Learning Partially Observable Action Schemas

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Learning Partially Observable Action Schemas
We present an algorithm that derives actions' effects and preconditions in partially observable, relational domains. Our algorithm has two unique features: an expressive relational language, and an exact tractable computation. An actionschema language that we present permits learning of preconditions and effects that include implicit objects and unstated relationships between objects. For example, we can learn that replacing a blown fuse turns on all the lights whose switch is set to on. The algorithm maintains and outputs a relationallogical representation of all possible action-schema models after a sequence of executed actions and partial observations. Importantly, our algorithm takes polynomial time in the number of time steps and predicates. Time dependence on other domain parameters varies with the action-schema language. Our experiments show that the relational structure speeds up both learning and generalization, and outperforms propositional learning methods. It also all...
Dafna Shahaf, Eyal Amir
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where AAAI
Authors Dafna Shahaf, Eyal Amir
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