Multi-dimensional dynamic knowledge representation

International Journal of Advanced Trends in Computer Science and Engineering, 2021

Description logic gives us the ability of reasoning with acceptable computational complexity with retaining the power of expressiveness. The power of description logic can be accompanied by the defeasible logic to manage non-monotonic reasoning. In some domains, we need flexible reasoning and knowledge representation to deal the dynamicity of such domains. In this paper, we present a DL representation for a small domain that describes the connections between different entities in a university publication system to show how could we deal with changeability in domain rules. An automated support can be provided on the basis of defeasible logical rules to represent the typicality in the knowledge base and to solve the conflicts that might happen.

1994

Abstract In this paper, we review recent work aimed at the application of declarative logic programming to knowledge representation in artificial intelligence. We consider extensions of the language of definite logic programs by classical (strong) negation, disjunction, and some modal operators and show how each of the added features extends the representational power of the language.

Theoretical Computer Science, 1997

This paper introduces an extension of logic programming based on multi-dimensional logics, called MLP. In a multi-dimensional logic the values of elements vary depending on more than one dimension, such as time and space. The resulting logic programming language is suitable for modelling objects which involve implicit and/or explicit temporal and spatial dependencies. The execution of programs of the language is based on a resolution-type proof procedure called MSLD-resolution (for multi-dimensional SLD-resolution). The paper also establishes the declarative semantics of multi-dimensional logic programs, based on an extension of Herbrand models. In particular, it is shown that MLP programs satisfy the minimum model semantics. A novel multidimensional interface to MLP is also outlined; it can be used as a powerful development tool with the advantage of non-determinism inherent in logic programming.

Since the fall of 1994, we have been working on a new version of Classic Resnick et al., 1993; Brachman et al., 1991], called NeoClassic.

2008

This paper provides an introduction to knowledge representation using OntoDLP, a formalism which combines the full computational power of Disjunctive Logic Programming (DLP) with suitable abstraction mechanisms for the representation of complex objects and default reasoning. The paper does not provide a formal definition of the language, rather it is intended as an informal presentation of its main features.

Using morpho-logics we show how to find explana- tions of observations, how to perform revision, con- traction, fusion, in an unified way. In the framework of abduction, we show how to deal with observa- tions inconsistent with the background,theory and introduce methods,to treat multiple observations. Based on these ideas we introduce a dynamics,for transforming the background,theory in function of observations. Keywords: Explanations, revision, fusion, dis-

Internet: http://www8. informatik. unierlangen. …, 2007

c v aspects run the risk of not scaling up properly to account for human level competence. In the end, our view is that Knowledge Representation is the study of how what we know can at the same time be represented as comprehensibly as possible and reasoned with as effectively as possibly. There is a tradeoff between these two concerns, which is an implicit theme throughout the book, and explicit in the final chapter. Although we start with full first-order logic as a representation language, and logical entailment as the basis for reasoning, this is just the starting point, and a somewhat unrealistic one at that. Subsequent chapters expand and enhance the picture by looking at languages with very different intuitions and emphases, and approaches to reasoning sometimes quite removed from logical entailment. Our approach is to explain the key concepts underlying a wide variety of formalisms, without trying to account for the quirks of particular representation schemes proposed in the literature. By exposing the heart of each style of representation, complemented by a discussion of the basics of reasoning with that representation, we aim to give the reader a solid foundation for understanding the more detailed and sophisticated work found in the research literature. The book is organized as follows. The first chapter provides an overview and motivation for the whole area. Chapters 2 through 5 are concerned with the basic techniques of Knowledge Representation using first-order logic in a direct way. These early chapters introduce the notation of first-order logic, show how it can be used to represent commonsense worlds, and cover the key reasoning technique of Resolution theorem-proving. Chapters 6 and 7 are concerned with representing knowledge in a more limited way, so that the reasoning is more amenable to procedural control; among the important concepts covered there we find rule-based production systems. Chapters 8 through 10 deal with a more object-oriented approach to Knowledge Representation and the taxonomic reasoning that goes with it. Here we delve into the ideas of frame representations and description logics, as well as spending time on the notion of inheritance. Chapters 11 and 12 deal with reasoning that is uncertain or not logically guaranteed to be correct, including default reasoning and probabilities. Chapters 13 through 15 deal with forms of reasoning that are not concerned with deriving new beliefs from old ones, including the notion of planning, which is central to AI. Finally, Chapter 16 explores the tradeoff mentioned above. A course based on the topics of this book has been taught a number of times at the University of Toronto. The course comprises about 24 hours of lectures and occasional tutorials, and is intended for upper-level undergraduate students or entrylevel graduate students in Computer Science or a related discipline. Students are expected to have already taken an introductory course in AI where the larger picture 2003 R. Brachman and H. Levesque July 17, 2003 c vi of intelligent agents is presented and explored, and to have some working knowledge of symbolic logic and symbolic computation, for example, in Prolog or Lisp. As part of a program in AI or Cognitive Science, the Knowledge Representation course fits well between a basic course in AI and research-oriented graduate courses (on topics like probabilistic reasoning, nonmonotonic reasoning, logics of knowledge and belief, and so on). A number of the exercises used in the course are included at the end of each chapter of the book. These exercises focus on the technical aspects of Knowledge Representation, although it should be possible with this book to consider some essay-type questions as well. Depending on the students involved, a course instructor may want to emphasize the programming questions and de-emphasize the mathematics, or perhaps vice-versa. Comments and corrections on all aspects of the book are most welcome and should be sent to the authors. Preface iv 2.3.1 Interpretations 20 2.3.2 Denotation 21 2.3.3 Satisfaction and models 21 2.4 The pragmatics 22 2.4.1 Logical consequence 23 2.4.2 Why we care 23 2.5 Explicit and implicit belief 25 2.5.1 An example 25 2.5.2 Knowledge-based systems 27 2003 R. Brachman and H. Levesque July 17, 2003 3 Expressing Knowledge 4 Resolution 5 Reasoning with Horn Clauses c viii 2.6 Bibliographic notes 2.7 Exercises 3.1 Knowledge engineering 3.2 Vocabulary 3.3 Basic facts 3.4 Complex facts 3.5 Terminological facts 3.6 Entailments 3.7 Abstract individuals 3.8 Other sorts of facts 3.9 Bibliographic notes 3.10 Exercises 4.1 The propositional case 4.1.

Proceedings of the IEEE, 2000

This paper discusses a number of important issues that drive knowledge representation research. It begins by considering the relationship between knowledge and the world and the use of knowledge by reasoning agents (both biological and mechanical) and concludes that a knowledge representation system must sup port activities of perception, learning, and planning to act. An argument is made that the mechanisms of traditional formal logic, while important to our understanding of mechanical reasoning, are not by themselves sufficient to solve all of the associated problems. In particular, notational aspects of a knowledge representation system are important-both for computational and concep tual reasons. Two such aspects are distinguished-expressive adequacy and notational efficacy. The paper also discusses the structure of conceptual representations and argues that taxonomic classification structures can advance both expressive adequacy and notational efficacy. It predicts that such techniques will eventually be applicable throughout computer science and that their application can produce a new style of programming-more oriented toward specifying the desired behavior in conceptual terms. Such "taxonomic programming" can have advantages for flexibility, extensibility, and maintainability, as well as for documentation and user education.

2004

: Knowledge Representations issues take on special significance in the light of development of the novel Web's reality that involves the Semantic Web, GRID, P2P and other today's ITs. In contrast to the previous IT evolution's stages, the recent one utilizes ontology as separated resource. An elaborate knowledge representation approach implies an efficiency of knowledge-based systems and their interoperability. This paper deals with Ontology Engineering approach that allows both build and generate the consistent dynamic autonomous knowledge-based systems.

Applied Artificial Intelligence, 1991

In this paper we discuss recent developments of the research on Knowledge Representation, focusing on hybrid formalisms, nonmonotonic reasoning and formalisms for reasoning about knowledge and reasoning in a multi-agent scenario.

Computational Linguistics, 2001

2000

Dynamic logic programming allows the representation and the inference of evolv- ing knowledge. Legal knowledge reasoning needs the capability to model laws that change over time and to model laws produced by distinct entities with different priorities at differ- ent time points. In this paper we propose the use of dynamic logic programming to model these legal dynamic situations. Some

WSEAS Transactions on …

In this paper it is presented a formal methodology based on topology theory to represent knowledge. This framework is used to describe the creation of a semantic network, which is then represented by means of First-Order Logic. By a series of functions applied to a natural basis, issued from the application domain, a family of sets are synthesized with their sub-spaces correlated. Therefore the resultant sub-spaces and their relations form a network of elementary and complex concepts. Complete correspondence among the sub-spaces, the IDEF1x model of the semantic network and the First-Order Logic is obtained by employing this framework. The process planning application domain is used to illustrate the creation of the resultant knowledge base.

2002

In the first part of this Chapter we will introduce a general temporally enhanced conceptual data model able to represent time varying data, in the spirit of a temporally enhanced Entity-Relationship data model. In the second part, we will introduce an object-oriented conceptual data model enriched with schema change operators, which are able to represent the explicit temporal evolution of the schema while maintaining a consistent view on the (static) instantiated data. We will introduce a provably correct encoding of both conceptual data models and their inference problems in Description Logics. In this way, we study the properties of both the temporal conceptual data model and the object-oriented data model with schema change facilities.

2000

At present, the search for specific information on the World Wide Web is faced with several problems, which arise on the one hand from the vast number of information sources available, and on the other hand from their intrinsic heterogeneity, since standards are missing. A promising approach for solving the complex problems emerging in this context is the use of multi-agent systems of information agents, which cooperatively solve advanced information-retrieval problems. This requires advanced capabilities to address complex tasks, such as search and assessment of information sources, query planning, information merging and fusion, dealing with incomplete information, and handling of inconsistency. In this paper, our interest lies in the role which some methods from the field of declarative logic programming can play in the realization of reasoning capabilities for information agents. In particular, we are interested to see in how they can be used, extended, and further developed for the specific needs of this application domain. We review some existing systems and current projects, which typically address information-integration problems. We then focus on declarative knowledge-representation methods, and review and evaluate approaches and methods from logic programming and nonmonotonic reasoning for information agents. We discuss advantages and drawbacks, and point out the possible extensions and open issues. Contents 1 Introduction 3 2 Intelligent Information Agents 4 3 Problems and Challenges 8 4 Systems and Frameworks 9 4.1 Cohen's Information System for Structured Collections of Text 10 4.2 Information Manifold 11 4.3 Carnot 11 4.4 InfoSleuth 12 4.5 Infomaster 12 4.6 COIN 13 7 Revision and Update 7.1 Revision Programs by Marek and Truszczyński 7.2 Update Rules as Logic Programs by Pereira et al. 7.3 Abductive Updates by Inoue and Sakama 7.4 Updates by Means of PLPs by Foo and Zhang 7.5 Dynamic Logic Programming by Alferes et al. 7.6 Updates and Preferences by Alferes and Pereira 7.7 Inheritance Programs and Updates 7.8 Revision of Preference Default Theories by Brewka 7.9 Arbitration 8 Quantitative Information 8.1 Disjunctive Programs with Weak Constraints by Buccafurri et al. 8.2 Weight Constraint Rules by Niemelä et al. 8.3 Weighted Logic Programming by Marek and Truszczyński 8.4 Probabilistic Programs by Subrahmanian et al. 9 Temporal Reasoning 9.1 Reasoning about Actions 9.2 Temporal Logics for BDI agents 9.3 LUPS, a Language for Specifying Updates 10 Evaluation 10.1 Preference Handling 10.2 Logic Programs with Quantitative Information 10.3 Revision and Update 10.4 Temporal Reasoning 11 Conclusion References