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DAGSTUHL
2007

Learning Probabilistic Relational Dynamics for Multiple Tasks

8 years 11 months ago
Learning Probabilistic Relational Dynamics for Multiple Tasks
The ways in which an agent’s actions affect the world can often be modeled compactly using a set of relational probabilistic planning rules. This paper addresses the problem of learning such rule sets for multiple related tasks. We take a hierarchical Bayesian approach, in which the system learns a prior distribution over rule sets. We present a class of prior distributions parameterized by a rule set prototype that is stochastically modified to produce a task-specific rule set. We also describe a coordinate ascent algorithm that iteratively optimizes the task-specific rule sets and the prior distribution. Experiments using this algorithm show that transferring information from related tasks significantly reduces the amount of training data required to predict action effects in blocks-world domains.
Ashwin Deshpande, Brian Milch, Luke S. Zettlemoyer
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where DAGSTUHL
Authors Ashwin Deshpande, Brian Milch, Luke S. Zettlemoyer, Leslie Pack Kaelbling
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