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CHANGING TOPICS OF DIALOGUE FOR NATURAL
MIXED-INITIATIVE INTERACTION OF CONVERSATIONAL
AGENT BASED ON HUMAN COGNITION AND MEMORY
Sungsoo Lim, Keunhyun Oh and Sung-Bae Cho
Department of Computer Science,Yonsei University, Seoul, Korea
Keywords: Mixed-initiative interaction, Global workspace, Semantic network, Spreading activation theory.
Abstract: 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.
1 INTRODUCTION
Conversational agent can be classified into user-
initiative, system-initiative, and mixed-initiative
agent with the subject who plays a leading role when
solving problems. In the user-initiative
conversational agent, the user takes a leading role
when continuing the conversation, requesting
necessary information and services to the agent with
the web searching engine and question-answer
systems. On the other hand, in the system-initiative
conversational agent, the agent calls on users to
provide information by answering the predefined
questions. Although the various conversational
agents have been suggested with the user-initiative
or system-initiative, these techniques still have
significant limitations for efficient problem solving.
To overcome their limitations, the mixed-initiative
interaction has been discussed extensively.
Mixed-initiative conversational agent is defined
as the process the user and system; which both have
the initiative; and solve problems effectively by
continuing identification of each other’s intention
through mutual interaction when needed (Hong et al.,
2007, Tecuci et al., 2007). Macintosh, Ellis, and
Allen (2005) showed that mixed-initiative
interaction can provide better satisfaction to the user,
comparing system based interface and mixed-
initiative interface through ATTAIN (Advanced
Traffic and Travel Information System) (Macintosh
et al., 2005).
The research to implement mixed-initiative
interaction has been studied widely. Hong used
hierarchical Bayesian network to embody mixed-
initiative interaction (Hong et al., 2007), and Bohus
and Rundnicky utilized dialogue stack (Bohus and
Rudnicky, 2009). However, these methods, which
utilize predefined methodology depending on
working memory solely, can process only static
conversation and cannot change topics naturally.
In this paper, we focus on the question – how
could the conversational agent naturally change the
topics of dialogue? We assume that the changed
topics in human-human dialogue are related with
their own experiences and the semantics which are
presented in the current dialogues.
We apply the global workspace theory (GWT)
on the process of changing topics. GWT is a simple
cognitive architecture that has been developed to
account qualitatively for a large set of matched pairs
of conscious and unconscious processes. On the
view of memory, we define the consiousness part as
a working memory where the attended topics on the
107
Lim S., Oh K. and Cho S. (2010).
CHANGING TOPICS OF DIALOGUE FOR NATURAL MIXED-INITIATIVE INTERACTION OF CONVERSATIONAL AGENT BASED ON HUMAN
COGNITION AND MEMORY.
In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Artificial Intelligence, pages 107-112
DOI: 10.5220/0002733501070112
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