agents start a stepwise dialogue consisting of exchanges of temporal plan proposals, plus temporal arguments against them, where both, actions with different duration, and temporal defeasible arguments, need to be integrated. This thesis proposes a computational framework for this research on multi-agent planning.

ECAI Conference 2010 Conference Paper

On the use of Argumentation in Multi-Agent Planning

• Óscar Sapena
• Eva Onaindia
• Alejandro Torreño

EAAI Journal 2008 Journal Article

A distributed CSP approach for collaborative planning systems

• Oscar Sapena
• Eva Onaindia
• Antonio Garrido
• Marlene Arangu

Distributed or multi-agent planning extends classical AI planning to domains where several agents can plan and act together. There exist many recent developments in this discipline that range over different approaches for distributed planning algorithms, distributed plan execution processes or communication protocols among agents. One of the key issues about distributed planning is that it is the most appropriate way to tackle certain kind of planning problems, specially those where a centralized solving is unfeasible. In this paper we present a new planning framework aimed at solving planning problems in inherently distributed domains where agents have a collection of private data which cannot share with other agents. However, collaboration is required since agents are unable to accomplish its own tasks alone or, at least, can accomplish its tasks better when working with others. Our proposal motivates a new planning scheme based on a distributed heuristic search and a constraint programming resolution process.

ECAI Conference 2008 Conference Paper

Detection of unsolvable temporal planning problems through the use of landmarks

• Eliseo Marzal
• Laura Sebastiá
• Eva Onaindia

Deadline constraints have been recently introduced in PDDL3. 0. The results obtained in the constraints domains in the last Planning Competition show that planners are not yet fully competitive. When dealing with deadline constraints the number of feasible solutions for a problem is reduced and thus it is specially relevant the ability to detect unsolvability. In this paper we present a new approach, based on the use of temporal landmarks, for the detection of unsolvable temporal planning problems.

EAAI Journal 2008 Journal Article

Planning and scheduling in an e-learning environment. A constraint-programming-based approach

• Antonio Garrido
• Eva Onaindia
• Oscar Sapena

AI planning techniques offer very appealing possibilities for their application to e-learning environments. After all, dealing with course designs, learning routes and tasks keeps a strong resemblance with a planning process and its main components aimed at finding which tasks must be done and when. This paper focuses on planning learning routes under a very expressive constraint programming approach for planning. After presenting the general planning formulation based on constraint programming, we adapt it to an e-learning setting. This requires to model learners profiles, learning concepts, how tasks attain concepts at different competence levels, synchronisation constraints for working-group tasks, capacity resource constraints, multi-criteria optimisation, breaking symmetry problems and designing particular heuristics. Finally, we also present a simple example (modelled by means of an authoring tool that we are currently implementing) which shows the applicability of this model, the use of different optimisation metrics, heuristics and how the resulting learning routes can be easily generated.

ECAI Conference 2004 Conference Paper

Concurrent Planning by Decomposition

• Laura Sebastiá
• Eva Onaindia
• Eliseo Marzal

IJCAI Conference 2003 Conference Paper

On the application of least-commitment and heuristic search in temporal planning

• Antonio Garrido
• Eva Onaindia

Graphplan planning graphs are structures widely used in modern planners. The exclusion relations calculated in the planning graph extension provide very useful information, especially in temporal planning where actions have different duration. However, Graphplan backward search has some inefficiencies that impose limitations when dealing with large temporal problems. This paper presents a new search process for temporal planning to avoid these inefficiencies. This search uses the information of a planning graph and shows beneficial in the scalability of the planner. Moreover, our experiments show that a planner with this new search is competitive with other state-of-the-art planners w. r. t. the plan quality.

ECAI Conference 2000 Conference Paper

A Graph-based Approach for POCL Planning

• Laura Sebastiá
• Eva Onaindia
• Eliseo Marzal