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Marco Schaerf - One of the best experts on this subject based on the ideXlab platform.
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Space Efficiency of Propositional Knowledge Representation Formalisms
Journal of Artificial Intelligence Research, 2000Co-Authors: Marco Cadoli, Francesco M. Donini, Paolo Liberatore, Marco SchaerfAbstract:We investigate the space efficiency of a Propositional Knowledge Representation (PKR) formalism. Intuitively, the space efficiency of a formalism F in representing a certain piece of Knowledge A, is the size of the shortest formula of F that represents A. In this paper we assume that Knowledge is either a set of propositional interpretations (models) or a set of propositional formulae (theorems). We provide a formal way of talking about the relative ability of PKR Formalisms to compactly represent a set of models or a set of theorems. We introduce two new compactness measures, the corresponding classes, and show that the relative space efficiency of a PKR formalism in representing models/theorems is directly related to such classes. In particular, we consider Formalisms for nonmonotonic reasoning, such as circumscription and default logic, as well as belief revision operators and the stable model semantics for logic programs with negation. One interesting result is that Formalisms with the same time complexity do not necessarily belong to the same space efficiency class.
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KR - Comparing space efficiency of propositional Knowledge Representation Formalisms
1996Co-Authors: Marco Cadoli, Francesco M. Donini, Paolo Liberatore, Marco SchaerfAbstract:We investigate the space efficiency of a Propositional Knowledge Representation (PKR) formalism. Informally, the space efficiency of a formalism F in representing a certain piece of Knowledge α, is the size of the shortest formula of F that represents α. In this paper we assume that Knowledge is either a set of propositional interpretations or a set of formulae (theorems). We provide a formal way of talking about the relative ability of PKR Formalisms to compactly represent a set of models or a set of theorems. We introduce two new compactness measures, the corresponding classes, and show that the relative space efficiency of a PKR formalism in representing models/theorems is directly related to such classes. In particular, we consider Formalisms for nonmonotonic reasoning, such as circumscription and default logic, as well as belief revision operators.
Marc Denecker - One of the best experts on this subject based on the ideXlab platform.
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Safe inductions and their applications in Knowledge Representation
Artificial Intelligence, 2018Co-Authors: Bart Bogaerts, Joost Vennekens, Marc DeneckerAbstract:Abstract In many Knowledge Representation Formalisms, a constructive semantics is defined based on sequential applications of rules or of a semantic operator. These constructions often share the property that rule applications must be delayed until it is safe to do so: until it is known that the condition that triggers the rule will continue to hold. This intuition occurs for instance in the well-founded semantics of logic programs and in autoepistemic logic. In this paper, we formally define the safety criterion algebraically. We study properties of so-called safe inductions and apply our theory to logic programming and autoepistemic logic. For the latter, we show that safe inductions manage to capture the intended meaning of a class of theories on which all classical constructive semantics fail.
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IJCAI - Safe inductions: An algebraic study
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017Co-Authors: Bart Bogaerts, Joost Vennekens, Marc DeneckerAbstract:In many Knowledge Representation Formalisms, a constructive semantics is defined based on sequential applications of rules or of a semantic operator. These constructions often share the property that rule applications must be delayed until it is safe to do so: until it is known that the condition that triggers the rule will remain to hold. This intuition occurs for instance in the well-founded semantics of logic programs and in autoepistemic logic. In this paper, we formally define the safety criterion algebraically. We study properties of so-called safe inductions and apply our theory to logic programming and autoepistemic logic. For the latter, we show that safe inductions manage to capture the intended meaning of a class of theories on which all classical constructive semantics fail.
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A Formal Theory of Justifications
Logic Programming and Nonmonotonic Reasoning, 2015Co-Authors: Marc Denecker, Gerhard Brewka, Hannes StrassAbstract:We develop an abstract theory of justifications suitable for describing the semantics of a range of logics in Knowledge Representation, computational and mathematical logic. A theory or program in one of these logics induces a semantical structure called a justification frame. Such a justification frame defines a class of justifications each of which embodies a potential reason why its facts are true. By defining various evaluation functions for these justifications, a range of different semantics are obtained. By allowing nesting of justification frames, various language constructs can be integrated in a seamless way. The theory provides elegant and compact formalisations of existing and new semantics in logics of various areas, showing unexpected commonalities and interrelations, and creating opportunities for new expressive Knowledge Representation Formalisms.
Yuliya Lierler - One of the best experts on this subject based on the ideXlab platform.
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Strong Equivalence and Program's Structure in Arguing Essential Equivalence between Logic Programs
arXiv: Artificial Intelligence, 2019Co-Authors: Yuliya LierlerAbstract:Answer set programming is a prominent declarative programming paradigm used in formulating combinatorial search problems and implementing distinct Knowledge Representation Formalisms. It is common that several related and yet substantially different answer set programs exist for a given problem. Sometimes these encodings may display significantly different performance. Uncovering {\em precise formal} links between these programs is often important and yet far from trivial. This paper claims the correctness of a number of interesting program rewritings.
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PADL - Strong Equivalence and Program's Structure in Arguing Essential Equivalence between First-Order Logic Programs
Practical Aspects of Declarative Languages, 2018Co-Authors: Yuliya LierlerAbstract:Answer set programming is a prominent declarative programming paradigm used in formulating combinatorial search problems and implementing distinct Knowledge Representation Formalisms. It is common that several related and yet substantially different answer set programs exist for a given problem. Sometimes these encodings may display significantly different performance. Uncovering precise formal links between these programs is often important and yet far from trivial. This paper claims the correctness of a number of interesting program rewritings. Notably, they assume programs with variables and such important language features as choice, disjunction, and aggregates.
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AAAI - An abstract view on modularity in Knowledge Representation
2015Co-Authors: Yuliya Lierler, Miroslaw TruszczynskiAbstract:Modularity is an essential aspect of Knowledge Representation theory and practice. It has received substantial attention. We introduce model-based modular systems, an abstract framework for modular Knowledge Representation Formalisms, similar in scope to multi-context systems but employing a simpler information-flow mechanism. We establish the precise relationship between the two frameworks, showing that they can simulate each other. We demonstrate that recently introduced modular Knowledge Representation Formalisms integrating logic programming with satisfiability and, more generally, with constraint satisfaction can be cast as modular systems in our sense. These results show that our formalism offers a simple unifying framework for studies of modularity in Knowledge Representation.
David H. Jonassen - One of the best experts on this subject based on the ideXlab platform.
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Computers as cognitive tools: Learningwith technology, notfrom technology
Journal of Computing in Higher Education, 1995Co-Authors: David H. JonassenAbstract:COGNITIVE TOOLS are computer-based applications that are normally used as productivity software. However, these applications may also function as Knowledge Representation Formalisms that require learners to think critically when using them to represent content being studied or what they already know about a subject. Applications such as databases, spreadsheets, semantic networks, expert systems, multimedia/hypermedia construction, can function as computer-based cognitive tools that function as intellectual partners with learners to expand and even amplify their thinking, thereby changing the role of learners in college classrooms to Knowledge constructors rather than information reproducers. Cognitive tools are examples of learning with technologies rather than from them.
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Computers as Cognitive Tools: Learning with Technology, Not from Technology
Journal of Computing in Higher Education, 1995Co-Authors: David H. JonassenAbstract:COGNITIVE TOOLS are computer-based applications that are normally used as productivity software. However, these applications may also function as Knowledge Representation Formalisms that require learners to think critically when using them to represent content being studied or what they already know about a subject. Applications such as databases, spreadsheets, semantic networks, expert systems, multimedia/hypermedia construction, can function as computer-based cognitive tools that function as intellectual partners with learners to expand and even amplify their thinking, thereby changing the role of learners in college classrooms to Knowledge constructors rather than information reproducers. Cognitive tools are examples of learningwith technologies rather thanfrom them.
Christophe Gonzales - One of the best experts on this subject based on the ideXlab platform.
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Graph Structures for Knowledge Representation and Reasoning - Graph Structures for Knowledge Representation and Reasoning
Lecture Notes in Computer Science, 2015Co-Authors: Madalina Croitoru, Sebastian Rudolph, Stefan Woltran, Christophe GonzalesAbstract:Versatile and effective techniques for Knowledge Representation and reasoning (KRR) are essential for the development of successful intelligent systems. Many representatives of next generation KRR systems are based on graph-based Knowledge Representation Formalisms and leverage graph-theoretical notions and results. The goal of the workshop series on Graph Structures for Knowledge Representation and Reasoning (GKR) is to bring together the researchers involved in the development and application of graph-based Knowledge Representation Formalisms and reasoning techniques. This volume contains revised selected papers of the third edition of GKR, which took place in Beijing, China on August 3, 2013. Like the previous editions, held in Pasadena, USA (2009), and in Barcelona, Spain (2011), the workshop was associated with IJCAI (the International Joint Conference on Artificial Intelligence), thus providing the perfect venue for a rich and valuable exchange. The scientific program of this workshop included many topics related to graph-based Knowledge Representation and reasoning such as Representations of constraint satisfaction problems, formal concept analysis, conceptual graphs, argumentation frameworks and many more. All in all, the third edition of the GKR workshop was very successful. The papers coming from diverse fields all addressed various issues for Knowledge Representation and reasoning and the common graph-theoretic background allowed to bridge the gap between the different communities. This made it possible for the participants to gain new insights and inspiration. We are grateful for the support of IJCAI and we would also like to thank the Program Committee of the workshop for their hard work in reviewing papers and providing valuable guidance to the contributors. But, of course, GKR 2013 would not have been possible without the dedicated involvement of the contributing authors and participants.
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Graph Structures for Knowledge Representation and Reasoning
2013Co-Authors: Madalina Croitoru, Sebastian Rudolph, Stefan Woltran, Christophe GonzalesAbstract:Versatile and effective techniques for Knowledge Representation and reasoning (KRR) are essential for the development of successful intelligent systems. Many representatives of next generation KRR systems are based on graph-based Knowledge Representation Formalisms and leverage graph-theoretical notions and results. The goal of the workshop series on Graph Structures for Knowledge Representation and Reasoning (GKR) is to bring together the researchers involved in the development and application of graph-based Knowledge Representation Formalisms and reasoning techniques. This volume contains revised selected papers of the third edition of GKR, which took place in Beijing, China on August 3, 2013. Like the previous editions, held in Pasadena, USA (2009), and in Barcelona, Spain (2011), the workshop was associated with IJCAI (the International Joint Conference on Artificial Intelligence), thus providing the perfect venue for a rich and valuable exchange. The scientific program of this workshop included many topics related to graph-based Knowledge Representation and reasoning such as Representations of constraint satisfaction problems, formal concept analysis, conceptual graphs, argumentation frameworks and many more. All in all, the third edition of the GKR workshop was very successful. The papers coming from diverse fields all addressed various issues for Knowledge Representation and reasoning and the common graph-theoretic background allowed to bridge the gap between the different communities. This made it possible for the participants to gain new insights and inspiration. We are grateful for the support of IJCAI and we would also like to thank the Program Committee of the workshop for their hard work in reviewing papers and providing valuable guidance to the contributors. But, of course, GKR 2013 would not have been possible without the dedicated involvement of the contributing authors and participants.
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First IJCAI International Workshop on Graph Structures for Knowledge Representation and Reasoning (GKR@IJCAI'09)
2009Co-Authors: Madalina Croitoru, Christophe Gonzales, Boris Motik, Jérôme Lang, Marie-laure MugnierAbstract:The development of effective techniques for Knowledge Representation and reasoning (KRR) is a crucial aspect of successful intelligent systems. Different Representation paradigms, as well as their use in dedicated reasoning systems, have been extensively studied in the past. Nevertheless, new challenges, problems, and issues have emerged in the context of Knowledge Representation in Artificial Intelligence (AI), involving the logical manipulation of increasingly large information sets (see for example Semantic Web, BioInformatics and so on). Improvements in storage capacity and performance of computing infrastructure have also affected the nature of KRR systems, shifting their focus towards Representational power and execution performance. Therefore, KRR research is faced with a challenge of developing Knowledge Representation structures optimized for large scale reasoning. This new generation of KRR systems includes graph-based Knowledge Representation Formalisms such as Bayesian Networks (BNs), Semantic Networks (SNs), Conceptual Graphs (CGs), Formal Concept Analysis (FCA), CPnets, GAI-nets, all of which have been successfully used in a number of applications. The goal of this workshop is to bring together the researchers involved in the development and application of graph-based Knowledge Representation Formalisms and reasoning techniques.
