Distributed Constraint Optimization for Mobile Sensor Teams (Doctoral Consortium)

Abstract

Coordinating a mobile sensing agents (MST) to adequately position themselves with regards to points of interest generally called targets (e.g., disaster survivors, military targets, or pollution spills), is a challenging problem in many multiagent applications. Such applications are inherently dynamic due to changes in the environment, technology failures, and incomplete knowledge of the agents. Agents must adaptively respond by changing their locations to continually optimize the coverage of targets. Optimally choosing where to position agents to meet the coverage requirements in a static setting is a known NP-hard optimization problem. Doing so in a dynamic distributed environment is a challenging task. In this work I continue to develop and study the DCOP MST model DCOP is a general model of distributed multi-agent coordination. A DCOP is constituted of agents, variables, and (soft and hard) constraints between sets of variables that reflect the costs of assignments to the variables. Each agent has exclusive control over a subset of the variables and knows information relevant to its variables, such as the values that can be assigned to them (their domains) and the constraints involving them. The goal is to select an assignment of values to the variables that minimizes the aggregated costs of the constraints. In many ways DCOPs are a natural fit for MST applications, which are inherently decentralized. However, DCOPs fall short in two ways. First, constraints in a MST problem may involve all agents which can result in an exponential-sized constraint structure, which is difficult to solve. Second, DCOP is a static model. In contrast, the coverage problem confronting the agents in realistic applications is highly dynamic. There are three types of dynamism in MST applications: changes in the environment external to the agents, including targets arising, moving, and disappearing, or target coverage requirements being modified by an outside authority; changes inherent to the agents, including sensor failures resulting in targets being missed or false information being disseminated; and changes in the agents' knowledge of the environment, such as the presence of tar- gets and the quality with which they can be sensed from different locations. In DCOP MST, agents maintain variables for their physical positions, while each target is represented by a constraint that reflects the quality of coverage of that target. In contrast to conventional, static DCOP, DCOP MST not only permits dynamism but exploits it by restricting variable domains to nearby locations; consequently, variable domains and constraints change as the agents move through the environment. DCOP MST confers three major advantages. It directly represents the multiple forms of dynamism inherent in MSTs. It also provides a compact representation that can be solved efficiently with local search algorithms, with information and communication locality based on physical locality as typically occurs in MST applications. Finally, DCOP MST facilitates organization of the team into multiple sub-teams that can specialize in different roles and coordinate their activity through dynamic events. We demonstrate how a search-and-detection team responsible for finding new targets and a surveillance sub-team tasked with coverage of known targets can effectively work together to improve performance while using the DCOP MST framework to coordinate. We propose different algorithms to meet the specific needs of each sub-team and several methods for cooperation between sub-teams. For the search-and-detection team, we develop an algorithm based on DSA that forces intensive exploration for new targets. For the surveillance sub-team, we adapt several well-known incomplete DCOP algorithms, including the Maximum Gain Messages (MGM) algorithm, the Distributed Stochastic Algorithm (DSA) and the Max-sum algorithm which requires us to develop an efficient method for agents to find the value assignment in their local environment, which is optimal in minimizing the maximum unmet coverage requirement over all targets. In order to avoid an exponential constraint network, instead of choosing from among all possible locations, each agent considers only nearby locations. Constraints thus do not need to involve all agents at all times but only the agents who are close enough to possibly cover the target. The disadvantage of dynamic domains based on physical locality is that adaptations of standard local search algorithms tend to become trapped in local optima where targets beyond the immediate range of the agents go uncovered. To address this shortcoming we develop exploration methods to be used with the local search algorithms. In designing the algorithms that the agents run, we must balance 172