Connectionism and cognitive architecture: a critical analysis
Connections and symbols
Symmetric neural networks and propositional logic satisfiability
Neural Computation
CHCL - A Connectionist Infernce System
Proceedings of the International Workshop on Parallelization in Inference Systems
Unification as constraint satisfaction in structured connectionist networks
Neural Computation
Connectionist model generation: A first-order approach
Neurocomputing
A (somewhat) new solution to the variable binding problem
Neural Computation
Neural-Symbolic Cognitive Reasoning
Neural-Symbolic Cognitive Reasoning
A structured connectionist unification algorithm
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Holographic reduced representations
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
We show how to encode, retrieve and process complex structures equivalent to First-Order Logic (FOL) formulae, with Artificial Neural Networks (ANNs) designed for energy-minimization. The solution constitutes a binding mechanism that uses a neural Working Memory (WM) and a long-term synaptic memory (LTM) that can store both procedural and declarative FOL-like Knowledge-Base (KB). Complex structures stored in LTM are retrieved into the WM only upon need, where they are further processed. The power of our binding mechanism is demonstrated on unification problems: as neurons are dynamically allocated from a pool, most generally unified structures emerge at equilibrium. The network's size is O(n·k), where n is the size of the retrieved FOL structures and k is the size of the KB. The mechanism is fault-tolerant, as no fatal failures occur when random units fail. The paradigm can be used in a variety of applications, such as language processing, reasoning and planning.