Publication

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

Conference Article

Conference

Catalan Conference on Artificial Intelligence (CCIA)

Edition

16th

Pages

31-40

Doc link

http://dx.doi.org/10.3233/978-1-61499-320-9-31

File

Download the digital copy of the doc pdf document

Abstract

The 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.

Categories

mobile robots, planning (artificial intelligence).

Author keywords

robotics, human robot interaction, hide-and-Seek, POMDP

Scientific reference

A. Goldhoorn, A. Sanfeliu and R. Alquézar Mancho. Comparison of MOMDP and heuristic methods to play hide-and-seek, 16th Catalan Conference on Artificial Intelligence, 2013, Vic, Spain, in Artificial Intelligence Research and Development, Vol 256 of Frontiers in Artificial Intelligence and Applications, pp. 31-40, 2013, IOS Press.