Artificial Intelligence - Special issue on knowledge representation
The H∞ control problem
Risk-Sensitive Control on an Infinite Time Horizon
SIAM Journal on Control and Optimization
The benefits of relaxing punctuality
Journal of the ACM (JACM)
Neuro-Dynamic Programming
Planning for temporally extended goals
Annals of Mathematics and Artificial Intelligence
TALplanner: A temporal logic based forward chaining planner
Annals of Mathematics and Artificial Intelligence
Constraint Processing
Least-squares policy iteration
The Journal of Machine Learning Research
Dynamic programming for structured continuous Markov decision problems
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Convex Approximations of Chance Constrained Programs
SIAM Journal on Optimization
Coordinating agile systems through the model-based execution of temporal plans
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Risk-sensitive reinforcement learning applied to control under constraints
Journal of Artificial Intelligence Research
Modelling mixed discrete-continuous domains for planning
Journal of Artificial Intelligence Research
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Kongming: a generative planner for hybrid systems with temporally extended goals
Kongming: a generative planner for hybrid systems with temporally extended goals
Optimization over state feedback policies for robust control with constraints
Automatica (Journal of IFAC)
COLIN: planning with continuous linear numeric change
Journal of Artificial Intelligence Research
Risk-sensitive plan execution for connected sustainable home
BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
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This paper presents a model-based planner called the Probabilistic Sulu Planner or the p-Sulu Planner, which controls stochastic systems in a goal directed manner within user-specified risk bounds. The objective of the p-Sulu Planner is to allow users to command continuous, stochastic systems, such as unmanned aerial and space vehicles, in a manner that is both intuitive and safe. To this end, we first develop a new plan representation called a chance-constrained qualitative state plan (CCQSP), through which users can specify the desired evolution of the plant state as well as the acceptable level of risk. An example of a CCQSP statement is "go to A through B within 30 minutes, with less than 0.001% probability of failure." We then develop the p-Sulu Planner, which can tractably solve a CCQSP planning problem. In order to enable CCQSP planning, we develop the following two capabilities in this paper: 1) risk-sensitive planning with risk bounds, and 2) goal-directed planning in a continuous domain with temporal constraints. The first capability is to ensures that the probability of failure is bounded. The second capability is essential for the planner to solve problems with a continuous state space such as vehicle path planning. We demonstrate the capabilities of the p-Sulu Planner by simulations on two real-world scenarios: the path planning and scheduling of a personal aerial vehicle as well as the space rendezvous of an autonomous cargo spacecraft.