TY - GEN
T1 - Changing topics of dialogue for natural mixed-initiative interaction of conversational agent based on human cognition and memory
AU - Lim, Sungsoo
AU - Oh, Keunhyun
AU - Cho, Sung Bae
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - Mixed-initiative interaction (MII) plays an important role in conversation agent. In the former MII research, MII process only static conversation and cannot change the conversation topic dynamically by the system because the agent depends only on the working memory and predefined methodology. In this paper, we propose the mixed-initiative interaction based on human cognitive architecture and memory structure. Based on the global workspace theory, one of the cognitive architecture models, proposed method can change the topic of conversation dynamically according to the long term memory which contains past conversation. We represent the long term memory using semantic network which is a popular representation for storing knowledge in the field of cognitive science, and retrieve the semantic network according to the spreading activation theory which has been proven to be efficient for inferring in semantic networks. Through some dialogue examples, we show the usability of the proposed method.
AB - Mixed-initiative interaction (MII) plays an important role in conversation agent. In the former MII research, MII process only static conversation and cannot change the conversation topic dynamically by the system because the agent depends only on the working memory and predefined methodology. In this paper, we propose the mixed-initiative interaction based on human cognitive architecture and memory structure. Based on the global workspace theory, one of the cognitive architecture models, proposed method can change the topic of conversation dynamically according to the long term memory which contains past conversation. We represent the long term memory using semantic network which is a popular representation for storing knowledge in the field of cognitive science, and retrieve the semantic network according to the spreading activation theory which has been proven to be efficient for inferring in semantic networks. Through some dialogue examples, we show the usability of the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=77956293397&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:77956293397
SN - 9789896740221
SN - 9789896740214
T3 - ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings
SP - 107
EP - 112
BT - ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings
T2 - 2nd International Conference on Agents and Artificial Intelligence, ICAART 2010
Y2 - 22 January 2010 through 24 January 2010
ER -