1 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.AI2024★ 1 cited
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…
cs.AI2023★ 1 cited
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…
cs.AI2023★ 1 cited
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…