Foundations of logic programming
Foundations of logic programming
Learning strategies and automated knowledge acquisition: an overview
Computational models of learning
Multilayer feedforward networks are universal approximators
Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Parallel algorithms for shared-memory machines
Handbook of theoretical computer science (vol. A)
Training knowledge-based neural networks to recognize genes in DNA sequences
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
The Utility of Knowledge in Inductive Learning
Machine Learning
Symbolic knowledge and neural networks: insertion, refinement and extraction
Symbolic knowledge and neural networks: insertion, refinement and extraction
Reasoning about termination of pure Prolog programs
Information and Computation
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Theory refinement combining analytical and empirical methods
Artificial Intelligence
Knowledge-based artificial neural networks
Artificial Intelligence
Handbook of logic in artificial intelligence and logic programming (Vol. 4)
Neural network fundamentals with graphs, algorithms, and applications
Neural network fundamentals with graphs, algorithms, and applications
Extracting rules from neural networks by pruning and hidden-unit splitting
Neural Computation
Machine Learning
Neural Networks in Computer Intelligence
Neural Networks in Computer Intelligence
Nonmonotonic Logic: Context-Dependent Reasoning
Nonmonotonic Logic: Context-Dependent Reasoning
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Introduction to the Theory of Neural Computation
Introduction to the Theory of Neural Computation
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Towards a Hybrid Model of First-Order Theory Refinement
Hybrid Neural Systems, revised papers from a workshop
KI '94 Proceedings of the 18th Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Analog versus discrete neural networks
Neural Computation
Artificial nonmonotonic neural networks
Artificial Intelligence
Neural Networks and Structured Knowledge: Rule Extraction andApplications
Applied Intelligence
Multi-adjoint Logic Programming: A Neural Net Approach
ICLP '02 Proceedings of the 18th International Conference on Logic Programming
Towards a Theory Revision Approach for the Vertical Fragmentation of Object Oriented Databases
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Learning Logic Programs with Neural Networks
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
An Analysis-Revision Cycle to Evolve Requirements Specifications
Proceedings of the 16th IEEE international conference on Automated software engineering
Neural-symbolic intuitionistic reasoning
Design and application of hybrid intelligent systems
A connectionist computational model for epistemic and temporal reasoning
Neural Computation
Connectionist computations of intuitionistic reasoning
Theoretical Computer Science
Connectionist modal logic: Representing modalities in neural networks
Theoretical Computer Science
Connectionist Models for Formal Knowledge Adaptation
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Extracting reduced logic programs from artificial neural networks
Applied Intelligence
Integrating model verification and self-adaptation
Proceedings of the IEEE/ACM international conference on Automated software engineering
Representing, learning and extracting temporal knowledge from neural networks: a case study
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
From sensorimotor graphs to rules: an agent learns from a stream of experience
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
Probabilistic first-order theory revision from examples
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Learning and reasoning about norms using neural-symbolic systems
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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This paper presents the Connectionist Inductive Learning and LogicProgramming System (C-IL^2P). C-IL^2P is a new massively parallel computational model based on afeedforward Artificial Neural Network that integrates inductive learningfrom examples and background knowledge, with deductive learning from LogicProgramming. Starting with the background knowledge represented by apropositional logic program, a translation algorithm is applied generating aneural network that can be trained with examples. The results obtained withthis refined network can be explained by extracting a revised logic programfrom it. Moreover, the neural network computes the stable model of the logicprogram inserted in it as background knowledge, or learned with theexamples, thus functioning as a parallel system for Logic Programming. Wehave successfully applied C-IL^2P to two real-worldproblems of computational biology, specifically DNA sequence analyses.Comparisons with the results obtained by some of the main neural, symbolic,and hybrid inductive learning systems, using the same domain knowledge, showthe effectiveness of C-IL^2P.