Three models of connectionist rule processing are presented and discussed: Shastri and Ajjanagadde's SHRUTI system, which uses connectionist mechanisms to overcome the inefficiency of classical rule-based models; Sun's CONSYDERR model, which combines rule-processing and similarity-matching in a two-level architecture; and Sumida and Dyer's Parallel Distributed Semantic (PDS) Network, which adds generalisation and inferencing capabilities to a semantic network. The Sun and Sumida and Dyer models are illustrated with implementations and example runs. Aspects of rule-based reasoning not addressed by these models (rule learning, the encoding of rules wuthout hard-wired structure, and holistic rule application) are discussed in the context of a ...
AbstractThe paper presents a connectionist framework that is capable of representing and learning pr...
Connectionist representations are mappings between elements in the problem domain and vectors of act...
Due to the vast and rapid increase in the size of data, data mining has been an increasingly importa...
At present, the prevailing Connectionist methodology for representing rules is to implicitly embody ...
This thesis addresses the problem of efficiently representing large knowledge bases and performing a...
Davies Martin. Knowledge of Rules in Connectionist Networks. In: Intellectica. Revue de l'Associatio...
Rules encoded by traditional rule-based systems are brittle and inflexible because it is difficult t...
The performance of symbolic inference tasks has long been a challenge to connectionists.In this pape...
Although the connectionist approach has lead to elegant solutions to a number of problems in cogniti...
Abstract: "This report contains three papers on symbol processing in connectionist networks. The fir...
The ability to apply a rule to a set of known facts is a common task in both natural and artificial ...
We map structured connectionist models of knowledge representation and reasoning onto existing gener...
In Representations without Rules, Connectionism and the Syntactic Argument , Kenneth Aizawa argues ...
Fodor and Pylyshyn argued that connectionist models could not be used to exhibit and explain a pheno...
The performance of symbolic inference tasks has long been a challenge to connectionists. In this pap...
AbstractThe paper presents a connectionist framework that is capable of representing and learning pr...
Connectionist representations are mappings between elements in the problem domain and vectors of act...
Due to the vast and rapid increase in the size of data, data mining has been an increasingly importa...
At present, the prevailing Connectionist methodology for representing rules is to implicitly embody ...
This thesis addresses the problem of efficiently representing large knowledge bases and performing a...
Davies Martin. Knowledge of Rules in Connectionist Networks. In: Intellectica. Revue de l'Associatio...
Rules encoded by traditional rule-based systems are brittle and inflexible because it is difficult t...
The performance of symbolic inference tasks has long been a challenge to connectionists.In this pape...
Although the connectionist approach has lead to elegant solutions to a number of problems in cogniti...
Abstract: "This report contains three papers on symbol processing in connectionist networks. The fir...
The ability to apply a rule to a set of known facts is a common task in both natural and artificial ...
We map structured connectionist models of knowledge representation and reasoning onto existing gener...
In Representations without Rules, Connectionism and the Syntactic Argument , Kenneth Aizawa argues ...
Fodor and Pylyshyn argued that connectionist models could not be used to exhibit and explain a pheno...
The performance of symbolic inference tasks has long been a challenge to connectionists. In this pap...
AbstractThe paper presents a connectionist framework that is capable of representing and learning pr...
Connectionist representations are mappings between elements in the problem domain and vectors of act...
Due to the vast and rapid increase in the size of data, data mining has been an increasingly importa...