Towards solving large-scale POMDP problems via spatio-Temporal belief state clustering

Research output: Contribution to conferencePaperpeer-review

5 Citations (Scopus)

Abstract

Markov decision process (MDP) is commonly used to model a stochastic environment for supporting optimal decision making. However, solving a large-scale MDP problem under the partially observable condition (also called POMDP) is known to be computationally intractable. Belief compression by reducing belief state dimension has recently been shown to be an effective way for making the problem tractable. With the conjecture that temporally close belief states should possess a low intrinsic degree of freedom due to problem regularity, this paper proposes to cluster the belief states based on a criterion function measuring the belief states spatial and temporal differences. Further reduction of the belief state dimension can then result in a more efficient POMDP solver. The proposed method has been tested using a synthesized navigation problem (Hallway2) and empirically shown to be a promising direction towards solving large-scale POMDP problems. Some future research directions are also included.

Original languageEnglish
Pages17-24
Number of pages8
Publication statusPublished - 2005
Event5th Workshop on Reasoning with Uncertainty in Robotics, RUR 2005, Held at the International Joint Conference on Artificial Intelligence, IJCAI 2005 - Edinburgh, Scotland, Edinburgh, United Kingdom
Duration: 30 Jul 200530 Jul 2005

Conference

Conference5th Workshop on Reasoning with Uncertainty in Robotics, RUR 2005, Held at the International Joint Conference on Artificial Intelligence, IJCAI 2005
Country/TerritoryUnited Kingdom
CityEdinburgh
Period30/07/0530/07/05

Scopus Subject Areas

  • Artificial Intelligence
  • Human-Computer Interaction

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