Human control strategies for multi-robot teams

Expanding human span of control over teams of robots presents an obstacle to the wider deployment of robots for practical tasks in a variety of areas. One difficulty is that many different types of human interactions may be necessary to maintain and control a robot team. We have developed a taxonomy of human-robot tasks based on complexity of control that helps explicate the forms of control likely to be needed and the demands they pose to human operators. In this paper we use research from two of these areas to illustrate our taxonomy and its utility in characterizing and improving human-robot interaction

Modeling User Perception of Interaction Opportunities for Effective Teamwork

This paper presents a model of collaborative decision-making for groups that involve people and computer agents. The model distinguishes between actions relating to participantspsila commitment to the group and actions relating to their individual tasks, uses this distinction to decompose group decision making into smaller problems that can be solved efficiently. It allows computer agents to reason about the benefits of their actions on a collaboration and the ways in which human participants perceive these benefits. The model was tested in a setting in which computer agents need to decide whether to interrupt people to obtain potentially valuable information. Results show that the magnitude of the benefit of interruption to the collaboration is a major factor influencing the likelihood that people will accept interruption requests. They further establish that peoplepsilas perceived type of their partners (whether humans or computers) significantly affected their perceptions of the usefulness of interruptions when the benefit of the interruption is not clear-cut. These results imply that system designers need to consider not only the possible benefits of interruptions to collaborative human-computer teams but also the way that such benefits are perceived by people.Engineering and Applied Science

Multiagent Teamwork: Hybrid Approaches

Conference paper published in CSI Communications</p

A prototype infrastructure for distributed robot-agent-person teams

Effective coordination of robots, agents and people promises to improve the safety, robustness and quality with which shared goals are achieved by harnessing the highly heterogeneous entities&apos; diverse capabilities. Proxy-based integration architectures are emerging as a standard method for coordinating teams of heterogeneous entities. Such architectures are designed to meet imposing challenges such as ensuring that the diverse capabilities of the group members are effectively utilized, avoiding miscoordination in a noisy, uncertain environment and reacting flexibly to changes in the environment. However, we contend that previous architectures have gone too far in taking coordination responsibility away from entities and giving it to proxies. Our goal is to create a proxy-based integration infrastructure where there is a beneficial symbiotic relationship between the proxies and the team members. By leveraging the coordination abilities of both proxies and socially capable team members the quality of the coordination can be improved. We present two key new ideas to achieve this goal. First, coordination tasks are represented as explicit roles, hence the responsibilities not the actions are specified, thus allowing the team to leverage the coordination skills of the most capable team members. Second, building on the first idea, we have developed a novel role allocation and reallocation algorithm. These ideas have been realized in a prototype software proxy architecture and used to create heterogeneous teams for an urban disaster recovery domain. Using the rescue domain as a testbed, we have experimented with the role allocation algorithm and observed results to support the hypothesis that leveraging the coordination capabilities of people can help the performance of the te..

A Contextual Approach To Learning Collaborative Behavior Via Observation

This dissertation describes a novel technique to creating a simulated team of agents through observation. Simulated human teamwork can be used for a number of purposes, such as expert examples, automated teammates for training purposes and realistic opponents in games and training simulation. Current teamwork simulations require the team member behaviors be programmed into the simulation, often requiring a great deal of time and effort. None are able to observe a team at work and replicate the teamwork behaviors. Machine learning techniques for learning by observation and learning by demonstration have proven successful at observing behavior of humans or other software agents and creating a behavior function for a single agent. The research described here combines current research in teamwork simulations and learning by observation to effectively train a multi-agent system in effective team behavior. The dissertation describes the background and work by others as well as a detailed description of the learning method. A prototype built to evaluate the developed approach as well as the extensive experimentation conducted is also described

An Architecture For Multi-Agent Systems Operating In Soft Real-Time Environments With Unexpected Events

In this thesis, we explore the topic of designing an architecture and processing algorithms for a multi-agent system, where agents need to address potential unexpected events in the environment, operating under soft real-time constraints. We first develop a classification of unexpected events into Opportunities, Barriers and Potential Causes of Failure, and outline the interaction required to support the allocation of tasks for these events. We then propose a hybrid architecture to provide for agent autonomy in the system, employing a central coordinating agent. Certain agents in the community operate autonomously, while others remain under the control of the coordinating agent. The coordinator is able to determine which agents should form teams to address unexpected events in a timely manner, and to oversee those agents as they perform their tasks. The proposed architecture avoids the overhead of negotiation amongst agent teams for the assignment of tasks, a benefit when operating under limited time and resource constraints. It also avoids the bottleneck of having one coordinating agent making all decisions before work can proceed in the community, by allowing some agents to work independently. We illustrate the potential usefulness of the framework by describing an implementation of a simulator loosely based on that used for the RoboCup Rescue Simulation League contest. The implementation provides a set of simulated computers, each running a simple soft real-time operating system. On top of this basic simulation we implement the model described above and test it against two different search-and-rescue scenarios. From our experiments, we observe that our architecture is able to operate in dynamic and real-time environments, and can handle, in an appropriate and timely manner, any unexpected events that occur. We also comment on the value of our proposed approach for designing adjustable autonomy multi-agent systems and for specific environments such as robotics, where reducing the overall level of communication within the system is crucial