A neural implementation of multi-adjoint logic programs via sf-homogenization

A generalization of the homogenization process needed for the neural im- plementation of multi-adjoint logic programming (a unifying theory to deal with uncertainty, imprecise data or incomplete information) is presented here. The idea is to allow to represent a more general family of adjoint pairs, but maintaining the advantage of the existing implementation recently introduced in [6]. The soundness of the transformation is proved and its complexity is analysed. In addition, the corresponding generalization of the neural-like implementation of the fixed point semantics of multi-adjoint is presented

Aspects of functional programming

This thesis explores the application of functional programming in new areas and its implementation using new technologies. We show how functional languages can be used to implement solutions to problems in fuzzy logic using a number of languages: Haskell, Ginger and Aladin. A compiler for the weakly-typed, lazy language Ginger is developed using Java byte-code as its target code. This is used as the inspiration for an implementation of Aladin, a simple functional language which has two novel features: its primitives are designed to be written in any language, and evaluation is controlled by declaring the strictness of all functions. Efficient denotational and operational semantics are given for this machine and an implementation is devel- oped using these semantics. We then show that by using the advantages of Aladin (simplicity and strictness control) we can employ partial evaluation to achieve con- siderable speed-ups in the running times of Aladin programs

Integrating artificial neural networks and constraint logic programming.

by Vincent Wai-leuk Tam.Thesis (M.Phil.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 74-80).Chapter 1 --- Introduction and Summary --- p.1Chapter 1.1 --- The Task --- p.1Chapter 1.2 --- The Thesis --- p.2Chapter 1.2.1 --- Thesis --- p.2Chapter 1.2.2 --- Antithesis --- p.3Chapter 1.2.3 --- Synthesis --- p.5Chapter 1.3 --- Results --- p.6Chapter 1.4 --- Contributions --- p.6Chapter 1.5 --- Chapter Summaries --- p.7Chapter 1.5.1 --- Chapter 2: An ANN-Based Constraint-Solver --- p.8Chapter 1.5.2 --- Chapter 3: A Theoretical Framework of PROCLANN --- p.8Chapter 1.5.3 --- Chapter 4: The Prototype Implementation --- p.8Chapter 1.5.4 --- Chapter 5: Benchmarking --- p.9Chapter 1.5.5 --- Chapter 6: Conclusion --- p.9Chapter 2 --- An ANN-Based Constraint-Solver --- p.10Chapter 2.1 --- Notations --- p.11Chapter 2.2 --- Criteria for ANN-based Constraint-solver --- p.11Chapter 2.3 --- A Generic Neural Network: GENET --- p.13Chapter 2.3.1 --- Network Structure --- p.13Chapter 2.3.2 --- Network Convergence --- p.17Chapter 2.3.3 --- Energy Perspective --- p.22Chapter 2.4 --- Properties of GENET --- p.23Chapter 2.5 --- Incremental GENET --- p.27Chapter 3 --- A Theoretical Framework of PROCLANN --- p.29Chapter 3.1 --- Syntax and Declarative Semantics --- p.30Chapter 3.2 --- Unification in PROCLANN --- p.33Chapter 3.3 --- PROCLANN Computation Model --- p.38Chapter 3.4 --- Soundness and Weak Completeness of the PROCLANN Compu- tation Model --- p.40Chapter 3.5 --- Probabilistic Non-determinism --- p.46Chapter 4 --- The Prototype Implementation --- p.48Chapter 4.1 --- Prototype Design --- p.48Chapter 4.2 --- Implementation Issues --- p.52Chapter 5 --- Benchmarking --- p.58Chapter 5.1 --- N-Queens --- p.59Chapter 5.1.1 --- Benchmarking --- p.59Chapter 5.1.2 --- Analysis --- p.59Chapter 5.2 --- Graph-coloring --- p.63Chapter 5.2.1 --- Benchmarking --- p.63Chapter 5.2.2 --- Analysis --- p.64Chapter 5.3 --- Exceptionally Hard Problem --- p.66Chapter 5.3.1 --- Benchmarking --- p.67Chapter 5.3.2 --- Analysis --- p.67Chapter 6 --- Conclusion --- p.68Chapter 6.1 --- Contributions --- p.68Chapter 6.2 --- Limitations --- p.70Chapter 6.3 --- Future Work --- p.71Chapter 6.3.1 --- Parallel Implementation --- p.71Chapter 6.3.2 --- General Constraint Handling --- p.72Chapter 6.3.3 --- Other ANN Models --- p.73Chapter 6.3.4 --- Other Domains --- p.73Bibliography --- p.74Appendix A The Hard Graph-coloring Problems --- p.81Appendix B An Exceptionally Hard Problem (EHP) --- p.18