Abstract—A cooperative team’s performance strongly depends on the view that the team has of the environment in which it operates. In a team with many autonomous vehicles and many sensors, there is a large volume of information available from which to create that view. However, typically communication bandwidth limitations prevent all sensor readings being shared with all other team members. This paper presents three policies for sharing information in a large team that balance the value of information against communication costs. Analytical and empirical evidence of their effectiveness is provided. The results show that using some easily obtainable probabilistic information about the team dramatically improves overall belief sharing performance. Specifically, by collectively estimating the value of a piece of information, the team can make most efficient use of its communication resources. I.

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To perform large-scale coordination in real-world environments requires that many individually complex technologies come together to form integrated solutions. We present an application where several key technologies are integrated into a unified system via a multiagent infrastructure. We show how the synergistic behavior among heterogeneous technologies results in a significant improvement over the performance of the individual technologies acting alone. Critical extensions were required to the language describing required behavior to allow the pieces to work together. Initial experimental results show system performance on a task of coordinating a military convoy in an adversarial environment was significantly improved when all technologies worked together. However, experiments with a human user in the loop showed that significant advances must still be made before such systems can be fielded in the real-world.

many challenges since they require a tight combination with the physical world as well as a balance between autonomous operation and coordination among heterogeneous nodes. These fundamental challenges range from how NCPSs are architected, implemented, composed, and programmed to how they can be validated. In this paper, we describe a new paradigm for programming an NCPS that enables users to specify their needs and nodes to contribute capabilities and resources. This new paradigm is based on the partially ordered knowledge sharing model that makes explicit the abstract structure of a computation in space and time. Based on this model, we propose an application framework that provides a uniform abstraction for a wide range of NCPS applications, especially those concerned with distributed sensing, optimization, and control. The proposed framework provides a generic service to represent, manipulate, and share knowledge across the network under minimal assumptions on connectivity. Our framework is tested on a new distributed version of an evolutionary optimization algorithm that runs on a computing cluster and is also used to solve a dynamic distributed optimization problem in a simulated NCPS that uses mobile robots as controllable data mules. I.

Abstract. The performance of a cooperative team depends on the views that individual team members build of the environment in which they are operating. Teams with many vehicles and sensors generate a large amount of information from which to create those views. However, bandwidth limitations typically prevent exhaustive sharing of this information. As team size and information diversity grows, it becomes even harder to provide agents with needed information within bandwidth constraints, and it is impractical for members to maintain any detailed information for every team mate. Building on previous token-based algorithms, this chapter presents an approach for efficiently sharing information in large teams. The key distinction from previous work is that this approach models differences in how agents in the team value knowledge and certainty about features. By allowing the tokens passed through the network to passively estimate the value of certain types of information to regions of the network, it is possible to improve token routing through the use of local decision-theoretic models. We show that intelligent routing and stopping can increase the amount of locally useful information received by team members while making more efficient use of agents’ communication resources. 1

Abstract- Many exciting, emerging applications require that a group of agents share a coherent view of the world given spatial distribution, incomplete and uncertain sensors, and communication constraints. This article describes an analysis and design methodology for distributed algorithms that coordinate the exchange of information for extremely large groups of agents maintaining a coherent belief in some environment property. The design methodology uses the tools of statistical mechanics to create a probability distribution which relates groupings of agents, called Sharing Groups, to pair-wise agent divergences and social temperature. Social temperature is a decision parameter, the same for all agents, that agents use probabilistically to decide when to join a Sharing Group. We show empirically as well as via Monte-Carlo simulations that for a critical value of social temperature the sharing groups formed result in bandwidth efficiency and divergence from ground-truth that is simultaneously optimal independent of the method of information exchange. 1

Hierarchies are one of the most common organizational structures observed in multi-agent systems. In this paper we study vertical specialization as a reason for hierarchical structures. In vertically specialized systems, more highly skilled agents are also more costly. By using less capable agents to initially process tasks and forwarding only exceptional tasks to more capable agents, such systems may be able to economize on the number of highly capable agents. The result is a hierarchical structure with least capable agents at the bottom. However, such a structure increases the delay in completing some tasks, because they must pass through multiple levels of control. Thus, vertical specialization presents a tradeoff between economizing on skilled agents and increasing task completion time. We find that for a wide range of settings, vertical specialization induces an optimal hierarchy of height at most three. This suggests that a multi-agent system designer interested in exploiting vertical specialization needs to use at most three levels of specialization in order to reap most of the benefits.

Abstract — We study the problem of designing a ground robot that learns how to navigate in natural, outdoor settings. This robot must learn, through its own experience, a traversability model. This model provides a mapping from the robot’s sensors to a traversability measure. We propose a novel approach to exploration in which the robot purposefully probes into apparent obstacles, with the goal of improving its traversability model. We formulate a decision-theoretic algorithm that identifies potential “tunnels ” through apparent obstacles and estimates the benefit of exploring them. We have implemented and tested the algorithm on an outdoor robot platform and we present results from initial experiments that demonstrate the operation of our algorithm and quantify its performance advantage over a baseline system. I.