Compiled Labelled Deductive Systems (CLDS) provide a uniform logical framework where families of ... more Compiled Labelled Deductive Systems (CLDS) provide a uniform logical framework where families of different logics can be given a uniform proof system and semantics. A variety of applications of this framework have been proposed so far ranging from extensions of classical logics (e.g. normal modal logics and multi-modal logics) to non-classical logics such as resource and substructural loogics. Labelled natural
This paper proposes a Compiled Labelled Deductive System, called ACCLDS, for reasoning about role... more This paper proposes a Compiled Labelled Deductive System, called ACCLDS, for reasoning about role-based access control in distributed systems, which builds upon Massacci's tableau system for role-based access control. The ACCLDS system overcomes some of the limitations of Massaci's approach by combining its multi-modal propositional language with a labelling algebra that allows reason- ing explicitly about dynamic properties of the
This paper proposes a multi-agent, multi-threaded architec- ture for a distributed inference syst... more This paper proposes a multi-agent, multi-threaded architec- ture for a distributed inference system for a dynamic group of agents. The system can opportunistically make use of new agents that join the group, whilst a proof is in progress. It can also recover if an agent leaves. Final proofs only make use of the knowledge bases of agents that are in
This paper describes a proof theoretic and semantic approach in which logics belonging to dierent... more This paper describes a proof theoretic and semantic approach in which logics belonging to dierent families can be given common notions of derivability relation and semantic entailment. This approach builds upon Gabbay's methodology of Labelled Deductive Systems (LDS) and it is called the compila- tion approach for labelled deductive systems (CLDS). Two dierent logics are here considered, (i) the modal
this paper we shall, instead, use a fragmentof this family of logics as a case-study to illustrat... more this paper we shall, instead, use a fragmentof this family of logics as a case-study to illustrate a set of methodsoriginating in the LDS program. In particular, we aim to illuminate thefollowing aspects:(I) By virtue of the extra power of labels and labelling algebras, traditionalproof systems can be transformed so as to become applicable over amuch wider territory whilst retaining
Neural-symbolic integration concerns the integration of sym- bolic and connectionist systems. Dis... more Neural-symbolic integration concerns the integration of sym- bolic and connectionist systems. Distributed knowledge,rep- resentation is traditionally seen under a purely symbolic per- spective. In this paper, we show how neural networks can represent symbolic distributed knowledge, acting as multi- agent systems with learning capability (a key feature of neu- ral networks). We then apply our approach to the well-known
Page 1. Artur S. d'Avila Garcez Krysia B. Broda Dov M. Gabbay Neural... more Page 1. Artur S. d'Avila Garcez Krysia B. Broda Dov M. Gabbay Neural-Symbolic Learning Systems Foundations and Applications Springer Page 2. Page 3. Perspectives in Neural Computing Springer London Berlin Heidelberg ...
This book is about the integration of neural networks and symbolic rules. While symbolic artifici... more This book is about the integration of neural networks and symbolic rules. While symbolic artificial intelligence assumes that the mind is the focus of intelligence, and thus intelligent behaviour emerges from complex symbol processing mechanisms, connectionist artificial intelligence admits that intelligence lies in the brain, and therefore tries to model it by simulating its electrochemical neuronal structures. Clearly, such structures
In this chapter we introduce some basic concepts used throughout this book. We also present basic... more In this chapter we introduce some basic concepts used throughout this book. We also present basic definitions and results on Inductive Learning and Artificial Neural Networks. In order that the book be reasonably self-contained, we include an introductory section on Logic Programming and Nonmonotonic Reasoning, together with some results in Belief Revision of logic program theories. References to introductory work
Logic Programming and Non-monotonic Reasoning, 2009
Several learning systems based on Inverse Entailment (IE) have been proposed, some that compute s... more Several learning systems based on Inverse Entailment (IE) have been proposed, some that compute single clause hypotheses, exemplified by Progol, and others that produce multiple clauses in response to a single seed example. A common denominator of these systems is a restricted hypothesis search space, within which each clause must individually explain some example E, or some member of an
Logic Journal of The Igpl / Bulletin of The Igpl, 1999
In this paper a uniform methodology to perform Natural Deduction over the family of linear, relev... more In this paper a uniform methodology to perform Natural Deduction over the family of linear, relevance and intuitionistic logics is proposed The methodology follows the Labelled Deductive Systems (LDS) discipline, where the deductive process manipulates declarative units { formulas labelled according to a labelling algebra In the system de - scribed here, labels are either ground terms or variables
These are the proceedings of the tenth International Workshop on Computational Logic in Multi-Age... more These are the proceedings of the tenth International Workshop on Computational Logic in Multi-Agent Systems (CLIMA-X), held from 9-10th September in Hamburg, colocated with MATES and MOCA.
Abductive inference has many known applications in multiagent systems. However, most abductive fr... more Abductive inference has many known applications in multiagent systems. However, most abductive frameworks rely on a centrally executed proof procedure whereas many of the application problems are distributed by nature. Confidentiality and communication overhead concerns often preclude transmitting all the knowledge required for centralised reasoning. We present in this paper a novel multi-agent abductive reasoning framework underpinned by a flexible and extensible distributed proof procedure that permits collaborative abductive reasoning with constraints between agents over decentralised knowledge.
In this chapter, we apply the C-IL2P system to two problems of DNA classification, which have bec... more In this chapter, we apply the C-IL2P system to two problems of DNA classification, which have become benchmark data sets for testing the accuracy of machine learning systems. We compare the results obtained by different neural, symbolic and hybrid inductive learning systems. For example, the test-set performance of C-IL2P is at least as good as those of KBANN and Backpropagation,
Compiled Labelled Deductive Systems (CLDS) provide a uniform logical framework where families of ... more Compiled Labelled Deductive Systems (CLDS) provide a uniform logical framework where families of different logics can be given a uniform proof system and semantics. A variety of applications of this framework have been proposed so far ranging from extensions of classical logics (e.g. normal modal logics and multi-modal logics) to non-classical logics such as resource and substructural loogics. Labelled natural
This paper proposes a Compiled Labelled Deductive System, called ACCLDS, for reasoning about role... more This paper proposes a Compiled Labelled Deductive System, called ACCLDS, for reasoning about role-based access control in distributed systems, which builds upon Massacci's tableau system for role-based access control. The ACCLDS system overcomes some of the limitations of Massaci's approach by combining its multi-modal propositional language with a labelling algebra that allows reason- ing explicitly about dynamic properties of the
This paper proposes a multi-agent, multi-threaded architec- ture for a distributed inference syst... more This paper proposes a multi-agent, multi-threaded architec- ture for a distributed inference system for a dynamic group of agents. The system can opportunistically make use of new agents that join the group, whilst a proof is in progress. It can also recover if an agent leaves. Final proofs only make use of the knowledge bases of agents that are in
This paper describes a proof theoretic and semantic approach in which logics belonging to dierent... more This paper describes a proof theoretic and semantic approach in which logics belonging to dierent families can be given common notions of derivability relation and semantic entailment. This approach builds upon Gabbay's methodology of Labelled Deductive Systems (LDS) and it is called the compila- tion approach for labelled deductive systems (CLDS). Two dierent logics are here considered, (i) the modal
this paper we shall, instead, use a fragmentof this family of logics as a case-study to illustrat... more this paper we shall, instead, use a fragmentof this family of logics as a case-study to illustrate a set of methodsoriginating in the LDS program. In particular, we aim to illuminate thefollowing aspects:(I) By virtue of the extra power of labels and labelling algebras, traditionalproof systems can be transformed so as to become applicable over amuch wider territory whilst retaining
Neural-symbolic integration concerns the integration of sym- bolic and connectionist systems. Dis... more Neural-symbolic integration concerns the integration of sym- bolic and connectionist systems. Distributed knowledge,rep- resentation is traditionally seen under a purely symbolic per- spective. In this paper, we show how neural networks can represent symbolic distributed knowledge, acting as multi- agent systems with learning capability (a key feature of neu- ral networks). We then apply our approach to the well-known
Page 1. Artur S. d'Avila Garcez Krysia B. Broda Dov M. Gabbay Neural... more Page 1. Artur S. d'Avila Garcez Krysia B. Broda Dov M. Gabbay Neural-Symbolic Learning Systems Foundations and Applications Springer Page 2. Page 3. Perspectives in Neural Computing Springer London Berlin Heidelberg ...
This book is about the integration of neural networks and symbolic rules. While symbolic artifici... more This book is about the integration of neural networks and symbolic rules. While symbolic artificial intelligence assumes that the mind is the focus of intelligence, and thus intelligent behaviour emerges from complex symbol processing mechanisms, connectionist artificial intelligence admits that intelligence lies in the brain, and therefore tries to model it by simulating its electrochemical neuronal structures. Clearly, such structures
In this chapter we introduce some basic concepts used throughout this book. We also present basic... more In this chapter we introduce some basic concepts used throughout this book. We also present basic definitions and results on Inductive Learning and Artificial Neural Networks. In order that the book be reasonably self-contained, we include an introductory section on Logic Programming and Nonmonotonic Reasoning, together with some results in Belief Revision of logic program theories. References to introductory work
Logic Programming and Non-monotonic Reasoning, 2009
Several learning systems based on Inverse Entailment (IE) have been proposed, some that compute s... more Several learning systems based on Inverse Entailment (IE) have been proposed, some that compute single clause hypotheses, exemplified by Progol, and others that produce multiple clauses in response to a single seed example. A common denominator of these systems is a restricted hypothesis search space, within which each clause must individually explain some example E, or some member of an
Logic Journal of The Igpl / Bulletin of The Igpl, 1999
In this paper a uniform methodology to perform Natural Deduction over the family of linear, relev... more In this paper a uniform methodology to perform Natural Deduction over the family of linear, relevance and intuitionistic logics is proposed The methodology follows the Labelled Deductive Systems (LDS) discipline, where the deductive process manipulates declarative units { formulas labelled according to a labelling algebra In the system de - scribed here, labels are either ground terms or variables
These are the proceedings of the tenth International Workshop on Computational Logic in Multi-Age... more These are the proceedings of the tenth International Workshop on Computational Logic in Multi-Agent Systems (CLIMA-X), held from 9-10th September in Hamburg, colocated with MATES and MOCA.
Abductive inference has many known applications in multiagent systems. However, most abductive fr... more Abductive inference has many known applications in multiagent systems. However, most abductive frameworks rely on a centrally executed proof procedure whereas many of the application problems are distributed by nature. Confidentiality and communication overhead concerns often preclude transmitting all the knowledge required for centralised reasoning. We present in this paper a novel multi-agent abductive reasoning framework underpinned by a flexible and extensible distributed proof procedure that permits collaborative abductive reasoning with constraints between agents over decentralised knowledge.
In this chapter, we apply the C-IL2P system to two problems of DNA classification, which have bec... more In this chapter, we apply the C-IL2P system to two problems of DNA classification, which have become benchmark data sets for testing the accuracy of machine learning systems. We compare the results obtained by different neural, symbolic and hybrid inductive learning systems. For example, the test-set performance of C-IL2P is at least as good as those of KBANN and Backpropagation,
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