Results 1  10
of
16
On the Integration of Connectionist and LogicBased Systems
, 2004
"... We discuss the computation by neural networks of semantic operators TP determined by propositional logic programs P. We revisit and clarify the foundations of the relevant notions employed in approximating both TP and its fixed points when P is a firstorder program. ..."
Abstract

Cited by 17 (8 self)
 Add to MetaCart
We discuss the computation by neural networks of semantic operators TP determined by propositional logic programs P. We revisit and clarify the foundations of the relevant notions employed in approximating both TP and its fixed points when P is a firstorder program.
Some aspects of the integration of connectionist and logicbased systems
 In Proceedings of the Third International Conference on Information, pages 297–300, International Information Institute
, 2004
"... We discuss the computation by neural networks of semantic operators TP determined by propositional logic programs P over quite general manyvalued logics T. We revisit and clarify the foundations of the relevant notions employed in approximating both TP and ..."
Abstract

Cited by 9 (3 self)
 Add to MetaCart
We discuss the computation by neural networks of semantic operators TP determined by propositional logic programs P over quite general manyvalued logics T. We revisit and clarify the foundations of the relevant notions employed in approximating both TP and
MFCSIT 2004 Preliminary Version On the Integration of Connectionist and LogicBased Systems
"... It is a longstanding and important problem to integrate logicbased systems and connectionist systems. In brief, this problem is concerned with how each of these two paradigms interacts with the other and how each complements the other: how one may give a logical interpretation of neural networks, ..."
Abstract
 Add to MetaCart
It is a longstanding and important problem to integrate logicbased systems and connectionist systems. In brief, this problem is concerned with how each of these two paradigms interacts with the other and how each complements the other: how one may give a logical interpretation of neural networks
The Connectionist Inductive Learning and Logic Programming System
, 1999
"... This paper presents the Connectionist Inductive Learning and Logic Programming System (CIL²P). CIL²P is a new massively parallel computational model based on a feedforward Artificial Neural Network that integrates inductive learning from examples and background knowledge, with deductive learning ..."
Abstract

Cited by 28 (8 self)
 Add to MetaCart
This paper presents the Connectionist Inductive Learning and Logic Programming System (CIL²P). CIL²P is a new massively parallel computational model based on a feedforward Artificial Neural Network that integrates inductive learning from examples and background knowledge, with deductive learning
Heterogeneous Knowledge Representation: integrating connectionist and symbolic computations
, 1995
"... Heterogeneous knowledge representation allows to combine several knowledge representtechniques. For instance connectionist and symbolic systems are two different computational paradigms and knowledge representation tools. Unfortunately, the integration of different paradigms and knowledge repres ..."
Abstract
 Add to MetaCart
representations is not easy and very often is informal. In this paper, we propose a formal approachtointegrate these two paradigms where as symbolic system we consider a (logic) rule based system. The integration is operated at language level between neural networks and rule languages. The formal model
Connectionist Model generation: A FirstOrder Approach
, 2007
"... Knowledge based artificial neural networks have been applied quite successfully to propositional knowledge representation and reasoning tasks. However, as soon as these tasks are extended to structured objects and structuresensitive processes as expressed e.g., by means of firstorder predicate log ..."
Abstract

Cited by 20 (5 self)
 Add to MetaCart
logic, it is not obvious at all what neural symbolic systems would look like such that they are truly connectionist, are able to learn, and allow for a declarative reading and logical reasoning at the same time. The core method aims at such an integration. It is a method for connectionist model
Connectionist Model Generation: A FirstOrder Approach
"... Knowledge based artificial neural networks have been applied quite successfully to propositional knowledge representation and reasoning tasks. However, as soon as these tasks are extended to structured objects and structuresensitive processes as expressed e.g., by means of firstorder predicate log ..."
Abstract
 Add to MetaCart
logic, it is not obvious at all what neural symbolic systems would look like such that they are truly connectionist, are able to learn, and allow for a declarative reading and logical reasoning at the same time. The core method aims at such an integration. It is a method for connectionist model
Connectionist Models: Not Just a Notational Variant, Not a Panacea
"... Connectionist models inherently include features and exhibit behaviors which are difficult to achieve with traditional logicbased models. Among the more important of such characteristics are 1) the ability to compute nearest match rather than requiring unification or exact match; 2) learning; 3) fa ..."
Abstract
 Add to MetaCart
Connectionist models inherently include features and exhibit behaviors which are difficult to achieve with traditional logicbased models. Among the more important of such characteristics are 1) the ability to compute nearest match rather than requiring unification or exact match; 2) learning; 3
The integration of connectionism and firstorder knowledge representation and reasoning as a challenge for artificial intelligence
 In Proceedings of the Third International Conference on Information
, 2006
"... Intelligent systems based on firstorder logic on the one hand, and on artificial neural networks (also called connectionist systems) on the other, differ substantially. It would be very desirable to combine the robust neural networking machinery with symbolic knowledge representation and reasoning ..."
Abstract

Cited by 12 (8 self)
 Add to MetaCart
Intelligent systems based on firstorder logic on the one hand, and on artificial neural networks (also called connectionist systems) on the other, differ substantially. It would be very desirable to combine the robust neural networking machinery with symbolic knowledge representation and reasoning
Regular Issue Papers Reinforcement Structureparameter Learning for NeuralNetworkBased Fuzzy Logic Control Systems
"... Abstruct This paper proposes a reinforcement neuralnetworkbased fuzzy logic control system (RNNFLCS) for solving various reinforcement learning problems. The proposed RNNFLCS is constructed by integrating two neuralnetworkbased fuzzy logic controllers (NNFLC’s), each of which is a connection ..."
Abstract
 Add to MetaCart
Abstruct This paper proposes a reinforcement neuralnetworkbased fuzzy logic control system (RNNFLCS) for solving various reinforcement learning problems. The proposed RNNFLCS is constructed by integrating two neuralnetworkbased fuzzy logic controllers (NNFLC’s), each of which is a
Results 1  10
of
16