Coalitional Skill Games for Self-Interested Robots With Svo

Abstract

Robots in a multi-robot system cannot communicate with each other due to various reasons. The robots can only maximize their own individual performance without regard for the overall system performance. To guarantee high system revenue even when the robots are self-interested, an algorithm called the intermediary recruitment algorithm (IRA) is proposed. It imitates the operating mechanism of an intermediary recruitment market to allocate the tasks to the robots. The simulation results analyse the influence of the robots' social value orientation (SVO) on the system revenue,where SVO is used to measure the degree of self-interest, and the effectiveness of the IRA is also verified.

Task Allocation and Utility Distribution Algorithms Based on Nash Bargaining Solution

“…In contrast, to improve the total system revenue, tasks should be allocated to service agents at lower costs. The IRA algorithm can solve the problem of utility distribution and task allocation in coalition skill games, but when the number of skills required by any task agent is large, the time complexity is high [13] . With the development of the economy and internet technology, the number of service agents and tasks in crowdsourcing and other practical applications is growing rapidly, and the tasks to be completed also show a trend of increasing complexity.…”