Escaping heuristic depressions in real-time heuristic search

Carlos Hernández, Jórge A. Baier

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferencia

2 Citas (Scopus)

Resumen

Heuristic depressions are local minima of heuristic functions. While visiting one them, real-time (RT) search algorithms like LRTA* will update the heuristic value for most of their states several times before escaping, resulting in costly solutions. Existing RT search algorithm tackle this problem by doing more search and/or lookahead but do not guide search towards leaving depressions. We present eLSS-LRTA*, a new RT search algorithm based on LSS-LRTA* that actively guides search towards exiting regions with heuristic depressions. We show that our algorithm produces better-quality solutions than LSS-LRTA* for equal values of lookahead in standard RT benchmarks.

Idioma originalInglés
Título de la publicación alojada10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011
EditorialInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Páginas1197-1198
Número de páginas2
Volumen2
EstadoPublicada - 2011
Evento10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011 - Taipei, Taiwán
Duración: 2 may 20116 may 2011

Otros

Otros10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011
PaísTaiwán
CiudadTaipei
Período2/05/116/05/11

Áreas temáticas de ASJC Scopus

  • Inteligencia artificial

Citar esto

Hernández, C., & Baier, J. A. (2011). Escaping heuristic depressions in real-time heuristic search. En 10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011 (Vol. 2, pp. 1197-1198). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).