In recent years, the concept of "autonomous mental development" (AMD) has been applied to the construction of artificial systems such as conversational agents, in order to resolve some of the difficulties involved in the manual definition of their knowledge bases and behavioral patterns. AMD 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 (GP). Knowledge acquisition implemented by finite-state machines and templates, and language learning by GP are used for language development. Several illustrations and usability tests show the usefulness of the proposed developmental conversational agent.
Bibliographical noteFunding Information:
Manuscript received March 28, 2005; revised October 3, 2005. This work was supported in part by the Brain Science and Engineering Research Program sponsored by the Korean Ministry of Commerce, Industry, and Energy. The authors are with the Department of Computer Science, Yonsei University, Seoul 120-749, Korea (e-mail: firstname.lastname@example.org; email@example.com; firstname.lastname@example.org). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TEVC.2006.890265
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computational Theory and Mathematics