Abstract
Since the manual construction of our knowledge-base has several crucial limitations when applied to intelligent systems, mental development has been investigated in recent years. Autonomous mental development is a new paradigm for developing autonomous machines, which are adaptive and flexible to the environment. Language development, a kind of mental development, is an important aspect of intelligent conversational agents. In this paper, we propose an intelligent conversational agent and its language development mechanism by putting together five promising techniques; Bayesian networks, pattern matching, finite state machines, templates, and genetic programming. Knowledge acquisition implemented by finite state machines and templates, and language learning by genetic programming are developed for language development. Several illustrations and usability tests show the usefulness of the proposed developmental conversational agent.
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© 2006 Springer-Verlag Berlin Heidelberg
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Hong, JH., Lim, S., Cho, SB. (2006). Language Learning for the Autonomous Mental Development of Conversational Agents. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_98
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DOI: https://doi.org/10.1007/11893295_98
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
Online ISBN: 978-3-540-46485-3
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