Abstract
This paper presents a VLSI design for a competitive neural network model, known as GENET (Wang and Tsang 1991), for solving Constraint Satisfaction Problems (CSP). The CSP is a mathematical abstraction of the problems in many AI application domains. In essence, a CSP can be defined as a triple (Z, D, C), where Z is a finite set of variables, D is a mapping from every variable to a domain, which is a finite set of arbitrary objects, and C is a set of constraints. Each constraint in C restricts the values that can be simultaneously assigned to a number of variables in Z. If the constraints in C involve up to but no more than n variables it is called an n-ary CSP. The task is to assign one value per variable satisfying all the constraints in C (Mackworth 1977). In addition, associated with the variable assignments might be costs and utilities. This turns CSPs into optimization problems, demanding CSP solvers to find a set of variable assignments that would produce a maximum total utility at a minimal cost. Furthermore, some CSPs might be over-constrained, i.e. not all the constraints in C can be satisfied simultaneously. In this case, the set of assignments to a maximum number of variables without violating any constraints in C, or the set of assignments to all the variables which violates a minimal number of constraints might be sought for.
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Wang, C.J., Tsang, E.P.K. (1994). A Cascadable VLSI Design for GENET. In: Delgado-Frias, J.G., Moore, W.R. (eds) VLSI for Neural Networks and Artificial Intelligence. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-1331-9_19
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DOI: https://doi.org/10.1007/978-1-4899-1331-9_19
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