A State-Based Regression Formulation for Domains with Sensing
Actions<br> and Incomplete InformationArticle
Authors: Le-Chi Tuan ; Chitta Baral ; Tran Cao Son
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Le-Chi Tuan;Chitta Baral;Tran Cao Son
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 a three-valued characterization of domains
with sensing actions to define the regression function. We prove the soundness
and completeness of our regression formulation with respect to the definition
of progression. More specifically, we show that (i) a plan obtained through
regression for a planning problem is indeed a progression solution of that
planning problem, and that (ii) for each plan found through progression, using
regression one obtains that plan or an equivalent one.
MII: Frameworks for the Development of Efficient and Scalable Knowledge-based Systems; Funder: National Science Foundation; Code: 0220590
CRI: Computing Support for the Next Generation Application-driven Declarative Programming Systems; Funder: National Science Foundation; Code: 0454066
CREST: Center for Research Excellence in Bioinformatics and Computational Biology; Funder: National Science Foundation; Code: 0420407
Knowledge Representation, Reasoning, and Problem Solving in a Cellular Domain; Funder: National Science Foundation; Code: 0412000
Reasoning and Plannning with Sensing Actions and Their Applications; Funder: National Science Foundation; Code: 0070463
Bibliographic References
1 Document citing this article
Richard Scherl;Cao Son Tran;Chitta Baral, Lecture notes in computer science, State-Based Regression with Sensing and Knowledge, pp. 345-357, 2008, 10.1007/978-3-540-89197-0_33.