AAAI Publications, 2013 AAAI Fall Symposium Series

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Reasoning with Uncertainties Over Existence of Objects
Vien Anh Ngo, Marc Toussaint

Last modified: 2013-11-12


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.


Planning under uncertainty, relational planning, Bayesian inference

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