Devin Wild Thomas, Wheeler Ruml

Real-time Cost-algebraic Heuristic Search

Introduction

Real-time cost-algebraic heuristic search. Explore real-time cost-algebraic heuristic search for planning under time pressure. This paper provides a general completeness proof, detailing h-value changes and easing future applications.

Abstract

Planning under time pressure arises in many situations. Real-time heuristic search, in which an agent must compute its next action within a prespecified time bound, has proven to be a useful model of real-time planning. However, it is laborious to prove the completeness of new real-time search algorithms. In this paper, we provide a general proof of the completeness of a standard real-time heuristic search algorithm in any problem domain that obeys the axioms of a cost algebra. The proof includes additional detail on how h values change as the algorithm learns. This foundation clarifies the dependence of the proof on domain and algorithm properties and will ease future applications of real-time planning.

Review

The paper "Real-time Cost-algebraic Heuristic Search" addresses a critical challenge in the field of real-time planning: the difficulty in proving the completeness of new real-time search algorithms. Real-time heuristic search is acknowledged as a vital model for planning under time constraints, making the robustness and theoretical guarantees of these algorithms paramount. The authors identify the laborious nature of establishing completeness as a bottleneck for progress in this area. To overcome this, the paper presents a significant theoretical contribution: a general proof of completeness for a standard real-time heuristic search algorithm. This proof is applicable across any problem domain that adheres to the axioms of a cost algebra, offering a powerful abstraction. A key detail highlighted is the inclusion of specifics on how heuristic (h) values evolve as the algorithm learns, which adds depth to the understanding of the algorithm's behavior. This generalized foundation is posited to clarify the intricate interplay between domain properties and algorithm characteristics. Ultimately, this work promises to substantially ease future development and application of real-time planning solutions by providing a more accessible and unified framework for proving algorithmic completeness. By abstracting the core principles necessary for such proofs through the lens of cost algebra, the paper lays a valuable theoretical groundwork. This foundational contribution has the potential to accelerate the design, analysis, and deployment of reliable real-time planning systems, thereby benefiting researchers and practitioners in artificial intelligence and robotics.

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