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Comparison of MOMDP and heuristic methods to play hide-and-seek

AuthorsGoldhoorn, Alex ; Sanfeliu, Alberto ; Alquézar Mancho, Renato
KeywordsHuman robot interaction
Issue Date2013
PublisherIOS Press
CitationArtificial Intelligence Research and Applications: 31-40 (2013)
SeriesFrontiers in Artificial Intelligence and Applications 256
AbstractThe hide-and-seek game is considered an excellent domain for studying the interactions between mobile robots and humans. Prior to the implementation and test in our mobile robots TIBI and DABO, we have been devising different models and strategies to play this game and comparing them extensively in simulations. Recently, we have proposed the use of MOMDP (Mixed Observability Markov Decision Processes) models to learn a good policy to be applied by the seeker. Even though MOMDPs reduce the computational cost of POMDPs (Partially Observable MDPs), they still have a high computational complexity which is exponential with the number of states. For the hide-and-seek game, the number of states is directly related to the number of grid cells, and for two players (the hider and the seeker), it is the square of the number of cells. As an alternative to off-line MOMDP policy computation with the complete grid fine resolution, we have devised a two-level MOMDP, where the policy is computed on-line at the top level with a reduced number of states independent of the grid size. In this paper, we introduce a new fast heuristic method for the seeker and compare its performance to both off-line and on-line MOMDP approaches. We show simulation results in maps of different sizes against two types of automated hiders.
DescriptionTrabajo presentado al 16th International Conference of the Catalan Association for Artificial Intelligence en Vic del 23 al 25 de octubre de 2013.
Publisher version (URL)http://dx.doi.org/10.3233/978-1-61499-320-9-31
Identifiersisbn: 978-1-61499-319-3
Appears in Collections:(IRII) Libros y partes de libros
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