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Dimensions of neuralsymbolic integration – a structural survey
 We Will Show Them: Essays in Honour of Dov Gabbay
"... Research on integrated neuralsymbolic systems has made significant progress in the recent past. In particular the understanding of ways to deal with symbolic knowledge within connectionist systems (also called artificial neural networks) has reached a critical mass which enables the community to ..."
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Cited by 25 (8 self)
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Research on integrated neuralsymbolic systems has made significant progress in the recent past. In particular the understanding of ways to deal with symbolic knowledge within connectionist systems (also called artificial neural networks) has reached a critical mass which enables the community to
d’Avila Garcez. Logical Modes of Attack in Argumentation Networks
"... This paper studies methodologically robust options for giving logical contents to nodes in abstract argumentation networks. It defines a variety of notions of attack in terms of the logical contents of the nodes in a network. General properties of logics are refined both in the object level and in t ..."
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This paper studies methodologically robust options for giving logical contents to nodes in abstract argumentation networks. It defines a variety of notions of attack in terms of the logical contents of the nodes in a network. General properties of logics are refined both in the object level and in the metalevel to suit the needs of the application. The networkbased system improves upon some of the attempts in the literature to define attacks in terms of defeasible proofs, the socalled rulebased systems. We also provide a number of examples and consider a rigorous case study, which indicate that our system does not suffer from anomalies. We define consequence relations based on a notion of defeat, consider rationality postulates, and prove that one such consequence relation is consistent. 1
Graphtheoretic fibring of logics
 Part II  Completeness preservation. Preprint, SQIG  IT and IST  TU Lisbon
, 2008
"... A graphtheoretic account of fibring of logics is developed, capitalizing on the interleaving characteristics of fibring at the linguistic, semantic and proof levels. Fibring of two signatures is seen as an mgraph where the nodes and the medges include the sorts and the constructors of the signatu ..."
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A graphtheoretic account of fibring of logics is developed, capitalizing on the interleaving characteristics of fibring at the linguistic, semantic and proof levels. Fibring of two signatures is seen as an mgraph where the nodes and the medges include the sorts and the constructors of the signatures at hand. Fibring of two models is an mgraph where the nodes and the medges are the values and the operations in the models, respectively. Fibring of two deductive systems is an mgraph whose nodes are language expressions and the medges represent the inference rules of the two original systems. The sobriety of the approach is confirmed by proving that all the fibring notions are universal constructions. This graphtheoretic view is general enough to accommodate very different fibrings of propositional based logics encompassing logics with nondeterministic semantics, logics with an algebraic semantics, logics with partial semantics, and substructural logics, among others. Soundness and weak completeness are proved to be preserved under very general conditions. Strong completeness is also shown to be preserved under tighter conditions. In this setting, the collapsing problem appearing in several combinations of logic systems can be avoided. 1
The Grand Challenges and Myths of NeuralSymbolic Computation ⋆
"... Abstract. The construction of computational cognitive models integrating the connectionist and symbolic paradigms of artificial intelligence is a standing research issue in the field. The combination of logicbased inference and connectionist learning systems may lead to the construction of semantic ..."
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Abstract. The construction of computational cognitive models integrating the connectionist and symbolic paradigms of artificial intelligence is a standing research issue in the field. The combination of logicbased inference and connectionist learning systems may lead to the construction of semantically sound computational cognitive models in artificial intelligence, computer and cognitive sciences. Over the last decades, results regarding the computation and learning of classical reasoning within neural networks have been promising. Nonetheless, there still remains much do be done. Artificial intelligence, cognitive and computer science are strongly based on several nonclassical reasoning formalisms, methodologies and logics. In knowledge representation, distributed systems, hardware design, theorem proving, systems specification and verification classical and nonclassical logics have had a great impact on theory and realworld applications. Several challenges for neuralsymbolic computation are pointed out, in particular for classical and nonclassical computation in connectionist systems. We also analyse myths about neuralsymbolic computation and shed new light on them considering recent research advances.
Embedding Normative Reasoning into Neural Symbolic Systems
"... Normative systems are dynamic systems because their rules can change over time. Considering this problem, we propose a neuralsymbolic approach to provide agents the instruments to reason about and learn norms in a dynamic environment. We propose a variant of d’Avila Garcez et al. Connectionist Induc ..."
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Normative systems are dynamic systems because their rules can change over time. Considering this problem, we propose a neuralsymbolic approach to provide agents the instruments to reason about and learn norms in a dynamic environment. We propose a variant of d’Avila Garcez et al. Connectionist Inductive Learning and Logic Programming(CILP) System to embed Input/Output logic normative rules into a feedforward neural network. The resulting system called NormativeCILP(NCILP) shows how neural networks can cope with some of the underpinnings of normative reasoning: permissions, dilemmas, exceptions and contrary to duty problems. We have applied our approach in a simplified RoboCup environment, using the NCILP simulator that we have developed. In the concluding part of the paper, we provide some of the results obtained in the experiments. 1
Graphtheoretic Fibring of Logics Part I Completeness
, 2008
"... It is well known that interleaving presentations is at the heart of fibring, as shown by the mechanism of fibring languages and deduction systems. This idea is abstractly introduced herein at the level of the general notion of mgraph (that is, a graph where each edge can have a finite sequence of n ..."
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It is well known that interleaving presentations is at the heart of fibring, as shown by the mechanism of fibring languages and deduction systems. This idea is abstractly introduced herein at the level of the general notion of mgraph (that is, a graph where each edge can have a finite sequence of nodes as source). Signatures, interpretation structures and deduction systems are seen as mgraphs. After defining a category freely generated by a mgraph, formulas and expressions in general can be seen as morphisms. Moreover, derivations involving rule instantiation are also morphisms. Soundness and completeness results are proved. As a consequence of the generality of the approach our results apply to very different logics encompassing, among others, substructural logics as well as logics with nondeterministic semantics and subsume all logics endowed with an algebraic semantics. 1
Extracting Argumentative Dialogues from the Neural Network that Computes the Dungean Argumentation Semantics
"... Argumentation is a leading principle both foundationally and functionally for agentoriented computing where reasoning accompanied by communication plays an essential role in agent interaction. We constructed a simple but versatile neural network for neural network argumentation, so that it can deci ..."
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Argumentation is a leading principle both foundationally and functionally for agentoriented computing where reasoning accompanied by communication plays an essential role in agent interaction. We constructed a simple but versatile neural network for neural network argumentation, so that it can decide which argumentation semantics (admissible, stable, semistable, preferred, complete, and grounded semantics) a given set of arguments falls into, and compute argumentation semantics via checking. In this paper, we are concerned with the opposite direction from neural network computation to symbolic argumentation/dialogue. We deal with the question how various argumentation semantics can have dialectical proof theories, and describe a possible answer to it by extracting or generating symbolic dialogues from the neural network computation under various argumentation semantics. 1
unknown title
, 2013
"... A neural cognitive model of argumentation with application to legal inference and decision making ..."
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A neural cognitive model of argumentation with application to legal inference and decision making
Chapter 3 Abstract Argumentation and Values
"... Abstract argumentation frameworks, as described in Chapter 2, are directed towards determining whether a claim that some statement is true can be coherently maintained in the context of a set of conflicting arguments. For example, if we use preferred semantics, that an argument is a member of all p ..."
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Abstract argumentation frameworks, as described in Chapter 2, are directed towards determining whether a claim that some statement is true can be coherently maintained in the context of a set of conflicting arguments. For example, if we use preferred semantics, that an argument is a member of all preferred extensions es
DOI: 10.2298/CSIS100915027F Problem Solving by soaking the concept network
"... Abstract. Because of the complexity and fuzziness of the real world, it’s hard to build a dense knowledge system and reason in it with traditional methods. But man can deal with such tasks freely. Inspired by cognition and linguistics, a solution is advanced for reasoning dense knowledge in this pap ..."
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Abstract. Because of the complexity and fuzziness of the real world, it’s hard to build a dense knowledge system and reason in it with traditional methods. But man can deal with such tasks freely. Inspired by cognition and linguistics, a solution is advanced for reasoning dense knowledge in this paper. Objects and concepts are organized in the form of concept graph. Soaking the nodes in the graph until the result is represented in the graph the final graph can be the explanation of the scenario. With the naïve algorithm, monotonic scenario reasoning problem can be solved in dense knowledge environment.