Roughly speaking, adequatness is the property of a theorem proving method to solve simpler problems faster than more difficult ones. Au-tomated inferencing methods are often not ade-quate as they require thousands of steps to solve problems which humans solve effortlessly, sponta-neously, and with remarkable efficiency. L. Shastri and V. Ajjanagadde- who call this gap the ar-tificial intelligence paradox- suggest that their connectionist inference system is a first step to-ward bridging this gap. In this paper we show that their inference method is equivalent to rea-soning by reductions in the well-known connec-tion method. In particular, we extend a reductio
basic assumption of much of AI, that mental processes are best viewed as algorithmic symbol manipula...
A framework for inductive inference in logic is presented: a Model Inference Problem is defined, and...
It is generally acknowledged that tremendous computational activity underlies some of the most commo...
Although the connectionist approach has lead to elegant solutions to a number of problems in cogniti...
The inference capabilities of humans suggest that they might be using algorithms with high degrees o...
Cognitive scientists used to deem reasoning either as a higher cognitive process based on the manipu...
The performance of symbolic inference tasks has long been a challenge to connectionists.In this pape...
The ability to apply a rule to a set of known facts is a common task in both natural and artificial ...
Contains fulltext : 72913.pdf (publisher's version ) (Closed access)Leech, Maresch...
A consequence relation (CR) relates sets of beliefs to the appropriate conclusions that might be ded...
This paper aims to offer a new view of the role of connectionist models in the study of human cognit...
Classical symbolic computational models of cognition are at variance with the empirical findings in ...
Although connectionism is advocated by its proponents as an alternative to the classical computation...
AbstractThere is a lot of excitement in the field of artificial intelligence (AI) at the moment cent...
Traditional epistemology has it that the pursuit of knowledge is predicated on two inter-connected g...
basic assumption of much of AI, that mental processes are best viewed as algorithmic symbol manipula...
A framework for inductive inference in logic is presented: a Model Inference Problem is defined, and...
It is generally acknowledged that tremendous computational activity underlies some of the most commo...
Although the connectionist approach has lead to elegant solutions to a number of problems in cogniti...
The inference capabilities of humans suggest that they might be using algorithms with high degrees o...
Cognitive scientists used to deem reasoning either as a higher cognitive process based on the manipu...
The performance of symbolic inference tasks has long been a challenge to connectionists.In this pape...
The ability to apply a rule to a set of known facts is a common task in both natural and artificial ...
Contains fulltext : 72913.pdf (publisher's version ) (Closed access)Leech, Maresch...
A consequence relation (CR) relates sets of beliefs to the appropriate conclusions that might be ded...
This paper aims to offer a new view of the role of connectionist models in the study of human cognit...
Classical symbolic computational models of cognition are at variance with the empirical findings in ...
Although connectionism is advocated by its proponents as an alternative to the classical computation...
AbstractThere is a lot of excitement in the field of artificial intelligence (AI) at the moment cent...
Traditional epistemology has it that the pursuit of knowledge is predicated on two inter-connected g...
basic assumption of much of AI, that mental processes are best viewed as algorithmic symbol manipula...
A framework for inductive inference in logic is presented: a Model Inference Problem is defined, and...
It is generally acknowledged that tremendous computational activity underlies some of the most commo...