26 citations · 43 across the 25 of their papers we have counts for
23 papers · 1 filter
RAMP: Hybrid DRL for Online Learning of Numeric Action Models
Yarin Benyamin, Argaman Mordoch, Shahaf S. Shperberg +1
Automated planning algorithms require an action model specifying the preconditions and effects of each action, but obtaining such a model is often hard. Learning action models from…
Toward PDDL Planning Copilot
Yarin Benyamin, Argaman Mordoch, Shahaf S. Shperberg +1
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-horiz…
Enhancing Lifelong Multi-Agent Path-finding by Using Artificial Potential Fields
Arseniy Pertzovsky, Roni Stern, Ariel Felner +1
We explore the use of Artificial Potential Fields (APFs) to solve Multi-Agent Path Finding (MAPF) and Lifelong MAPF (LMAPF) problems. In MAPF, a team of agents must move to their g…
Integrating Reinforcement Learning, Action Model Learning, and Numeric Planning for Tackling Complex Tasks
Yarin Benyamin, Argaman Mordoch, Shahaf S. Shperberg +1
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. Algorith…
Algorithm Selection for Optimal Multi-Agent Path Finding via Graph Embedding
Carmel Shabalin, Omri Kaduri, Roni Stern
Multi-agent path finding (MAPF) is the problem of finding paths for multiple agents such that they do not collide. This problem manifests in numerous real-world applications such a…
Optimal and Bounded Suboptimal Any-Angle Multi-agent Pathfinding
Konstantin Yakovlev, Anton Andreychuk, Roni Stern
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 possib…
Safe Learning of PDDL Domains with Conditional Effects -- Extended Version
Argaman Mordoch, Enrico Scala, Roni Stern +1
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 s…
Novelty Accommodating Multi-Agent Planning in High Fidelity Simulated Open World
James Chao, Wiktor Piotrowski, Roni Stern +4
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 ob…
A Domain-Independent Agent Architecture for Adaptive Operation in Evolving Open Worlds
Shiwali Mohan, Wiktor Piotrowski, Roni Stern +4
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 f…
Heuristic Search For Physics-Based Problems: Angry Birds in PDDL+
Wiktor Piotrowski, Yoni Sher, Sachin Grover +2
This paper studies how a domain-independent planner and combinatorial search can be employed to play Angry Birds, a well established AI challenge problem. To model the game, we use…
Learning to Operate in Open Worlds by Adapting Planning Models
Wiktor Piotrowski, Roni Stern, Yoni Sher +4
Planning agents are ill-equipped to act in novel situations in which their domain model no longer accurately represents the world. We introduce an approach for such agents operatin…
An Example of the SAM+ Algorithm for Learning Action Models for Stochastic Worlds
Brendan Juba, Roni Stern
In this technical report, we provide a complete example of running the SAM+ algorithm, an algorithm for learning stochastic planning action models, on a simplified PPDDL version of…
Playing Angry Birds with a Domain-Independent PDDL+ Planner
Wiktor Piotrowski, Roni Stern, Matthew Klenk +4
This demo paper presents the first system for playing the popular Angry Birds game using a domain-independent planner. Our system models Angry Birds levels using PDDL+, a planning…
Safe Learning of Lifted Action Models
Brendan Juba, Hai S. Le, Roni Stern
Creating a domain model, even for classical, domain-independent planning, is a notoriously hard knowledge-engineering task. A natural approach to solve this problem is to learn a d…
Improving Continuous-time Conflict Based Search
Anton Andreychuk, Konstantin Yakovlev, Eli Boyarski +1
Conflict-Based Search (CBS) is a powerful algorithmic framework for optimally solving classical multi-agent path finding (MAPF) problems, where time is discretized into the time st…
Revisiting Bounded-Suboptimal Safe Interval Path Planning
Konstantin Yakovlev, Anton Andreychuk, Roni Stern
Safe-interval path planning (SIPP) is a powerful algorithm for finding a path in the presence of dynamic obstacles. SIPP returns provably optimal solutions. However, in many practi…
Bidding in Spades
Gal Cohensius, Reshef Meir, Nadav Oved +1
We present a Spades bidding algorithm that is superior to recreational human players and to publicly available bots. Like in Bridge, the game of Spades is composed of two independe…
Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks
Roni Stern, Nathan Sturtevant, Ariel Felner +9
The MAPF problem is the fundamental problem of planning paths for multiple agents, where the key constraint is that the agents will be able to follow these paths concurrently witho…
Multi-Agent Pathfinding with Continuous Time
Anton Andreychuk, Konstantin Yakovlev, Dor Atzmon +1
Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents such that every agent reaches its goal and the agents do not collide. Most prior work on MAPF was…
Modifying Optimal SAT-based Approach to Multi-agent Path-finding Problem to Suboptimal Variants
Pavel Surynek, Ariel Felner, Roni Stern +1
In multi-agent path finding (MAPF) the task is to find non-conflicting paths for multiple agents. In this paper we focus on finding suboptimal solutions for MAPF for the sum-of-cos…
Efficient, Safe, and Probably Approximately Complete Learning of Action Models
Roni Stern, Brendan Juba
In this paper we explore the theoretical boundaries of planning in a setting where no model of the agent's actions is given. Instead of an action model, a set of successfully execu…
Sequential Plan Recognition
Reuth Mirsky, Roni Stern, Ya'akov +2
Plan recognition algorithms infer agents' plans from their observed actions. Due to imperfect knowledge about the agent's behavior and the environment, it is often the case that th…
PHA*: Finding the Shortest Path with A* in An Unknown Physical Environment
A. Ben-Yair, A. Felner, S. Kraus +2
We address the problem of finding the shortest path between two points in an unknown real physical environment, where a traveling agent must move around in the environment to explo…