Theory of linear and integer programming
Theory of linear and integer programming
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Valuation-based systems for Bayesian decision analysis
Operations Research
Structuring conditional relationships in influence diagrams
Operations Research
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Algebraic decision diagrams and their applications
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Stochastic dynamic programming with factored representations
Artificial Intelligence
Topological parameters for time-space tradeoff
Artificial Intelligence
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Nonserial Dynamic Programming
Stochastic Boolean Satisfiability
Journal of Automated Reasoning
Treewidth: Algorithmoc Techniques and Results
MFCS '97 Proceedings of the 22nd International Symposium on Mathematical Foundations of Computer Science
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
A Qualitative Linear Utility Theory for Spohn's Theory of Epistemic Beliefs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Hybrid Processing of Beliefs and Constraints
UAI '01 Proceedings of the 17th Conference in 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
Hybrid backtracking bounded by tree-decomposition of constraint networks
Artificial Intelligence
Reasoning about Uncertainty
Representing and Solving Decision Problems with Limited Information
Management Science
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Arc consistency for soft constraints
Artificial Intelligence
Mixtures of deterministic-probabilistic networks and their AND/OR search space
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Decision with uncertainties, feasibilities, and utilities: towards a unified algebraic framework
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Performing Bayesian inference by weighted model counting
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Conditional plausibility measures and Bayesian networks
Journal of Artificial Intelligence Research
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
Great expectations: part II: generalized expected utility as a universal decision rule
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
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
Unconstrained influence diagrams
UAI'02 Proceedings of the Eighteenth 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
An order of magnitude calculus
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Decomposition of multi-operator queries on semiring-based graphical models
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Quantified Constraint Optimization
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
The Knowledge Engineering Review
Solving limited memory influence diagrams
Journal of Artificial Intelligence Research
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Numerous formalisms and dedicated algorithms have been designed in the last decades to model and solve decision making problems. Some formalisms, such as constraint networks, can express "simple" decision problems, while others are designed to take into account uncertainties, unfeasible decisions, and utilities. Even in a single formalism, several variants are often proposed to model different types of uncertainty (probability, possibility...) or utility (additive or not). In this article, we introduce an algebraic graphical model that encompasses a large number of such formalisms: (1) we first adapt previous structures from Friedman, Chu and Halpern for representing uncertainty, utility, and expected utility in order to deal with generic forms of sequential decision making; (2) on these structures, we then introduce composite graphical models that express information via variables linked by "local" functions, thanks to conditional independence; (3) on these graphical models, we finally define a simple class of queries which can represent various scenarios in terms of observabilities and controllabilities. A natural decision-tree semantics for such queries is completed by an equivalent operational semantics, which induces generic algorithms. The proposed framework, called the Plausibility-Feasibility-Utility (PFU) framework, not only provides a better understanding of the links between existing formalisms, but it also covers yet unpublished frameworks (such as possibilistic influence diagrams) and unifies formalisms such as quantified boolean formulas and influence diagrams. Our backtrack and variable elimination generic algorithms are a first step towards unified algorithms.