78 0 0 0 OA 自律型アンドロイドによる対話における「同調的笑い」の生成
人との自然なインタラクションの実現を指向する自律型アンドロイドにおいて,人間どうしの対話でみられる自然なタイミングでの笑いの実現は重要な要素である.また,笑いの中でも,一方が笑うともう一方がつられて笑うshared laughterは対話の場を和ませたり,盛り上げたりするなどの効果があると考えらえる.本稿では,人間とアンドロイドERICAとの1対1対話において,アンドロイドはいかなる場合に人間につられて笑うべきかという観点で,言語および韻律特徴量を分析する.また分析結果に基づき人間が笑った場合にアンドロイドがつられて笑うべきか否かの予測を試みる.
6 0 0 0 OA 論証対話システムにおける情報探索対話戦略の最適化
- 著者
- 勝見 久央 吉野 幸一郎 平岡 拓也 秋元 康佑 山本 風人 本浦 庄太 定政 邦彦 中村 哲
- 出版者
- 一般社団法人 人工知能学会
- 雑誌
- 人工知能学会論文誌 (ISSN:13460714)
- 巻号頁・発行日
- vol.35, no.1, pp.DSI-D_1-12, 2020-01-01 (Released:2020-01-01)
- 参考文献数
- 28
- 被引用文献数
- 1
Argumentation-based dialogue systems, which can handle and exchange arguments through dialogue, have been widely researched. It is required that these systems have sufficient supporting information to argue their claims rationally; however, the systems do not often have enough information in realistic situations. One way to fill in the gap is acquiring such missing information from dialogue partners (information-seeking dialogue). Existing informationseeking dialogue systems were based on handcrafted dialogue strategies that exhaustively examine missing information. However, these strategies were not specialized in collecting information for constructing rational arguments. Moreover, the number of system’s inquiry candidates grows in accordance with the size of the argument set that the system deal with. In this paper, we formalize the process of information-seeking dialogue as Markov decision processes (MDPs) and apply deep reinforcement learning (DRL) for automatic optimization of a dialogue strategy. By utilizing DRL, our dialogue strategy can successfully minimize objective functions: the number of turns it takes for our system to collect necessary information in a dialogue. We also proposed another dialogue strategy optimization based on the knowledge existence. We modeled the knowledge of the dialogue partner by using Bernoulli mixture distribution. We conducted dialogue experiments using two datasets from different domains of argumentative dialogue. Experimental results show that the proposed dialogue strategy optimization outperformed existing heuristic dialogue strategies.