Projective Simulation for Classical Learning Agents: A Comprehensive Investigation

We study the model of projective simulation (PS), a novel approach to artificial intelligence based on stochastic processing of episodic memory which was recently introduced. 2 ) Here we provide a detailed analysis of the model and examine its performance, including its achievable efficiency, its le...

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Bibliographic Details
Published in:New generation computing 2015, Vol.33 (1), p.69-114
Main Authors: Mautner, Julian, Makmal, Adi, Manzano, Daniel, Tiersch, Markus, Briegel, Hans J.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Hardware
Computer Science
Computer Systems Organization and Communication Networks
Software Engineering/Programming and Operating Systems
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Summary:We study the model of projective simulation (PS), a novel approach to artificial intelligence based on stochastic processing of episodic memory which was recently introduced. 2 ) Here we provide a detailed analysis of the model and examine its performance, including its achievable efficiency, its learning times and the way both properties scale with the problems’ dimension. In addition, we situate the PS agent in different learning scenarios, and study its learning abilities. A variety of new scenarios are being considered, thereby demonstrating the model’s flexibility. Furthermore, to put the PS scheme in context, we compare its performance with those of Q-learning and learning classifier systems, two popular models in the field of reinforcement learning. It is shown that PS is a competitive artificial intelligence model of unique properties and strengths.
ISSN:0288-3635
1882-7055
DOI:10.1007/s00354-015-0102-0