C4.5: programs for machine learning
C4.5: programs for machine learning
Experience with a learning personal assistant
Communications of the ACM
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Mixed-initiative decision support in agent-based automated contracting
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Adaptive Agent Integration Architectures for Heterogeneous Team Members
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Journal of Artificial Intelligence Research
Proceedings of the 1st international conference on Knowledge capture
Why the elf acted autonomously: towards a theory of adjustable autonomy
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Towards any-team coaching in adversarial domains
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Proceedings of the 2002 ACM SIGGRAPH/Eurographics symposium on Computer animation
Intelligent Control of Life Support for Space Missions
IEEE Intelligent Systems
Revisiting Asimov's First Law: A Response to the Call to Arms
ATAL '01 Revised Papers from the 8th International Workshop on Intelligent Agents VIII
Towards adjustable autonomy for the real world
Journal of Artificial Intelligence Research
Human-robot communication for collaborative decision making - A probabilistic approach
Robotics and Autonomous Systems
Autonomous Agents and Multi-Agent Systems
A Decision-Theoretic Approach to Cooperative Control and Adjustable Autonomy
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Modeling the Rhetoric of Human-computer interaction
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: interaction techniques and environments - Volume Part II
A comparing method of two team behaviours in the simulation coach competition
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
Hi-index | 0.00 |
Through {\em adjustable autonomy} (AA), an agent can dynamically vary the degree to which it acts autonomously, allowing it to exploit human abilities to improve its performance, but without becoming overly dependent and intrusive in its human interaction. AA research is critical for successful deployment of multi-agent systems in support of important human activities. While most previous AA work has focused on individual agent-human interactions, this paper focuses on {\em teams} of agents operating in real-world human organizations. The need for agent teamwork and coordination in such environments introduces novel AA challenges. First, agents must be more judicious in asking for human intervention, because, although human input can prevent erroneous actions that have high team costs, one agent's inaction while waiting for a human response can lead to potential miscoordination with the other agents in the team. Second, despite appropriate local decisions by individual agents, the overall team of agents can potentially make global decisions that are unacceptable to the human team. Third, the diversity in real-world human organizations requires that agents gradually learn individualized models of the human members, while still making reasonable decisions even before sufficient data are available. We address these challenges using a multi-agent AA framework based on an adaptive model of users (and teams) that reasons about the uncertainty, costs, and constraints of decisions at {\em all} levels of the team hierarchy, from the individual users to the overall human organization. We have implemented this framework through Markov decision processes, which are well suited to reason about the costs and uncertainty of individual and team actions. Our approach to AA has proven essential to the success of our deployed multi-agent Electric Elves system that assists our research group in rescheduling meetings, choosing presenters, tracking people's locations, and ordering meals.