A theory of diagnosis from first principles
Artificial Intelligence
Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Artificial intelligence and mathematical theory of computation
Decision making using probabilistic inference methods
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Structuring conditional relationships in influence diagrams
Operations Research
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Selected papers of international conference on Fifth generation computer systems 92
Acting optimally in partially observable stochastic domains
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Exploiting structure in policy construction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
The *-minimax search procedure for trees containing chance nodes
Artificial Intelligence
Exploiting contextual independence in probabilistic inference
Journal of Artificial Intelligence Research
Knowledge representation for stochastic decision processes
Artificial intelligence today
Computing optimal policies for partially observable decision processes using compact representations
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Mixed constraint satisfaction: a framework for decision problems under incomplete knowledge
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
SPUDD: stochastic planning using decision diagrams
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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This paper introduces the independent choice logic, and in particular the "single agent with nature" instance of the independent choice logic, namely ICLDT. This is a logical framework for decision making uncertainty that extends both logic programming and stochastic models such as influence diagrams. This paper shows how the representation of a decision problem within the independent choice logic can be exploited to cut down the combinatorics of dynamic programming. One of the main problems with influence diagram evaluation techniques is the need to optimise a decision for all values of the 'parents' of a decision variable. In this paper we show how the rule based nature of the ICLDT can be exploited so that we only make distinctions in the values of the information available for a decision that will make a difference to utility.