Decentralized Control of Cooperative Systems: Categorization and Complexity Analysis

TL;DR

This paper analyzes the complexity of decentralized control problems in cooperative systems, identifying classes with varying complexity and proposing algorithms for goal-oriented cases, while examining the impact of communication methods.

Contribution

It categorizes decentralized control problems by complexity and introduces polynomial-time algorithms for certain goal-oriented classes, also analyzing communication effects.

Findings

Decentralized control complexity ranges from NEXP to P.

Two algorithms solve goal-oriented classes in polynomial time.

Communication methods do not alter worst-case complexity.

Abstract

Decentralized control of cooperative systems captures the operation of a group of decision makers that share a single global objective. The difficulty in solving optimally such problems arises when the agents lack full observability of the global state of the system when they operate. The general problem has been shown to be NEXP-complete. In this paper, we identify classes of decentralized control problems whose complexity ranges between NEXP and P. In particular, we study problems characterized by independent transitions, independent observations, and goal-oriented objective functions. Two algorithms are shown to solve optimally useful classes of goal-oriented decentralized processes in polynomial time. This paper also studies information sharing among the decision-makers, which can improve their performance. We distinguish between three ways in which agents can exchange information: indirect communication, direct communication and sharing state features that are not controlled by the agents. Our analysis shows that for every class of problems we consider, introducing direct or indirect communication does not change the worst-case complexity. The results provide a better understanding of the complexity of decentralized control problems that arise in practice and facilitate the development of planning algorithms for these problems.