Regression with respect to sensing actions and partial states

Authors:
Le-Chi Tuan;Chitta Baral;Xin Zhang;Tran Cao Son
Affiliations:
Computer Science and Engineering, Arizona State University, Tempe, AZ;Computer Science and Engineering, Arizona State University, Tempe, AZ;Computer Science and Engineering, Arizona State University, Tempe, AZ;Computer Science Department, New Mexico State University, Las Cruces, NM
Venue:
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Year:
2004

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Abstract

In this paper, we present a state-based regression function for planning domains where an agent does not have complete information and may have sensing actions. We consider binary domains, and employ the 0-approximation (Son & Baral 2001) to define the regression function. In binary domains, the use of 0-approximation means using 3-valued states. Although planning using this approach is incomplete with respect to the full semantics, we adopt it to have a lower complexity. We prove the soundness and completeness of our regression formulation with respect to the definition of progression and develop a conditional planner that utilizes our regression function.