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On the Integration of Connectionist and Logic-Based Systems

by Anthony Karel Seda , 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 first-order 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 first-order program.

Some aspects of the integration of connectionist and logic-based systems

by Anthony Karel Seda - 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 many-valued 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 many-valued 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 Logic-Based Systems

by Anthony Karel Seda
"... It is a long-standing and important problem to integrate logic-based 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, ..."
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It is a long-standing and important problem to integrate logic-based 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

by Artur S. Avila Garcez, et al. , 1999
"... This paper presents the Connectionist Inductive Learning and Logic Programming System (C-IL²P). C-IL²P 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 (C-IL²P). C-IL²P 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

by Danilo Montesi , 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 ..."
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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 First-Order Approach

by Sebastian Bader, Pascal Hitzler, Steffen Hölldobler , 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 structure-sensitive processes as expressed e.g., by means of first-order 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 First-Order Approach

by Core Scholar, Sebastian Bader, Pascal Hitzler, Steffen Holldobler, Sebastian Bader, Pascal Hitzler
"... 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 structure-sensitive processes as expressed e.g., by means of first-order predicate log ..."
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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

by David L. Waltz
"... Connectionist models inherently include features and exhibit behaviors which are difficult to achieve with traditional logic-based 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 ..."
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Connectionist models inherently include features and exhibit behaviors which are difficult to achieve with traditional logic-based 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 first-order knowledge representation and reasoning as a challenge for artificial intelligence

by Sebastian Bader, Pascal Hitzler, Steffen Hölldobler - In Proceedings of the Third International Conference on Information , 2006
"... Intelligent systems based on first-order 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 first-order 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 Neural-Network-Based Fuzzy Logic Control Systems

by Chin-teng Lin, C. S George Lee
"... Abstruct- This paper proposes a reinforcement neural-network-based fuzzy logic control system (RNN-FLCS) for solving various reinforcement learning problems. The proposed RNN-FLCS is constructed by integrating two neural-network-based fuzzy logic controllers (NN-FLC’s), each of which is a connection ..."
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Abstruct- This paper proposes a reinforcement neural-network-based fuzzy logic control system (RNN-FLCS) for solving various reinforcement learning problems. The proposed RNN-FLCS is constructed by integrating two neural-network-based fuzzy logic controllers (NN-FLC’s), each of which is a
Results 1 - 10 of 16