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View article: Toward PDDL Planning Copilot
Toward PDDL Planning Copilot Open
Large Language Models (LLMs) are increasingly being used as autonomous agents capable of performing complicated tasks. However, they lack the ability to perform reliable long-horizon planning on their own. This paper bridges this gap by in…
View article: Budget Allocation Policies for Real-Time Multi-Agent Path Finding
Budget Allocation Policies for Real-Time Multi-Agent Path Finding Open
Multi-Agent Pathfinding (MAPF) is the problem of finding paths for a set of agents such that each agent reaches its desired destination while avoiding collisions with the other agents. Many MAPF solvers are designed to run offline, that is…
View article: Should Multi-Agent Path Finding Algorithms Coordinate Target Arrival Times?
Should Multi-Agent Path Finding Algorithms Coordinate Target Arrival Times? Open
Multi-Agent Path Finding (MAPF) deals with finding conflict-free paths for a set of agents from an initial configuration to a given target configuration. The Lifelong MAPF (LMAPF) problem is a well-studied online version of MAPF in which a…
View article: EvoGPT: Enhancing Test Suite Robustness via LLM-Based Generation and Genetic Optimization
EvoGPT: Enhancing Test Suite Robustness via LLM-Based Generation and Genetic Optimization Open
Large Language Models (LLMs) have recently emerged as promising tools for automated unit test generation. We introduce a hybrid framework called EvoGPT that integrates LLM-based test generation with evolutionary search techniques to create…
View article: A Domain-Independent Agent Architecture for Adaptive Operation in Evolving Open Worlds
A Domain-Independent Agent Architecture for Adaptive Operation in Evolving Open Worlds Open
Model-based reasoning agents are ill-equipped to act in novel situations in which their model of the environment no longer sufficiently represents the world. We propose HYDRA, a framework for designing model-based agents operating in mixed…
View article: Integrating Reinforcement Learning, Action Model Learning, and Numeric Planning for Tackling Complex Tasks
Integrating Reinforcement Learning, Action Model Learning, and Numeric Planning for Tackling Complex Tasks Open
Automated Planning algorithms require a model of the domain that specifies the preconditions and effects of each action. Obtaining such a domain model is notoriously hard. Algorithms for learning domain models exist, yet it remains unclear…
View article: Transient Multi-Agent Path Finding for Lifelong Navigation in Dense Environments
Transient Multi-Agent Path Finding for Lifelong Navigation in Dense Environments Open
Multi-Agent Path Finding (MAPF) deals with finding conflict-free paths for a set of agents from an initial configuration to a given target configuration. The Lifelong MAPF (LMAPF) problem is a well-studied online version of MAPF in which a…
View article: Novelty Accommodating Multi-Agent Planning in High Fidelity Simulated Open World
Novelty Accommodating Multi-Agent Planning in High Fidelity Simulated Open World Open
Autonomous agents operating within real-world environments often rely on automated planners to ascertain optimal actions towards desired goals or the optimization of a specified objective function. Integral to these agents are common archi…
View article: Prioritised Planning with Guarantees
Prioritised Planning with Guarantees Open
Prioritised Planning (PP) is a family of incomplete and sub-optimal algorithms for multi-agent and multi-robot navigation. In PP, agents compute collision-free paths in a fixed order, one at a time. Although fast and usually effective, PP …
View article: Crafting a Pogo Stick in Minecraft with Heuristic Search (Extended Abstract)
Crafting a Pogo Stick in Minecraft with Heuristic Search (Extended Abstract) Open
Minecraft is a widely popular video game renowned for its intricate environment. The game's open-ended design allows the creation of unique tasks and challenges for the agents, providing a broad spectrum for researchers to experiment with …
View article: Optimal and Bounded Suboptimal Any-Angle Multi-agent Pathfinding (Extended Abstract)
Optimal and Bounded Suboptimal Any-Angle Multi-agent Pathfinding (Extended Abstract) Open
Multi-agent pathfinding (MAPF) is the problem of finding a set of conflict-free paths for a set of agents. We explore how to solve MAPF problems when each agent can move between any pair of possible locations as long as traversing the line…
View article: Safe Learning of PDDL Domains with Conditional Effects
Safe Learning of PDDL Domains with Conditional Effects Open
Powerful domain-independent planners have been developed to solve various types of planning problems. These planners often require a model of the acting agent's actions, given in some planning domain description language. Manually designin…
View article: Optimal and Bounded Suboptimal Any-Angle Multi-agent Pathfinding
Optimal and Bounded Suboptimal Any-Angle Multi-agent Pathfinding Open
Multi-agent pathfinding (MAPF) is the problem of finding a set of conflict-free paths for a set of agents. Typically, the agents' moves are limited to a pre-defined graph of possible locations and allowed transitions between them, e.g. a 4…
View article: Learning Safe Action Models with Partial Observability
Learning Safe Action Models with Partial Observability Open
A common approach for solving planning problems is to model them in a formal language such as the Planning Domain Definition Language (PDDL), and then use an appropriate PDDL planner. Several algorithms for learning PDDL models from observ…
View article: Safe Learning of PDDL Domains with Conditional Effects -- Extended Version
Safe Learning of PDDL Domains with Conditional Effects -- Extended Version Open
Powerful domain-independent planners have been developed to solve various types of planning problems. These planners often require a model of the acting agent's actions, given in some planning domain description language. Manually designin…
View article: Learning Safe Numeric Planning Action Models
Learning Safe Numeric Planning Action Models Open
A significant challenge in applying planning technology to real-world problems lies in obtaining a planning model that accurately represents the problem's dynamics. Obtaining a planning model is even more challenging in mission-critical do…
View article: Multi-Agent Planning and Diagnosis with Commonsense Reasoning
Multi-Agent Planning and Diagnosis with Commonsense Reasoning Open
In multi-agent systems, multi-agent planning and diagnosis are two key subfields – multi-agent planning approaches identify plans for the agents to execute in order to reach their goals, and multi-agent diagnosis approaches identify root c…
View article: Synthesizing Priority Planning Formulae for Multi-Agent Pathfinding
Synthesizing Priority Planning Formulae for Multi-Agent Pathfinding Open
Prioritized planning is a popular approach to multi-agent pathfinding. It prioritizes the agents and then repeatedly invokes a single-agent pathfinding algorithm for each agent such that it avoids the paths of higher-priority agents. Perfo…
View article: Blame Attribution for Multi-Agent Path Finding Execution Failures
Blame Attribution for Multi-Agent Path Finding Execution Failures Open
In Multi-Agent Systems (MAS), Multi-Agent Path Finding (MAPF) is the problem of finding a conflict-free plan for a group of agents from a set of starting points to a set of target points. Deviations from this plan are standard in real-worl…
View article: Adapting to Planning Failures in Lifelong Multi-Agent Path Finding
Adapting to Planning Failures in Lifelong Multi-Agent Path Finding Open
Multi-Agent Path Finding (MAPF) is the problem of finding collision-free paths for multiple agents operating in the same environment. In Lifelong MAPF (LMAPF), these agents continuously receive new destinations, and the task is to constant…
View article: Greedy Priority-Based Search for Suboptimal Multi-Agent Path Finding
Greedy Priority-Based Search for Suboptimal Multi-Agent Path Finding Open
Multi-Agent Path Finding (MAPF) is the problem of finding collision-free paths, one for each agent, in a shared environment, while minimizing their sum of travel times. Since solving MAPF optimally is NP-hard, researchers have explored alg…
View article: Frontmatter
Frontmatter Open
This volume contains the papers accepted for presentation at ICAPS 2023, the Thirty-Third International Conference on Automated Planning and Scheduling, to be held in Prague, Czech Republic, July 8-13, 2023. The annual ICAPS conference ser…
View article: Heuristic Search for Physics-Based Problems: Angry Birds in PDDL+ [Extended Abstract]
Heuristic Search for Physics-Based Problems: Angry Birds in PDDL+ [Extended Abstract] Open
Angry Birds is a very popular game that requires reasoning about sequential actions in a continuous world with discrete exogenous events. Different versions of the game are hard computationally, and the reigning world champion is still a h…
View article: Multi Agent Path Finding under Obstacle Uncertainty
Multi Agent Path Finding under Obstacle Uncertainty Open
In multi-agent path finding (MAPF), several agents must move from their current positions to their target positions without colliding. Prior work on MAPF commonly assumed perfect knowledge of the environment. We consider a MAPF setting whe…
View article: Heuristic Search for Physics-Based Problems: Angry Birds in PDDL+
Heuristic Search for Physics-Based Problems: Angry Birds in PDDL+ Open
This paper studies how a domain-independent planner and combinatorial search can be employed to play AngryBirds, a well established AI challenge problem. To model the game, we use PDDL+, a planning language for mixed discrete/continuous do…
View article: Distributed Spectrum-Based Fault Localization
Distributed Spectrum-Based Fault Localization Open
Spectrum-Based Fault Localization (SFL) is a popular approach for diagnosing faulty systems. SFL algorithms are inherently centralized, where observations are collected and analyzed by a single diagnoser. Applying SFL to diagnose distribut…
View article: Learning Safe Numeric Action Models
Learning Safe Numeric Action Models Open
Powerful domain-independent planners have been developed to solve various types of planning problems. These planners often require a model of the acting agent's actions, given in some planning domain description language. Yet obtaining suc…
View article: A Domain-Independent Agent Architecture for Adaptive Operation in Evolving Open Worlds
A Domain-Independent Agent Architecture for Adaptive Operation in Evolving Open Worlds Open
Model-based reasoning agents are ill-equipped to act in novel situations in which their model of the environment no longer sufficiently represents the world. We propose HYDRA - a framework for designing model-based agents operating in mixe…