Fast Algorithm for Catching a Prey Quickly in Known and Partially Known Game Maps

Jorge A. Baier, Adi Botea, Daniel Harabor, Carlos Hernández

Resultado de la investigación: Contribución a una revistaArtículo

4 Citas (Scopus)

Resumen

In moving target search, the objective is to guide a hunter agent to catch a moving prey. Even though in game applications maps are always available at developing time, current approaches to moving target search do not exploit preprocessing to improve search performance. In this paper, we propose MtsCopa, an algorithm that exploits precomputed information in the form of compressed path databases (CPDs), and that is able to guide a hunter agent in both known and partially known terrain. CPDs have previously been used in standard, fixed-target pathfinding but had not been used in the context of moving target search. We evaluated MtsCopa over standard game maps. Our speed results are orders of magnitude better than current state of the art. The time per individual move is improved, which is important in real-time search scenarios, where the time available to make a move is limited. Compared to state of the art, the number of hunter moves is often better and otherwise comparable, since CPDs provide optimal moves along shortest paths. Compared to previous successful methods, such as I-ARA∗, our method is simple to understand and implement. In addition, we prove MtsCopa always guides the agent to catch the prey when possible.

Idioma originalInglés
Número de artículo6851877
Páginas (desde-hasta)193-199
Número de páginas7
PublicaciónIEEE Transactions on Computational Intelligence and AI in Games
Volumen7
N.º2
DOI
EstadoPublicada - 1 jun 2015

Áreas temáticas de ASJC Scopus

  • Software
  • Ingeniería de control y sistemas
  • Inteligencia artificial
  • Ingeniería eléctrica y electrónica

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