Probabilistic modeling has been a dominant approach in Machine Learning research. As the field evolves, the problems of interest become increasingly challenging and complex. Makin...
Ming-Wei Chang, Lev-Arie Ratinov, Nicholas Rizzolo...
In this paper, we present a rule-based modelling language for constraint programming, called Rules2CP. Unlike other modelling languages, Rules2CP adopts a single knowledge represen...
This paper investigates how to represent and solve multiagent task scheduling as a Distributed Constraint Optimization Problem (DCOP). Recently multiagent researchers have adopted...
Evan Sultanik, Pragnesh Jay Modi, William C. Regli
A constraint satisfaction problem (CSP) is a problem to find an assignment that satisfies given constraints. An interesting approach to CSP is a repair-based method that first gen...
c Modular Abstractions for Template Numerical Constraints David Monniaux May 26, 2010 We propose a method for automatically generating abstract transformstatic analysis by abstrac...