Evolving Initial Heuristic Functions for Agent-Centered Heuristic Search
Vadim Bulitko · 2020 IEEE Conference on Games (CoG) · 2020
Heuristic functions guide search algorithms and have a profound impact on their performance. In the context of agent-centered real-time heuristic search (RTHS), a heuristic represents the agent's initial domain knowledge which the agent then updates as it explores the search graph. An ideal initial heuristic should capture some specific domain knowledge to guide the agent effectively yet be general enough for a broad class of problems. It should also be computationally efficient, compact in its representation and human-interpretable. Traditionally initial heuristics in RTHS have been designed by humans (e.g., Manhattan distance). In this paper we explore the alternative of building initial heuristics by machines. To keep them portable and human-interpretable we represent each heuristic as a closed-form algebraic formula. Yet to make the heuristics capture problem specifics and thus be more effective in guiding the search, we automatically build a heuristic tailored to a class of problems. To achieve both objectives, we propose and evaluate automatically searching the space of heuristic functions. As a preliminary demonstration, we find closed-form heuristics that outperform Manhattan distance in grid-based pathfinding. We then develop an insight on how such formula-based heuristics are able to exploit characteristics of certain pathfinding maps.