The performance of symbolic inference tasks has long been a challenge to connectionists.In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modelling.</p
Connectionism is the theory that sees brain in terms of neural or parallel distributed processing ne...
Connectionism (also known as parallel distributed processing) has generated a great deal of interest...
It is generally acknowledged that tremendous computational activity underlies some of the most commo...
The performance of symbolic inference tasks has long been a challenge to connectionists.In this pape...
The performance of symbolic inference tasks has long been a challenge to connectionists. In this pap...
We present a theory of how relational inference and generalization can be accomplished within a cogn...
Classical symbolic computational models of cognition are at variance with the empirical findings in ...
The ability to apply a rule to a set of known facts is a common task in both natural and artificial ...
Although the connectionist approach has lead to elegant solutions to a number of problems in cogniti...
In this paper, we try to combine the possibility of symbolic deductive reasoning with the learning c...
Modeling higher order cognitive processes like human decision making come in three representational ...
This thesis addresses the problem of efficiently representing large knowledge bases and performing a...
Three models of connectionist rule processing are presented and discussed: Shastri and Ajjanagadde's...
Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficienc...
AbstractThe paper presents a connectionist framework that is capable of representing and learning pr...
Connectionism is the theory that sees brain in terms of neural or parallel distributed processing ne...
Connectionism (also known as parallel distributed processing) has generated a great deal of interest...
It is generally acknowledged that tremendous computational activity underlies some of the most commo...
The performance of symbolic inference tasks has long been a challenge to connectionists.In this pape...
The performance of symbolic inference tasks has long been a challenge to connectionists. In this pap...
We present a theory of how relational inference and generalization can be accomplished within a cogn...
Classical symbolic computational models of cognition are at variance with the empirical findings in ...
The ability to apply a rule to a set of known facts is a common task in both natural and artificial ...
Although the connectionist approach has lead to elegant solutions to a number of problems in cogniti...
In this paper, we try to combine the possibility of symbolic deductive reasoning with the learning c...
Modeling higher order cognitive processes like human decision making come in three representational ...
This thesis addresses the problem of efficiently representing large knowledge bases and performing a...
Three models of connectionist rule processing are presented and discussed: Shastri and Ajjanagadde's...
Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficienc...
AbstractThe paper presents a connectionist framework that is capable of representing and learning pr...
Connectionism is the theory that sees brain in terms of neural or parallel distributed processing ne...
Connectionism (also known as parallel distributed processing) has generated a great deal of interest...
It is generally acknowledged that tremendous computational activity underlies some of the most commo...