Technical Note: \cal Q-Learning
Machine Learning
Empirical Evaluation of a Reinforcement Learning Spoken Dialogue System
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Spoken language analysis, modeling and recognition-statistical and adaptive connectionist approaches
Integration of speech and vision using mutual information
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
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In word meaning acquisition through interactions among humans and agents, the efficiency of the learning depends largely on the dialog strategies the agents have. This paper describes automatic acquisition of dialog strategies through interaction between two agents. In the experiments, two agents infer each other's comprehension level from its facial expressions and utterances to acquire efficient strategies. Q-learning is applied to a strategy acquisition mechanism. Firstly, experiments are carried out through the interaction between a mother agent, who knows all the word meanings, and a child agent with no initial word meaning. The experimental results showed that the mother agent acquires a teaching strategy, while the child agent acquires an asking strategy. Next, the experiments of interaction between a human and an agent are investigated to evaluate the acquired strategies. The results showed the effectiveness of both strategies of teaching and asking.