Formalizing nonmonotonic reasoning systems
Artificial Intelligence
The mathematics of inheritance systems
The mathematics of inheritance systems
Communications of the ACM
ACM Transactions on Mathematical Software (TOMS)
Default reasoning in semantic networks: a formalization of recognition and inheritance
Artificial Intelligence
General theory of cumulative inference
Proceedings of the 2nd international workshop on Non-monotonic reasoning
A skeptical theory of inheritance in nonmonotonic semantic networks
Artificial Intelligence
Recursive distributed representations
Artificial Intelligence - On connectionist symbol processing
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Symmetric neural networks and propositional logic satisfiability
Neural Computation
Cumulative default logic: in defense of nonmonotonic inference rules
Artificial Intelligence
Learning finite machines with self-clustering recurrent networks
Neural Computation
Knowledge-based artificial neural networks
Artificial Intelligence
Alternative approaches to default logic
Artificial Intelligence
Solving systems of nonlinear equations using the nonzero value of the topological degree
ACM Transactions on Mathematical Software (TOMS)
Handling inheritance in a system integrating logic in objects
Data & Knowledge Engineering
Effective backpropagation training with variable stepsize
Neural Networks
A training method for discrete multilayer neural networks
MANNA '95 Proceedings of the first international conference on Mathematics of neural networks : models, algorithms and applications: models, algorithms and applications
Iterative solution of nonlinear equations in several variables
Iterative solution of nonlinear equations in several variables
A class of gradient unconstrained minimization algorithms with adaptive stepsize
Journal of Computational and Applied Mathematics
Approximating the Semantics of Logic Programs by Recurrent Neural Networks
Applied Intelligence
The Connectionist Inductive Learning and Logic Programming System
Applied Intelligence
Machine Learning
CHCL - A Connectionist Infernce System
Proceedings of the International Workshop on Parallelization in Inference Systems
Connectionist Inference Systems
Proceedings of the International Workshop on Parallelization in Inference Systems
On Managing Nonmontonic Transitive Relationships
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Parallel evolutionary training algorithms for “hardware-friendly“ neural networks
Natural Computing: an international journal
Integrating Hybrid Rule-Based with Case-Based Reasoning
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Symbolic adaptive neuro-fuzzy inference for data mining of heterogenous data
Intelligent Data Analysis
International Journal of Knowledge Engineering and Soft Data Paradigms
A method for improving the accuracy of data mining classification algorithms
Computers and Operations Research
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
| Hi-index | 0.01 |
In this paper, we introduce Artificial Nonmonotonic Neural Networks (ANNNs), a kind of hybrid learning systems that are capable of nonmonotonic reasoning. Nonmonotonic reasoning plays an important role in the development of artificial intelligent systems that try to mimic common sense reasoning, as exhibited by humans. On the other hand, a hybrid learning system provides an explanation capability to trained Neural Networks through acquiring symbolic knowledge of a domain, refining it using a set of classified examples along with Connectionist learning techniques and, finally, extracting comprehensible symbolic information. Artificial Nonmonotonic Neural Networks acquire knowledge represented by a multiple inheritance scheme with exceptions, such as nonmonotonic inheritance networks, and then can extract the refined knowledge in the same scheme. The key idea is to use a special cell operation during training in order to preserve the symbolic meaning of the initial inheritance scheme. Methods for knowledge initialization, knowledge refinement and knowledge extraction are introduced. We, also, prove that these methods address perfectly the constraints imposed by nonmonotonicity. Finally, performance of ANNNs is compared to other well-known hybrid systems, through extensive empirical tests. . 2001 Elsevier Science B.V. All rights reserved.