Fictitious play for cooperative action selection in robot teams

TL;DR

This paper introduces a novel distributed decision-making approach using a variant of fictitious play for cooperative action selection in robot teams, enabling efficient task completion through coordinated strategies.

Contribution

It presents a new game-theoretic learning algorithm for cooperative robot control, integrating fictitious play with the BDI framework for improved coordination.

Findings

Algorithm performs well in simulated coordination tasks

Effective in diverse scenarios like warehouse and sensor networks

Supports safe and considerate robot decision-making

Abstract

A game theoretic distributed decision making approach is presented for the problem of control effort allocation in a robotic team based on a novel variant of fictitious play. The proposed learning process allows the robots to accomplish their objectives by coordinating their actions in order to efficiently complete their tasks. In particular, each robot of the team predicts the other robots' planned actions while making decisions to maximise their own expected reward that depends on the reward for joint successful completion of the task. Action selection is interpreted as an $n$-player cooperative game. The approach presented can be seen as part of the \emph{Belief Desire Intention} (BDI) framework, also can address the problem of cooperative, legal, safe, considerate and emphatic decisions by robots if their individual and group rewards are suitably defined. After theoretical analysis the performance of the proposed algorithm is tested on four simulation scenarios. The first one is a coordination game between two material handling robots, the second one is a warehouse patrolling task by a team of robots, the third one presents a coordination mechanism between two robots that carry a heavy object on a corridor and the fourth one is an example of coordination on a sensors network.