Scheduling algorithms for multiprogramming in a hard real-time environment
Tutorial: hard real-time systems
World modeling for the dynamic construction of real-time control plans
Artificial Intelligence
Introduction to knowledge systems
Introduction to knowledge systems
A Negotiation-based Interface Between a Real-time Scheduler and a Decision-Maker
A Negotiation-based Interface Between a Real-time Scheduler and a Decision-Maker
Plan development using local probabilistic models
UAI'96 Proceedings of the Twelfth international 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
An Intelligent System Combining Different Resource-Bounded Reasoning Techniques
Applied Intelligence
Planning and Resource Allocation for Hard Real-time, Fault-Tolerant Plan Execution
Autonomous Agents and Multi-Agent Systems
Probabilistic, Prediction-Based Schedule Debugging for Autonomous Robot Office Couriers
KI '99 Proceedings of the 23rd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Plan-based control of robotic agents: improving the capabilities of autonomous robots
Plan-based control of robotic agents: improving the capabilities of autonomous robots
Distributed reasoning for multiagent simple temporal problems
Journal of Artificial Intelligence Research
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Planning for real-time applications involves decisions not only about what actions to take in what states to progress toward achieving goals (the traditional decision problem faced by AI planning systems), but also about how to realize those actions within hard real-time deadlines given the inherent limitations of an execution platform. Determining how to arrange actions in a sequence such that timely execution is guaranteed within constraints is a manifestation of the scheduling problem. All cases of the scheduling problem in any domain of nontrivial complexity are difficult to solve (NP-Hard). To more efficiently solve the real-time plan scheduling problem, we propose and analyze an iterative feedback/constraint relaxation method in which a scheduler and planner iteratively interact to efficiently develop a well-utilized schedule which includes as many planned actions as possible. This method has been successfully implemented within the Cooperative Intelligent Real-time Control Architecture (CIRCA).