Name
Abstract | ||
|---|---|---|
This paper describes a connectionist inference architecture which performs standard symbolic inference on a subclass of first-order predicate calculus. This work first involves constructing efficient connectionist mechanisms to represent basic symbol components, dynamic bindings and basic symbolic inference procedures, and devising a set of algorithms which automatically translates input descriptions to localist neural networks. These connectionist mechanisms are built by taking an existing phase-locking mechanism and extending it further to obtain desirable features to represent and manipulate basic symbol structures. The existing phase-locking mechanism represents dynamic bindings very efficiently using temporal synchronous activity between neuron elements but has fundamental limitations in supporting standard symbolic inference. The extension addresses these limitations. The extended system encodes a significant subset of a Horn clause language in a connectionist style. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1111/1468-0394.00124 | EXPERT SYSTEMS |
Keywords | Field | DocType |
connectionist symbol processing,connectionist knowledge representation,localist network,dynamic bindings | DUAL (cognitive architecture),Horn clause,Computer science,Symbol,Inference,First-order logic,Artificial intelligence,Artificial neural network,Connectionism,Machine learning,Encoding (memory) | Journal |
Volume | Issue | ISSN |
17 | 1 | 0266-4720 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
3 | ||