Agent-based approach to complex systems modeling
In this paper, we propose a method for detection of local system structures in a complex database. The complex database is viewed as consisting of mixed numeric and nominal attributes, and the local system structure as expressed by ifthen rules. The detection of local system structures is an important task, and is concerned with inter-dependent issues. The issues involved in the detection of ifthen rules include finding the objects that share common interests and then finding ifthen rules that characterize those objects. To deal with these issues, an agent-based approach is proposed. Each agent has the role of collecting data points (objects) based on their similarity, for mixed data and detecting a rule. The similarity is introduced so that the agent can handle a mixed database. Each agent will occupy a part of the database as its territory according to the predefined algorithm with which agents try to expand or reduce their territories.
mixed data agent-based systems rule extraction