Authors:
Klaus Häming
and
Gabriele Peters
Affiliation:
University of Hagen, Germany
Keyword(s):
Ranking functions, Machine learning, Reinforcement learning, Belief revision, Hybrid learning system.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Learning Paradigms and Algorithms
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
To enable a reinforcement learning agent to acquire symbolical knowledge, we augment it with a high-level knowledge representation. This representation consists of ordinal conditional functions (OCF) which allow it to rank world models. By this means the agent is enabled to complement the self-organizing capabilities of the low-level reinforcement learning sub-system by reasoning capabilities of a high-level learning component. We briefly summarize the state-of-the-art method how new information is included into the OCF. To improve the emergence of plausible behavior, we then introduce a modification of this method. The viability of this modification is examined first, for the inclusion of conditional information with negated consequents and second, for the generalization of belief in the context of unobserved variables. Besides providing a theoretical justification for this modification, we also show the advantages of our approach in comparison to the state-of-the-art method of revi
sion in a reinforcement learning application.
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