Heuristic search is a core area of Artificial Intelligence, successfully applied to planning, constraint satisfaction and game playing. In real-time heuristic search autonomous agents interleave planning and plan execution and access environment locally which make them more suitable for Artificial Life style settings. Over the last two decades a large number of real-time heuristic search algorithms have been manually crafted and evaluated. In this paper we break down several published algorithms into building blocks and then let a simulated evolution re-combine the blocks in a performance-based way. Remarkably, even relatively short evolution runs result in algorithms with state-of-the-art performance. These promising preliminary results open exciting possibilities in the field of real-time heuristic search.

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© 2016 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license
2016
MIT Press
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.