Premium
A connectionist inference model for pattern‐directed knowledge representation
Author(s) -
Mitchell I,
Bavan A.S
Publication year - 2000
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/1468-0394.00132
Subject(s) - computer science , inference , theoretical computer science , connectionism , multigraph , backward chaining , artificial intelligence , representation (politics) , graph , forward chaining , chaining , set (abstract data type) , artificial neural network , expert system , inference engine , programming language , psychology , politics , political science , law , psychotherapist
In this paper we propose a connectionist model for variable binding. The model is topology dependent on the graph it builds based on the predicates available. The irregular connections between perceptron‐like assemblies facilitate forward and backward chaining. The model treats the symbolic data as a sequence and represents the training set as a partially connected network using basic set and graph theory to form the internal representation. Inference is achieved by opportunistic reasoning via the bidirectional connections. Consequently, such activity stabilizes to a multigraph. This multigraph is composed of isomorphic subgraphs which all represent solutions to the query made. Such a model has a number of advantages over other methods in that irrelevant connections are avoided by superimposing positionally dependent sub‐structures that are identical, variable binding can be encoded and multiple solutions can be extracted simultaneously. The model also has the ability to adapt its existing architecture when presented with new clauses and therefore add new relationships/rules to the model explicitly; this is done by some partial retraining of the network due to the superimposition properties.