Logic Programming and Artificial Neural Networks

2016 ◽  
pp. 185-220
Author(s):  
Sebastian Bader ◽  
Pascal Hitzler ◽  
Anthony Seda
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Logic Programming ◽  
Artificial Neural

A FRAMEWORK FOR INTEGRATING ARTIFICIAL NEURAL NETWORKS AND LOGIC PROGRAMMING

1995 ◽  
Vol 04 (01n02) ◽  
pp. 3-32 ◽  
Author(s):  
J.H.M. LEE ◽  
V.W.L. TAM
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Logic Programming ◽  
Operational Semantics ◽  
Real Life ◽  
Constraint Logic Programming ◽  
Constraint Satisfaction Problems ◽  
Problem Size ◽  
Life Problems ◽  
Artificial Neural

Many real-life problems belong to the class of constraint satisfaction problems (CSP’s), which are NP-complete, and some NP-hard, in general. When the problem size grows, it becomes difficult to program solutions and to execute the solution in a timely manner. In this paper, we present a general framework for integrating artificial neural networks and logic programming so as to provide an efficient and yet expressive programming environment for solving CSP’s. To realize this framework, we propose PROCLANN, a novel constraint logic programming language. The PROCLANN language retains the simple and elegant declarative semantics of constraint logic programming. Operationally, PROCLANN uses the standard goal reduction strategy in the frontend to generate constraints, and an efficient backend constraint-solver based on artificial neural networks. Its operational semantics is probabilistic in nature. We show that PROCLANN is sound and weakly complete. A novelty of PROCLANN is that while it is a committed-choice language, PROCLANN supports non-determinism, allowing the generation of multiple answers to a query. An initial prototype implementation of PROCLANN is constructed and demonstrates empirically that PROCLANN out-performs the state of art in constraint logic programming implementation on certain hard instances of CSP’s.

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