Author name cluster
Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.
AAAI Conference 2026 System Paper
The demo presents a tool that visualizes the acting of planning agents in dynamic environments that might be modified by "acts of nature'', The purpose of this tool is to better understand the behavior of the agent, debug agent's behavior, and for making the underlying planning concepts accessible to wider audience.
KR Conference 2025 Conference Paper
With the recent interest in the reversibility of action effects, i. e. , whether the effects of the action can be undone by applying other actions, the question arose how hard it is to reverse an action in a non-deterministic domain. With the use of phi-reversibility, the paper investigates the computational complexity of weak and strong non-deterministic action reversibility in fully observable non-deterministic domains, showing PSPACE-completeness for all weak variants in question and EXP-hardness and EXP, or NEXP memberships for strong variants.
IJCAI Conference 2025 Conference Paper
In this demonstration, we present a system that automates regression testing for video games using automated planning techniques. Traditional test scripts are a common method for testing both video games and software in general. While effective, they require manual creation and frequent updates throughout development, making the process labor-intensive. Our system eliminates this burden by automatically generating and maintaining test scripts. The test engineer only needs to define the game’s rules using the Planning Domain Definition Language (PDDL) and specify initial states and goals for individual test cases. This significantly reduces human effort while ensuring test scripts remain up to date. Additionally, our system integrates with game engine editors—supporting both Unity and Unreal to execute and evaluate test cases directly within the game. It collects detailed logs, telemetry data, and video recordings, allowing users to review test results efficiently.
KR Conference 2024 Conference Paper
Existing planning action domain model acquisition approaches consider different types of state traces from which they learn. The differences in state traces refer to the level of observability of state changes (from full to none) and whether the observations have some noise (the state changes might be inaccurately logged). However, to the best of our knowledge, all the existing approaches consider state traces in which each state change corresponds to an action specified by its name and all its parameters (all objects that are relevant to the action). Furthermore, the names and types of all the parameters of the actions to be learned are given. These assumptions are too strong. In this paper, we propose a method that learns action schema from state traces with fully observable state changes but without the parameters of actions responsible for the state changes (only action names are part of the state traces). Although we can easily deduce the number (and names) of the actions that will be in the learned domain model, we still need to deduce the number and types of the parameters of each action alongside its precondition and effects. We show that this task is at least as hard as graph isomorphism. However, our experimental evaluation on a large collection of IPC benchmarks shows that our approach is still practical as the number of required parameters is usually small. Compared to the state-of-the-art learning tools SAM and Extended SAM our new algorithm can provide better results in terms of learning action models more similar to reference models, even though it uses less information and has fewer restrictions on the input traces.
AAAI Conference 2022 Conference Paper
Effective decision making while competing for limited resources in adversarial environments is important for many real-world applications (e. g. two Taxi companies competing for customers). Decision-making techniques such as Automated planning have to take into account possible actions of adversary (or competing) agents. That said, the agent should know what the competitor will likely do and then generate its plan accordingly. In this paper we propose a novel approach for estimating strategies of the adversary (or the competitor), sampling its actions that might hinder agent’s goals by interfering with the agent’s actions. The estimated competitor strategies are used in plan generation such that agent’s actions have to be applied prior to the ones of the competitor, whose estimated times dictate the deadlines. We empirically evaluate our approach leveraging sampling of competitor’s actions by comparing it to the naive approach optimising the make-span (not taking the competing agent into account at all) and to Nash Equilibrium (mixed) strategies.
KR Conference 2021 Conference Paper
Planning and acting in dynamic environments deals with non-deterministic events that might change the state of the environment without consent of the agent. In the worst case, some events might cause the agent to become ``trapped'' in a dead-end state, which in practice might mean damage or destruction of the agent. Presence of non-deterministic events often considerably increases the number of alternatives that might occur in a single step and hence traditional non-deterministic planning techniques might not scale. In this paper, we address a class of problems where non-deterministic events represent ``cyclic phenomena''. If they interfere with the agent, they might be dangerous for it (e. g. ships cruising through the area of AUV operations). We present techniques that initially analyse the problem whether it falls within this class by considering the notion of event reversibility and if so, these techniques generate a plan such that encountered unsafe states, in which the ``cyclic phenomena'' might interfere with the agent, can be eventually crossed without any risk of ``falling'' into a dead-end state. Our approach is evaluated in the AUV and Perestroika domains.
KR Conference 2021 Short Paper
The problem of action reversibility studies whether effects of a given action can be reversed (or undone) by a sequence of (other) actions. For example, actions whose effects can be reversed cannot lead to dead-ends. In the usual settings, the problem of action reversibility is PSPACE-complete, that is, as hard as deciding plan existence. In this paper, we focus on subclasses of the action reversibility problem, universal and uniform action reversibility, where the former considers all states in which the action in question is applicable, while the latter requires a single reverting action sequence, independent of the considered states. Specifically, we study the relations between projection abstractions and the subclasses of the action reversibility problem and we show that universal uniform reversibility of a given action can be decided on projection consisting of only the variables present in the schema of the action in question.
AAAI Conference 2019 Conference Paper
Macro-operators, macros for short, are a well-known technique for enhancing performance of planning engines by providing “short-cuts” in the state space. Existing macro learning systems usually generate macros from most frequent sequences of actions in training plans. Such approach priorities frequently used sequences of actions over meaningful activities to be performed for solving planning tasks. This paper presents a technique that, inspired by resource locking in critical sections in parallel computing, learns macros capturing activities in which a limited resource (e. g. , a robotic hand) is used. In particular, such macros capture the whole activity in which the resource is “locked” (e. g. , the robotic hand is holding an object) and thus “bridge” states in which the resource is locked and cannot be used. We also introduce an “aggressive” variant of our technique that removes original operators superseded by macros from the domain model. Usefulness of macros is evaluated on several stateof-the-art planners, and a wide range of benchmarks from the learning tracks of the 2008 and 2011 editions of the International Planning Competition.
KER Journal 2018 Journal Article
Abstract The International Planning Competition (IPC) is a prominent event of the artificial intelligence planning community that has been organized since 1998; it aims at fostering the development and comparison of planning approaches, assessing the state-of-the-art in planning and identifying new challenging benchmarks. IPC has a strong impact also outside the planning community, by providing a large number of ready-to-use planning engines and testing pioneering applications of planning techniques. This paper focusses on the deterministic part of IPC 2014, and describes format, participants, benchmarks as well as a thorough analysis of the results. Generally, results of the competition indicates some significant progress, but they also highlight issues and challenges that the planning community will have to face in the future.
IJCAI Conference 2013 Conference Paper
Formulating knowledge for use in AI Planning engines is currently something of an ad-hoc process, where the skills of knowledge engineers and the tools they use may significantly influence the quality of the resulting planning application. There is little in the way of guidelines or standard procedures, however, for knowledge engineers to use when formulating knowledge into planning domain languages such as PDDL. This paper seeks to investigate this process using as a case study a road traffic accident management domain. Managing road accidents requires systematic, sound planning and coordination of resources to improve outcomes for accident victims. We have derived a set of requirements in consultation with stakeholders for the resource coordination part of managing accidents. We evaluate two separate knowledge engineering strategies for encoding the resulting planning domain from the set of requirements: (a) the traditional method of PDDL experts and text editor, and (b) a leading planning GUI with built in UML modelling tools. These strategies are evaluated using process and product metrics, where the domain model (the product) was tested extensively with a range of planning engines. The results give insights into the strengths and weaknesses of the approaches, highlight lessons learned regarding knowledge encoding, and point to important lines of research for knowledge engineering for planning.
KER Journal 2010 Journal Article
Abstract There are many approaches for solving planning problems. Many of these approaches are based on ‘brute force’ search methods and they usually do not care about structures of plans previously computed in particular planning domains. By analyzing these structures, we can obtain useful knowledge that can help us find solutions to more complex planning problems. The method described in this paper is designed for gathering macro-operators by analyzing training plans. This sort of analysis is based on the investigation of action dependencies in training plans. Knowledge gained by our method can be passed directly to planning algorithms to improve their efficiency.