A Communication Protocol Supporting Dynamic Autonomy Agreements in Multi-agent Systems

Barber, K. Suzanne; Martin, Cheryl; McKay, Ryan

doi:10.1007/3-540-45408-x_31

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…The communication protocol for forming DMF agreements is fully documented in [6], including expected messages and protocol properties. This communication protocol is specifically designed to support conversations among two or more agents participating in a given negotiation to establish a DMF.…”

Section: Mechanism For Dmf Changementioning

confidence: 99%

Adaptive decision-making frameworks for dynamic multi-agent organizational change

Martin

Barber

2006

Auton Agent Multi-Agent Syst

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This article presents a capability called Adaptive Decision-Making Frameworks (ADMF) and shows that it can result in significantly improved system performance across run-time situation changes in a multi-agent system. Specifically, ADMF can result in improved and more robust performance compared to the use of a single static decision-making framework (DMF). The ADMF capability allows agents to dynamically adapt the DMF in which they participate to fit their run-time situation as it changes. A DMF identifies a set of agents and specifies the distribution of decisionmaking control and the authority to assign subtasks among these agents as they determine how a goal or set of goals should be achieved. The ADMF capability is a form of organizational adaptation and differs from previous approaches to organizational adaptation and dynamic coordination in that it is the first to allow dynamic and explicit manipulation of these DMF characteristics at run-time as variables controlling agent behavior. The approach proposed for selecting DMFs at run-time parameterizes all domain-specific knowledge as characteristics of the agents' situation, so the approach is application-independent. The presented evaluation empirically shows that, for at least one multi-agent system, there is no one best DMF for multiple agents across run-time situational changes. Next, it motivates the further exploration of ADMF by showing that adapting DMFs to run-time variations in situation can result in improved overall system performance compared to static or random DMFs.

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Section: Mechanism For Dmf Changementioning

confidence: 99%

Adaptive decision-making frameworks for dynamic multi-agent organizational change

Martin

Barber

2006

Auton Agent Multi-Agent Syst

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“…In fact, the possibility of delayed response leading to miscoordination does not appear to have been addressed at all. In the Dynamic Adaptive Autonomy framework a group of agents allocates a number of votes to each agent in a team, hence defining the amount of influence each agent has over a decision and thus, by their definition, the autonomy of the agent with respect to the goal (Barber, Martin, & Mckay 2000). The Dynamic Adapative Autonomy framework gives the team more detailed control over transfer of control decisions than does our approach, since only part of the decision making control can be transferred.…”

Section: Related Workmentioning

confidence: 99%

Adjustable Autonomy for the Real World

Scerri

Pynadath

Tambe

2003

Multiagent Systems, Artificial Societies, and Simulated Organizations

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Adjustable autonomy refers to agents' dynamically varying their own autonomy, transferring decision making control to other entities (typically human users) in key situations. Determining whether and when such transfers of control must occur is arguably the fundamental research question in adjustable autonomy. Previous work, often focused on individual agent-human interactions, has provided several different techniques to address this question. Unfortunately, domains requiring collaboration between teams of agents and humans reveals two key shortcomings of these previous techniques. First, these techniques use rigid one-shot transfers of control that can result in unacceptable coordination failures in multiagent settings. Second, they ignore costs (e.g., in terms of time delays or effects of actions) to an agent's team due to such transfers of control.To remedy these problems, this paper presents a novel approach to adjustable autonomy, based on the notion of transfer of control strategy. A transfer of control strategy consists of a sequence of two types of actions: (i) actions to transfer decision-making control (e.g., from the agent to the user or vice versa) (ii) actions to change an agent's pre-specified coordination constraints with others, aimed at minimizing miscoordination costs. The goal is for high quality individual decisions to be made with minimal disruption to the coordination of the team. These strategies are operationalized using Markov Decision Processes to select the optimal strategy given an uncertain environment and costs to individuals and teams. We present a detailed evaluation of the approach in the context of a real-world, deployed multi-agent system that assists a research group in daily activities.

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“…The autonomy of the actor relative to the user changes dynamically. Other architectures have AA between agents, e.g., [11,1], but for EASE the AA is only between the actor and the user. Changing the priority of agents in EASE would be similar to changing the number of votes as in Barber, et al [1].…”

Section: Discussionmentioning

confidence: 99%

A User Controlled Approach to Adjustable Autonomy

Reed

Proceedings of the 38th Annual Hawaii International Conference on System Sciences

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This paper describes a framework for collaboration between a user and a multi-agent system to achieve adjustable autonomy. Adjustable autonomy (AA) is when the levels of autonomy of the agent system -its control over its reasoning -changes during execution due to interaction with a user or other systems. We describe a prototype agent development environment that allows users flexible on-line control over an otherwise completely autonomous agent system. AA can improve productivity during system design, allow earlier deployment, and create a more flexible system. AA can reduce the load on instructors when used in training situations, and allow the user to aid the system when faced with unforseen situations or problems. Examples are given of AA in simulated pilots for fighter aircraft. We found that the AA -the user's ability to modify the behavior of the agents during execution, resulted in a better, more flexible system.

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A Communication Protocol Supporting Dynamic Autonomy Agreements in Multi-agent Systems

Cited by 4 publications

References 10 publications

Adaptive decision-making frameworks for dynamic multi-agent organizational change

Adaptive decision-making frameworks for dynamic multi-agent organizational change

Adjustable Autonomy for the Real World

A User Controlled Approach to Adjustable Autonomy

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