Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Stochastic Boolean Satisfiability
Journal of Automated Reasoning
PPDP '99 Proceedings of the International Conference PPDP'99 on Principles and Practice of Declarative Programming
Uncertainty in Constraint Satisfaction Problems: a Probalistic Approach
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Semiring-Based CSPs and Valued CSPs: Basic Properties and Comparison
Over-Constrained Systems
A solver for quantified Boolean and linear constraints
Proceedings of the 2007 ACM symposium on Applied computing
Certainty closure: Reliable constraint reasoning with incomplete or erroneous data
ACM Transactions on Computational Logic (TOCL)
Algorithms for stochastic CSPs
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
CSCLP'05 Proceedings of the 2005 Joint ERCIM/CoLogNET international conference on Constraint Solving and Constraint Logic Programming
Event-Driven probabilistic constraint programming
CPAIOR'06 Proceedings of the Third international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Hi-index | 0.01 |
To model combinatorial decision problems involving uncertainty and probability, we extend the stochastic constraint programming framework proposed in [Walsh, 2002] along a number of important dimensions (e.g. to multiple chance constraints and to a range of new objectives). We also provide a new (but equivalent) semantics based on scenarios. Using this semantics, we can compile stochastic constraint programs down into conventional (nonstochastic) constraint programs. This allows us to exploit the full power of existing constraint solvers. We have implemented this framework for decision making under uncertainty in stochastic OPL, a language which is based on the OPL constraint modelling language [Hentenryck et al., 1999]. To illustrate the potential of this framework, we model a wide range of problems in areas as diverse as finance, agriculture and production.