Towards the integration of artificial neural networks and constraint logic programming

We present a general framework for integrating artificial neural networks (ANN) into constraint logic programming for solving constraint satisfaction problems (CSPs). This framework is realized in a novel programming language PROCLANN, which uses the standard goal reduction strategy as frontend to generate constraints for an efficient backend ANN-based constraint-solver. PROCLANN retains the simple and elegant declarative semantics of constraint logic programming. Its operational semantics is probabilistic in nature but it possesses soundness and completeness results. An initial prototype of PROCLANN is constructed and provides empirical evidence that PROCLANN compares favourably against the state of art in CLP implementations on certain hard instances of CSP