Forest Agostinelli

A Conflict-Driven Approach for Reaching Goals Specified with Negation as Failure

Introduction

A conflict-driven approach for reaching goals specified with negation as failure. Discover CDGR, a conflict-driven algorithm addressing non-monotonic logic for pathfinding goals specified with negation as failure. Achieve shorter paths and faster, more efficient search.

Abstract

In the context of pathfinding, first-order logic allows for the expressive specification of goals. Using negation as failure, one can specify what must not be true in a goal state instead of what must be true, which can result in succinct goal specifications while also being computationally advantageous. However, due to non-monotonicity, integration of negation as failure can be cumbersome. To address this problem, we introduce conflict-driven goal reaching (CDGR), a conflict-driven algorithm for reaching goals specified with non-monotonic logic that refines a search for a goal state based on conflicts encountered during search. Our results show that CDGR results in significantly shorter paths and can significantly speed up search when compared to not taking conflicts into consideration. Furthermore, our results show that finding paths to goals can be much more efficient when goals are specified with negation as failure instead of without negation as failure.

Review

This paper introduces a novel approach, Conflict-Driven Goal Reaching (CDGR), designed to address the challenges of integrating negation as failure (NAF) into first-order logic goal specifications within pathfinding contexts. While NAF offers advantages in terms of succinctness and potential computational efficiency, its non-monotonic nature traditionally complicates its application. CDGR proposes a conflict-driven algorithm that refines the search for a goal state by learning from conflicts encountered during exploration, thus providing a structured method to leverage the benefits of non-monotonic logic in complex planning scenarios. The core strength of the CDGR algorithm lies in its ability to dynamically adapt the search based on encountered conflicts, leading to more intelligent and directed exploration. The reported results are highly promising, indicating that CDGR significantly reduces path lengths and substantially accelerates the search process compared to methods that do not incorporate conflict considerations. Furthermore, a key finding emphasizes the efficiency gains when goals are expressed using negation as failure rather than traditional positive specifications, directly validating the utility and computational advantages of NAF in pathfinding problems. This work makes a significant contribution to the field of AI planning and goal-reaching, particularly by providing a practical and efficient mechanism for handling non-monotonic logic. The demonstrated improvements suggest that CDGR could be highly impactful for complex planning domains requiring expressive goal specifications. Future research could investigate the algorithm's performance and scalability in diverse and highly constrained environments, explore its applicability to other forms of non-monotonic reasoning, or delve deeper into the theoretical properties and completeness guarantees of the conflict identification and resolution strategies employed.

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