Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Stochastic dynamic programming with factored representations
Artificial Intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Stochastic Boolean Satisfiability
Journal of Automated Reasoning
A general non-probabilistic theory of inductive reasoning
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Valuation-based systems for discrete optimisation
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Beyond NP: Arc-Consistency for Quantified Constraints
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Information Algebras: Generic Structures for Inference
Information Algebras: Generic Structures for Inference
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Arc consistency for soft constraints
Artificial Intelligence
Conditional plausibility measures and Bayesian networks
Journal of Artificial Intelligence Research
In the quest of the best form of local consistency for weighted CSP
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Great expectations: part I: on the customizability of generalized expected utility
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Algebraic Markov decision processes
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Mixed constraint satisfaction: a framework for decision problems under incomplete knowledge
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Reasoning with conditional ceteris paribus preference statements
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Qualitative models for decision under uncertainty without the commensurability assumption
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Graphical models for preference and utility
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Plausibility measures: a user's guide
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Complexity results and algorithms for possibilistic influence diagrams
Artificial Intelligence
An algebraic graphical model for decision with uncertainties, feasibilities, and utilities
Journal of Artificial Intelligence Research
On valued negation normal form formulas
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
On residuation in multilattices: Filters, congruences, and homomorphisms
Fuzzy Sets and Systems
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Several formalisms exist to express and solve decision problems. Each is designed to capture different kinds of knowledge: utilities expressing preferences, uncertainties on the environment, or feasibility constraints on the decisions, with a possible sequential aspect. Despite the fact that every framework relies on specific properties exploited by dedicated algorithms, these formalisms present interesting similarities. In this paper, we show that it is possible to capture these similarities in a generic algebraic framework for sequential decision making with uncertainties, feasibilities, and utilities. This framework subsumes several existing approaches, from constraint satisfaction problems to quantified boolean formulas, Bayesian networks or possibilistic Markov decision processes. We introduce this framework using a toy example, increasingly sophisticated by uncertainties, feasibilities and possible observations. This leads to a formal definition of the framework together with dedicated queries representing usual decision problems. Generic algorithms for solving the considered queries should allow to both factorize existing algorithmic works and allow for cross-fertilization between the subsumed formalisms.