Operations Research
Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for reasoning about persistence and causation
Computational Intelligence
Formulation of tradeoffs in planning under uncertainty
Formulation of tradeoffs in planning under uncertainty
Abstract probabilistic modeling of action
Proceedings of the first international conference on Artificial intelligence planning systems
Conditional nonlinear planning
Proceedings of the first international conference on Artificial intelligence planning systems
An algorithm for probabilistic least-commitment planning
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
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
A model for projection and action
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
IJCAI'75 Proceedings of the 4th international joint conference on Artificial intelligence - Volume 1
Universal plans for reactive robots in unpredictable environments
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Reactive reasoning and planning
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
Anytime synthetic projection: maximizing the probability of goal satisfaction
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
The STRIPS assumption for planning under uncertainty
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Planning with deadlines in stochastic domains
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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When agents devise plans for execution in the real world, they face two important forms of uncertainty: they can never have complete knowledge about the state of the world, and they do not have complete control, as the effects of their actions are uncertain. While most classical planning methods avoid explicit uncertainty reasoning, we believe that uncertainty should be explicitly represented and reasoned about. We develop a probabilistic representation for states and actions, based on belief networks. We define conditional belief nets (CBNs) to capture the probabilistic dependency of the effects of an action upon the state of the world. We also use a CBN to represent the intrinsic relationships among entities in the environment, which persist from state to state. We present a simple projection algorithm to construct the belief network of the state succeeding an action, using the environment CBN model to infer indirect effects. We discuss how the qualitative aspects of belief networks and CBNs make them appropriate for the various stages of the problem solving process, from model construction to the design of planning algorithms.