Reasoning with Uncertainties Over Existence Of Objects

Vien Ngo, Marc Toussaint

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

In this paper we consider planning problems in relationalMarkov processes where objects may “appear” or “disap-pear”, perhaps depending on previous actions or propertiesof other objects. For instance, problems which require to ex-plicitly generate or discover objects fall into this category. Inour formulation this requires to explicitly represent the un-certainty over the number of objects (dimensions or factors)in a dynamic Bayesian networks (DBN). Many formalisms(also existing ones) are conceivable to formulate such prob-lems. We aim at a formulation that facilitates inference andplanning. Based on a specific formulation we investigate twoinference methods—rejection sampling and reversible-jumpMCMC—to compute a posterior over the process conditionedon the first and last time slice (start and goal state). We willdiscuss properties, efficiency, and appropriateness of eachone.
Original languageEnglish
Publication statusPublished - 2013
Event2013 AAAI Fall Symposium: How Should Intelligence Be Abstracted in AI Research -
Duration: 13 Nov 201315 Nov 2013

Conference

Conference2013 AAAI Fall Symposium: How Should Intelligence Be Abstracted in AI Research
Period13/11/201315/11/2013

Bibliographical note

AAAI Technical Report FS-13-02 (2013 AAAI Fall Symposium: How Should Intelligence Be Abstracted in AI Research)

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