Conflicts in Policy-Based Distributed Systems Management
IEEE Transactions on Software Engineering
Adjustable autonomy in real-world multi-agent environments
Proceedings of the fifth international conference on Autonomous agents
Electric Elves: Applying Agent Technology to Support Human Organizations
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Towards Socially Sophisticated BDI Agents
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Conflict management for agent guidance
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Deploying a personalized time management agent
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Some basic concepts for shared autonomy: a first report
Proceedings of the 2008 conference on Collaborative Decision Making: Perspectives and Challenges
Interactive execution monitoring of agent teams
Journal of Artificial Intelligence Research
The fundamental principle of coactive design: interdependence must shape autonomy
COIN@AAMAS'10 Proceedings of the 6th international conference on Coordination, organizations, institutions, and norms in agent systems
Reasoning about preferences in intelligent agent systems
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
An operational semantics for the goal life-cycle in BDI agents
Autonomous Agents and Multi-Agent Systems
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Many potential applications for agent technology require humans and agents to work together in order to achieve complex tasks effectively. In contrast, much of the work in the agents community to date has focused on technologies for fully autonomous agent systems. This paper presents a framework for the directability of agents, in which a human supervisor can define policies to influence agent activities at execution time. The framework focuses on the concepts of adjustable autonomy for agents (ie, varying the degree to which agents make decisions without human intervention) and strategy preference (ie, recommending how agents should accomplish assigned task). The directability framework has been implemented within a PRS environment, and applied to a multiagent intelligence-gathering domain.