擬人化エージェントにおける音声対話を通じての協調的応答戦略の自動学習

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As the opportunity of using computer systems spreads in everyday life, the need for friendly and intelligent interfaces are increasing. It is desirable that one can interact with computer systems as if one interacts with other person. Our interface agent called Visual Software Agent (VSA) has a realistic human face which moves in real-time on a CRT responding to a user, and can interact with users using natural languages. These two communication modalities help users interact naturally with this system; thus it is suitable for systems used by various people with little knowledge about computers, such as guidance systems at department stores, shopping arcades, companies, universities, etc. In this paper, we propose a dialog management system (DMS) with an automatic learning capability of cooperative answering strategy. This DMS consists of six modules, i.e., a state recognizer, an interpreter, an answer generator, a user-model manager, a learning module and a domain database. The system's answer is determined depending on semantic representation of user utterance and the current state of a user-model. Furthermore, the learning module modifies answering rules' strength using a reinforcement learning algorithm so that the system can learn cooperative answering strategy automatically from its experiences. With this learning module, VSA can modify its behavior of interactions with users; consequently, we can develop friendly guidance systems easily. Although the scale of experiments is still small, its results indicate the applicability of the learning module to real systems.
社団法人人工知能学会の論文
1997-05-01

擬人化エージェントにおける音声対話を通じての協調的応答戦略の自動学習

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