Decentralized Control of Cooperative Systems: Categorization and Complexity Analysis

TLDR

Decentralized control of cooperative systems, where multiple agents share a global objective, is NEXP‑complete and becomes harder when agents lack full observability of the global state. The paper aims to classify decentralized control problems with complexity between NEXP and P by examining independent transitions, independent observations, goal‑oriented objectives, and to assess how information sharing among agents can improve performance. The authors analyze problem classes defined by independent transitions, independent observations, and goal‑oriented objectives, and distinguish three information exchange modes—indirect communication, direct communication, and sharing uncontrolled state features—to study their impact. They present two polynomial‑time algorithms for optimally solving useful goal‑oriented decentralized processes, show that adding direct or indirect communication does not alter worst‑case complexity, and provide insights that aid planning algorithm development.

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.