Finding reliable solutions: event-driven probabilistic constraint programming
نویسندگان
چکیده
منابع مشابه
Finding reliable solutions: event-driven probabilistic constraint programming
Real-life management decisions are usually made in uncertain environments, and decision support systems that ignore this uncertainty are unlikely to provide realistic guidance. We show that previous approaches fail to provide appropriate support for reasoning about reliability under uncertainty. We propose a new framework that addresses this issue by allowing logical dependencies between constr...
متن کاملA Sample Average Approximation Approach for Event-Driven Probabilistic Constraint Programming
In this work we augment a known Monte Carlo simulationbased approach to stochastic discrete optimization problem, the so called Sample Average Approximation (SAA) method, with a new criterion to decide when the search has to be stopped. Our approach exploits a well known and effective sampling technique, Latin Hypercube Sampling (LHS), and confidence interval analysis, a well established approx...
متن کاملFinding Diverse and Similar Solutions in Constraint Programming
It is useful in a wide range of situations to find solutions which are diverse (or similar) to each other. We therefore define a number of different classes of diversity and similarity problems. For example, what is the most diverse set of solutions of a constraint satisfaction problem with a given cardinality? We first determine the computational complexity of these problems. We then propose a...
متن کاملProbabilistic Constraint Logic Programming
This paper addresses two central problems for probabilistic processing models: parameter estimation from incomplete data and efficient retrieval of most probable analyses. These questions have been answered satisfactorily only for probabilistic regular and context-free models. We address these problems for a more expressive probabilistic constraint logic programming model. We present a log-line...
متن کاملProbabilistic Concurrent Constraint Programming
We extend cc to allow the specification of a discrete probability distribution for random variables. We demonstrate the expressiveness of pcc by synthesizing combinators for default reasoning. We extend pcc uniformly over time, to get a synchronous reactive probabilistic programming language, Timed pcc. We describe operational and denotational models for pcc (and Timed pcc). The key feature of ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Annals of Operations Research
سال: 2008
ISSN: 0254-5330,1572-9338
DOI: 10.1007/s10479-008-0382-6