Understanding the Behavior of a Trading Agent through Fuzzy Rule-Extraction

Abstract

We propose a fuzzy rule extraction method for behavior analysis of agents in an artificial futures market. Our goal is to linguistically explain the behavior of highly profitable agents using fuzzy If-Then rules. Fuzzy rules are extracted from a trading history of such an agent. The antecedent part of each fuzzy rule is specified by previous spot prices while its consequent part is one of the three alternative actions of agents:Buy, Sell, and No Action. That is, fuzzy rules explain how each agent determines its action based on previous spot prices in the futures market. In computational experiments, we extract fuzzy rules from a trading history of a software agent with an unknown strategy. It is demonstrated that the behavior of the agent is linguistically explained through the visualization of the extracted fuzzy rules. While the proposed method is applied to the software agent in our computational experiments, it is also applicable to behavior analysis of human agents.