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ILP
2005
Springer

Guiding Inference Through Relational Reinforcement Learning

9 years 11 months ago
Guiding Inference Through Relational Reinforcement Learning
Abstract. Reasoning plays a central role in intelligent systems that operate in complex situations that involve time constraints. In this paper, we present the Adaptive Logic Interpreter, a reasoning system that acquires a controlled inference strategy adapted to the scenario at hand, using a variation on relational reinforcement learning. Employing this inference mechanism in a reactive agent architecture lets the agent focus its reasoning on the most rewarding parts of its knowledge base and hence perform better under time and computational resource constraints. We present experiments that demonstrate the benefits of this approach to reasoning in reactive agents, then discuss related work and directions for future research.
Nima Asgharbeygi, Negin Nejati, Pat Langley, Sachi
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ILP
Authors Nima Asgharbeygi, Negin Nejati, Pat Langley, Sachiyo Arai
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