Explaining decisions of a deep reinforcement learner with a cognitive architecture
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Authors
Somers, Sterling
Mitsopoulos, Konstantinos
Lebiere, Christian
Thomson, Robert
Issue Date
2018
Type
Conference presentations, papers, posters
Language
Keywords
deep reinforcement learning , cognitive modeling , introspection
Alternative Title
Abstract
The work presented is an evaluation of a method for developing a hybrid system, consisting of a Deep Reinforcement Learning (RL) agent and a cognitive model, capable of providing explanations of its action decisions. The methodology uses a symbolic/sub-symbolic cognitive architecture to introspect on the activity of the network to understand its representation. The entropy in the system’s behavioral predictions could be used as a signal to affirm or deny ascribing a representation to the network.
Description
Citation
Somers, Sterling, Constantinos Mitsupoulos, Christian Lebiere, and Robert Thomson. "Explaining Decisions of a Deep Reinforcement Learner with a Cognitive Architecture." In Proceedings of the International Conference on Cognitive Modeling, 2018, 144-149.
Publisher
International Conference on Cognitive Modeling