Learning goal hierarchies from structured observations and expert annotations |
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Authors: | Tolga Könik John E Laird |
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Affiliation: | (1) Computational Learning Laboratory, Center for the Study of Language and Information, Stanford University, Stanford, CA 94305, USA;(2) Artificial Intelligence Laboratory, Electrical Engineering and Computer Science Department, University of Michigan, Ann Arbor, MI 48109, USA |
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Abstract: | We describe a relational learning by observation framework that automatically creates cognitive agent programs that model expert task performance in complex dynamic domains.
Our framework uses observed behavior and goal annotations of an expert as the primary input, interprets them in the context
of background knowledge, and returns an agent program that behaves similar to the expert. We map the problem of creating an
agent program on to multiple learning problems that can be represented in a “supervised concept learning’’ setting. The acquired
procedural knowledge is partitioned into a hierarchy of goals and represented with first order rules. Using an inductive logic
programming (ILP) learning component allows our framework to naturally combine structured behavior observations, parametric
and hierarchical goal annotations, and complex background knowledge. To deal with the large domains we consider, we have developed
an efficient mechanism for storing and retrieving structured behavior data. We have tested our approach using artificially
created examples and behavior observation traces generated by AI agents. We evaluate the learned rules by comparing them to
hand-coded rules.
Editor: Rui Camacho |
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Keywords: | Relational learning by observation Relational learning Inductive logic programming (ILP) Behavioral cloning Cognitive agent architectures |
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