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| Paper IPM / Cognitive / 11397 |
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Due to the complicated nature of concept learning problem and limitations in the real environments, it is inevitable for the learning agent to model and deal with uncertainty. In this paper, we take evidence theory into consideration for modeling uncertainty in concept learning. Belief function framework, as a mapping from agent?s continuous sensory space to the discrete actions space, constructs an evidential model for reasoning under uncertainty during learning process. The agent learns this evidential mapping through interaction with the environment or mentor, using reinforcement signal. We show the way of learning these belief functions using reinforcement learning and evaluate the method by a synthetic simulator.
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