BibTex

Ideal Point Guided Iterative Deepening

Javier Coego, Lawrence Mandow, and J.L. Pérez de la Cruz.
Ideal point guided iterative deepening.
In Frontiers in Artificial Intelligence and Applications. ECAI 2012- 20th European Conference on Artificial Intelligence, volume 242. 2012.

Abstract

Many real world search problems involve different objectives, usually in conflict. In these cases the cost of a transition is given by a cost vector. This paper presents IPID, a new exact algorithm based on iterative deepening, that finds the set of all Pareto-optimal paths for a search problem in a graph with vectorial costs. Formal proofs of the admissibility of IPID are presented, as well as the results of some empirical comparisons between IPID and other approaches based on iterative deepening. Empirical results show that IPID is usually faster than those approaches.

BibTex

@incollection{COE:2012:C,
  affiliation={Universidad de M{\'a}laga Dpto. Lenguajes y Ciencias de la Computaci{\'o}n 29071 M{\'a}laga Spain},
  url={http: //dx.doi.org/10.3233/978-1-61499-098-7-246},
  author={Coego, Javier and Mandow, Lawrence and P{\'e}rez de la Cruz, J.L.},
  title={Ideal Point Guided Iterative Deepening},
  booktitle={Frontiers in Artificial Intelligence and Applications. ECAI 2012- 20th European Conference on Artificial Intelligence},
  volume={242},
  year={2012},
}
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