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Constraint satisfaction/optimization is a powerful paradigm for solving numerous tasks in distributed AI, including planning, scheduling and resource allocation. However, up to now, distributed algorithms for constraint reasoning (especially optimization) ...
The operating rules of water allocation in the alpine OFIMA hydropower network of the Maggia River basin (Canton Tessin, Switzerland) are investigated in response to changes in the production policy and environmental and climatic factors. The study was car ...
Econometric models play an important role in transportation analysis. Estimating more and more complex models becomes problematic. The associated log-likelihood function is highly nonlinear and non concave and the comlexity of the model requires constraint ...
Econometric models play an important role in transportation analysis. Estimating more and more complex models becomes problematic. The associated log-likelihood function is highly nonlinear and non concave and the comlexity of the model requires constraint ...
This mapping problem has to be solved in many application scenarios. In the automotive industry, for example, the implementation of car functions involves distributed task sets running on multiple electronic control units (ECU) with bus-based inter-task co ...
In recent years the electricity sector has undergone a number of changes that all point in the direction of liberalization and decentralization of control. However, a number of technological challenges have to be addressed before the desired degree of dece ...
A constraint satisfaction problem (e.g., a system of equations and inequalities) consists of a finite set of constraints specifying which value combinations from given variable domains are admitted. It is called numerical if its variable domains are contin ...
Improving the productivity of fed-batch filamentous fungal fermentations can be formulated as a dynamic optimization problem. However, numerical optimization based on a nominal process model is typically insufficient when uncertainty in the form of model m ...
An accurate model of the user's preferences is a crucial element of most decision support systems. It is often assumed that users have a well-defined and stable set of preferences that can be elicited through a set of questions. However, recent research ha ...
American Association for Artificial Intelligence, Menlo Park, CA 94025-3496, United States2005
We propose a framework for component-based modeling using an abstract layered model for components. A component is the superposition of two models: a behavior model and an interaction model. Interaction models describe architectural constraints induced by ...