SOAR: an architecture for general intelligence
Artificial Intelligence
World modeling for the dynamic construction of real-time control plans
Artificial Intelligence
Linear Control Systems
Machine Learning
Universal plans for reactive robots in unpredictable environments
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Plan development using local probabilistic models
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
What is wrong with us? Improving robustness through social diagnosis
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Planning and resource allocation for hard real-time, fault-tolerant plan execution
Proceedings of the third annual conference on Autonomous Agents
Planning and Resource Allocation for Hard Real-time, Fault-Tolerant Plan Execution
Autonomous Agents and Multi-Agent Systems
Design-to-Criteria Scheduling: Real-Time Agent Control
Revised Papers from the International Workshop on Infrastructure for Multi-Agent Systems: Infrastructure for Agents, Multi-Agent Systems, and Scalable Multi-Agent Systems
S-assess: a library for behavioral self-assessment
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Robust agent teams via socially-attentive monitoring
Journal of Artificial Intelligence Research
Approximately optimal monitoring of plan preconditions
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
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The degree to which a planner succeeds and meets response deadlines depends on the correctness and completeness of its models which describe events and actions that change the world state. It is often unrealistic to expect perfect models, so a planner must detect and respond to states it had not planned to handle. In this paper, we characterize different classes of these "unhandled" states and describe planning algorithms to build tests for, and later respond to them. We have implemented these unhandled state detection and response algorithms in the Cooperative Intelligent Real-time Control Architecture (CIRCA), and present experiments from flight simulation that show how the new algorithm enables a fully-automated aircraft to react appropriately to certain classes of unhandled states, averting failure and giving the aircraft a new chance to achieve its goals.