Principles of artificial intelligence
Principles of artificial intelligence
Artificial Intelligence
A model for reasoning about persistence and causation
Computational Intelligence
Proceedings of the seventh international conference (1990) on Machine learning
Planning and control
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Acting optimally in partially observable stochastic domains
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Learning Sorting and Decision Trees with POMDPs
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
STRIPS: a new approach to the application of theorem proving to problem solving
IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
Planning under uncertainty some key issues
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Learning to act using real-time dynamic programming
Artificial Intelligence
What is planning in the presence of sensing?
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
A robust and fast action selection mechanism for planning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Planning and Control in Artificial Intelligence: A Unifying Perspective
Applied Intelligence
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The problem of selecting action in environments that are dynamic and not completely predictable or observable is a central problem in intelligent behavior. From an AI point of view, the problem is to design a mechanism that can select the best actions given information provided by sensors and a suitable model of the actions and goals. We call this the problem of Planning as it is a direct generalization of the problem considered in Planning research where feedback is absent and the effect of actions is assumed to be predictable. In this paper we present an approach to Planning that combines ideas and methods from Operations Research and Artificial Intelligence. Basically Planning problems are described in high-level action languages that are compiled into general mathematical models of sequential decisions known as Markov Decision Processes or Partially Observable Markov Decision Processes, which are then solved by suitable Heuristic Search Algorithms. The result are controllers that map sequences of observations into actions, and which, under certain conditions can be shown to be optimal. We show how this approach applies to a number of concrete problems and discuss its relation to work in Reinforcement Learning.