Intention is choice with commitment
Artificial Intelligence
Exploiting belief bounds: practical POMDPs for personal assistant agents
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Using multiagent teams to improve the training of incident commanders
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Managing autonomy in robot teams: observations from four experiments
Proceedings of the ACM/IEEE international conference on Human-robot interaction
Lazy approximation for solving continuous finite-horizon MDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Hybrid BDI-POMDP framework for multiagent teaming
Journal of Artificial Intelligence Research
Towards adjustable autonomy for the real world
Journal of Artificial Intelligence Research
A fast analytical algorithm for solving Markov decision processes with real-valued resources
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Function allocation for NextGen airspace via agents
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: Industry track
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When human-multiagent teams act in real-time uncertain domains, adjustable autonomy (dynamic transferring of decisions between human and agents) raises three key challenges. First, the human and agents may differ significantly in their worldviews, leading to inconsistencies in their decisions. Second, these human-multiagent teams must operate and plan in real-time with deadlines with uncertain duration of human actions. Thirdly, adjustable autonomy in teams is an inherently distributed and complex problem that cannot be solved optimally and completely online. To address these challenges, our paper presents a solution for Resolving Inconsistencies in Adjustable Autonomy in Continuous Time (RIAACT). RIAACT incorporates models of the resolution of inconsistencies, continuous time planning techniques, and hybrid method to address coordination complexity. These contributions have been realized in a disaster response simulation system.