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Knowledge Acquisition Modeling Through Dialogue Between Cognitive Agents

Authors: Yousfi-Monod, Mehdi; Prince, Violaine;

Knowledge Acquisition Modeling Through Dialogue Between Cognitive Agents

Abstract

This article tackles learning and communication between cognitive artificial agents. Our focus is on dialogue as the only way for agents to acquire knowledge, as it often happens in natural situations. Since this restriction has scarcely been studied in artificial intelligence (AI) until now; this research aims to provid a dialogue model devoted to knowledge acquisition. It allows two agents, in a “teacher” – “student” relationship, to exchange information with a learning incentive (on behalf of the student). The article first defines the nature of the addressed agents, the types of relation they maintain, and the structure and contents of their knowledge base. It continues by describing the different goals of learning, their realization, and the solutions provided for problems encountered by agents. A general architecture is then established and comment on the part of the theory implementation is given. The conclusion talks about the achievements carried out and the potential improvement of this work.

Keywords

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Cognitive Agents, Dialog, [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing, Knowledge Modeling

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green