Pessimistic Heuristics Beat Optimistic Ones in Real-Time Search

  • Authors:

  • Aleksander Sadikov;Ivan Bratko

  • Affiliations:

  • University of Ljubljana, Faculty of Computer and Information Science, Artificial Intelligence Laboratory, Trž/aš/ka 25, 1000 Ljubljana, Slovenia, e-mail: {aleksander.sadikov/ivan.bratko}@f ...;University of Ljubljana, Faculty of Computer and Information Science, Artificial Intelligence Laboratory, Trž/aš/ka 25, 1000 Ljubljana, Slovenia, e-mail: {aleksander.sadikov/ivan.bratko}@f ...

  • Venue:
  • Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
  • Year:
  • 2006

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Abstract

Admissibility is a desired property of heuristic evaluation functions, because when these heuristics are used with complete search methods, such as A* and RBFS, they guarantee that an optimal solution will be found. Since every optimistic heuristic function is admissible, optimistic functions are widely used. We show, however, that with incomplete, real-time search, optimistic functions lose their appeal, and in fact they may hinder the search under quite reasonable conditions. Under these conditions the exact opposite is to be preferred, i.e. pessimistic heuristic functions that never underestimate the difficulty of the problem. We demonstrate that such heuristics behave better than optimistic ones of equal quality on a standard testbed using RTA* search method.