Probabilistic strain optimization under constraint uncertainty
نویسندگان
چکیده
منابع مشابه
Production Optimization under Uncertainty with Constraint Handling
To maximize the daily production from an oil and gas field, mathematical optimization may be used to find the optimal operating point. When optimizing, a model of the system is used to predict the outcome for different operating points. The model is, however, subject to uncertainty, e.g., the gas oil ratio estimates may be imprecise. The uncertainty is often ignored, and what is known as the ex...
متن کاملDistributed Constraint Optimization Under Stochastic Uncertainty
In many real-life optimization problems involving multiple agents, the rewards are not necessarily known exactly in advance, but rather depend on sources of exogenous uncertainty. For instance, delivery companies might have to coordinate to choose who should serve which foreseen customer, under uncertainty in the locations of the customers. The framework of Distributed Constraint Optimization u...
متن کاملProbabilistic Decision Graphs for optimization under Uncertainty
This paper provides a survey on probabilistic decision graphs for modeling and solving decision problems under uncertainty. We give an introduction to influence diagrams, which is a popular framework for representing and solving sequential decision problems with a single decision maker. As the methods for solving influence diagrams can scale rather badly in the length of the decision sequence, ...
متن کاملA distributed constraint optimization approach for coordination under uncertainty
Distributed Constraint Optimization (DCOP) provides a rich framework for modeling multi-agent coordination problems. Existing problem domains for DCOP focus on small (<100 variables), deterministic domains. We present a mapping to DCOP for large-scale team coordination problems that were used in the DARPA Coordinators program. This domain requires distributed, scalable algorithms to meet diffic...
متن کاملProbabilistic Logic under Uncertainty
Probabilistic logic combines the capability of binary logic to express the structure of argument models with the capacity of probabilities to express degrees of truth of those arguments. The limitation of traditional probabilistic logic is that it is unable to express uncertainty about the probability values themselves. This paper provides a brief overview subjective logic which is a probabilis...
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: BMC Systems Biology
سال: 2013
ISSN: 1752-0509
DOI: 10.1186/1752-0509-7-29