Using abstractions for decision-theoretic planning with time constraints
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
An algorithm for probabilistic least-commitment planning
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Control strategies for a stochastic planner
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
World modeling for the dynamic construction of real-time control plans
Artificial Intelligence
Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment
Journal of the ACM (JACM)
Representing Plans Under Uncertainty: A Logic of Time, Chance, and Action
Representing Plans Under Uncertainty: A Logic of Time, Chance, and Action
Universal plans for reactive robots in unpredictable environments
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Expecting the unexpected: detecting and reacting to unplanned-for world states
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Planning with deadlines in stochastic domains
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Display of information for time-critical decision making
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
On the complexity of solving Markov decision problems
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Planning and resource allocation for hard real-time, fault-tolerant plan execution
Proceedings of the third annual conference on Autonomous Agents
QoS Negotiation in Real-Time Systems and Its Application to Automated Flight Control
IEEE Transactions on Computers
Planning and Resource Allocation for Hard Real-time, Fault-Tolerant Plan Execution
Autonomous Agents and Multi-Agent Systems
Self-Adaptive Software for Hard Real-Time Environments
IEEE Intelligent Systems
Development of iterative real-time scheduler to planner feedback
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Detecting and reacting to unplanned-for world states
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Development of iterative scheduler to planner feedback
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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Approximate models of world state transitions are necessary when building plans for complex systems operating in dynamic environments. External event probabilities can depend on state feature values as well as time spent in that particular state. We assign temporally-dependent probability functions to state transitions. These functions are used to locally compute state probabilities, which are then used to select highly probable goal paths and eliminate improbable states. This probabilistic model has been implemented in the Cooperative Intelligent Real-time Control Architecture (CIRCA), which combines an AI planner with a separate real-time system such that plans are developed, scheduled, and executed with real-time guarantees. We present flight simulation tests that demonstrate how our probabilistic model may improve CIRCA performance.