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First-Order Logical Filtering Afsaneh Shirazi , Eyal Amir
 

Summary: First-Order Logical Filtering
Afsaneh Shirazi , Eyal Amir
University of Illinois at Urbana-Champaign, Department of Computer Science,
Urbana, IL 61801, USA
Abstract
Logical filtering is the process of updating a belief state (set of possible world states)
after a sequence of executed actions and perceived observations. In general, it is
intractable in dynamic domains that include many objects and relationships. Still,
potential applications for such domains (e.g., semantic web, autonomous agents,
and partial-knowledge games) encourage research beyond immediate intractability
results.
In this paper we present polynomial-time algorithms for filtering belief states that
are encoded as First-Order Logic (FOL) formulas. We sidestep previous discouraging
results, and show that our algorithms are exact in many cases of interest. These
algorithms accept belief states in full FOL, which allows natural representation
with explicit references to unidentified objects, and partially known relationships.
Our algorithms keep the encoding compact for important classes of actions, such
as STRIPS actions whose success or failure is known. These results apply to most
expressive modeling languages, such as partial databases and belief revision in FOL.
Key words: Filtering, First-Order Logic, Belief Update, Situation Calculus

  

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

 

Collections: Computer Technologies and Information Sciences