Improving prediction accuracy in agent based modeling systems under dynamic environment

Abstract

Considering the dynamic and complex nature of real systems, it is not easy to build an accurate artificial simulation. Agent Based Modeling Simulations used to build such simulated models are often oversimplified and not realistic enough to predict reliable results. In addition to this, the validation of such Agent Based Model (ABM) involves great difficulties thus putting a question mark on their effective usage and acceptability. One of the major problems affecting the reliability of ABM addressed in this work is the dynamic nature of the environment. An ABM initially validated at a given time stamp is bound to become invalid with the inevitable change in the environment over time. Thus, an ABM that does not learn regularly from its environment cannot sustain its validity over a longer period of time. It should therefore have the ability to absorb changes in the environment upon their detection. Thus, in this paper we present a novel approach for incorporating adaptability and learning in an ABM simulation, thereby making it capable to be consistently synchronized with the changing environment and provide reliable results. One phase of our method explores the use of Data Mining (DM) in ABM for detecting environment trends and dynamics. Another phase addresses different methods for finding similarity between the knowledge represented by two different decision trees, for detecting a change in the simulation's environment.