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In many anticipated applications of swarms, vehicles will work together to simultaneously search an area while servicing tasks (or jobs) as they appear. We call these Swarm Search and Service (SSS) missions.
While multi-agent interactions can be naturally modeled as a graph, the environment has traditionally been considered as a black box. To better utilize the inherent structure of our environment, we propose to create a shared agent-entity graph, where ...
As domestic service robots become more common and widespread, they must be programmed to efficiently accomplish tasks while aligning their actions with relevant norms. The first step to equip domestic robots with normative reasoning competence is ...
Autonomous AI systems will be entering human society in the near future to provide services and work alongside humans. For those systems to be accepted and trusted, the users should be able to understand the reasoning process of the system, i.e. the ...
Reaching agreement through consensus is fundamental to the operation of distributed systems such as sensor networks, social networks or multi-robot networks. Consensus requires agents in the system to reach an agreement over a variable of interest only ...
Robotic swarms are distributed systems whose members interact via local control laws to achieve a variety of behaviors, such as flocking. In many practical applications, human operators may need to change the current behavior of a swarm from the goal ...
This paper focuses on demand management of electricity in consumer groups that form a cooperative. We propose a novel multiagent coordination algorithm to shape the energy consumption of the cooperative in the presence of energy generation and storage. ...
In information driven multi-agent systems, information consumers collect information about their environment from various sources such as sensors. Each source has its own limitations, capabilities, and goals. Therefore, there is no guarantee that a ...
Large-scale multiagent systems have the potential to be highly dynamic. Trust and reputation are crucial concepts in these environments, as it may be necessary for agents to rely on their peers to perform as expected, and learn to avoid untrustworthy ...
We present a model of argumentation-based deliberative dialogue for decision making in a team of agents. The model captures conflicts among agents' plans due to scheduling and causality constraints, and conflicts between actions, goals and norms. We ...
An interesting class of multi-agent POMDP planning problems can be solved by having agents iteratively solve individual POMDPs, find interactions with other individual plans, shape their transition and reward functions to encourage good interactions and ...
Moving assets through a transportation network is a crucial challenge in hostile environments such as future battlefields where malicious adversaries have strong incentives to attack vulnerable patrols and supply convoys. Intelligent agents must balance ...
This article addresses the problem of activity recognition for dynamic, physically embodied agent teams. We define team activity recognition as the process of identifying team behaviors from traces of agent positions over time; for many physical domains,...
Distributed fusion of complex information is critical to the success of large organizations. For such organizations, comprised of thousands of agents, improving and shaping the quality of conclusions reached is a challenging problem. The challenge is ...
Recent work has provided tantalizing hints that small amounts of cooperation may actually hurt a group's performance rather than help it. In this paper, we take a systematic look at the value of cooperation. Using a simple cooperative task where agents ...
For an interesting class of emerging applications, a large robot team will need to distributedly allocate many more tasks than there are robots, with dynamically appearing tasks and a limited ability to communicate. The LA-DCOP algorithm can ...
Human users planning for multiple objectives in coalition environments are subjected to high levels of cognitive workload, which can severely impair the quality of the plans created. The cognitive workload is significantly increased when a user must not ...
In this paper, we propose a new approach to using probabilistic hierarchical task networks (HTNs) as an effective method for agents to plan in conditions in which their problem-solving knowledge is uncertain, and the environment is non-deterministic. In ...
The use of distributed POMDPs for cooperative teams has been severely limited by the incredibly large joint policyspace that results from combining the policy-spaces of the individual agents. However, much of the computational cost of exploring the ...
Large heterogeneous teams in a variety of applications must make joint decisions using large volumes of noisy and uncertain data. Often not all team members have access to a sensor, relying instead on information shared by peers to make decisions. These ...