When agents are initially created, they have little knowledge and experience with relatively low capability. They strive to adapt themselves to the changing environment. It is an advantage if they have the ability to learn and evolve. This paper addresses evolution of intelligent agents in transport information system. Fuzzy theory and ontological reasoning approach are proposed as evolution mechanisms, and fuzzy soft goal is introduced to facilitate the evolution process. Genetic programming operators are employed to restructure agents in the proposed multi-agent evolution cycle. We have also built an agent system to demonstrate our approach.
|Number of pages||9|
|Journal||International Journal of Fuzzy Systems|
|Publication status||Published - 2005 Jun 1|
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computational Theory and Mathematics
- Artificial Intelligence