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Journal of Artificial Intelligence Research 33 (2008) 349-402 Submitted 05/08; published 11/08 Learning Partially Observable Deterministic Action Models
 

Summary: Journal of Artificial Intelligence Research 33 (2008) 349-402 Submitted 05/08; published 11/08
Learning Partially Observable Deterministic Action Models
Eyal Amir EYAL@ILLINOIS.EDU
Computer Science Department
University of Illinois, Urbana-Champaign
Urbana, IL 61801, USA
Allen Chang ALLENC256@YAHOO.COM
2020 Latham st., apartment 25
Mountainview, CA 94040, USA
Abstract
We present exact algorithms for identifying deterministic-actions' effects and preconditions in
dynamic partially observable domains. They apply when one does not know the action model (the
way actions affect the world) of a domain and must learn it from partial observations over time.
Such scenarios are common in real world applications. They are challenging for AI tasks because
traditional domain structures that underly tractability (e.g., conditional independence) fail there
(e.g., world features become correlated). Our work departs from traditional assumptions about
partial observations and action models. In particular, it focuses on problems in which actions are
deterministic of simple logical structure and observation models have all features observed with
some frequency. We yield tractable algorithms for the modified problem for such domains.
Our algorithms take sequences of partial observations over time as input, and output determin-

  

Source: Amir, Eyal - Department of Computer Science, University of Illinois at Urbana-Champaign

 

Collections: Computer Technologies and Information Sciences