Decision dynamics in cooperative search based on evolutionary game theory

This paper investigates a search problem for a robotic group to find a target which appears randomly and stays for a fixed time interval. We assume that there are two search areas and the target appears in either of them. Under the situation, it is required to make a decision on which room to search while determining a control input. For the problem, we present an evolutionary game theoretic method to decide the action of each robot and show that the method eventually achieves two types of orders: macro and micro orders. The macro order means that the population share of robots converges to an ordered value, and the micro one means that the robots’ motions converge to periodic trajectories. The macro order is achieved by a probabilistic decision-making model called Win-Stay-Lose-Shift. Then, convergence of the expectation value of population share is proved for two typical payoff structures by employing knowledge of evolutionary game theory. Once the area to search is decided by the decision-making model, each robot determines a control input aiming at reduction of control energy. Finally, simulation results show the validity of the proposed method.

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Published 5 November 2010