441 research outputs found

    Precise Propagation of Upper and Lower Probability Bounds in System P

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    In this paper we consider the inference rules of System P in the framework of coherent imprecise probabilistic assessments. Exploiting our algorithms, we propagate the lower and upper probability bounds associated with the conditional assertions of a given knowledge base, automatically obtaining the precise probability bounds for the derived conclusions of the inference rules. This allows a more flexible and realistic use of System P in default reasoning and provides an exact illustration of the degradation of the inference rules when interpreted in probabilistic terms. We also examine the disjunctive Weak Rational Monotony of System P+ proposed by Adams in his extended probability logic.Comment: 8 pages -8th Intl. Workshop on Non-Monotonic Reasoning NMR'2000, April 9-11, Breckenridge, Colorad

    Multilingual Knowledge Base Completion by Cross-lingual Semantic Relation Inference

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    International audienceIn the present paper, we propose a simple en-dogenous method for enhancing a multilingual knowledge base through the cross-lingual semantic relation inference. It can be run on multilingual resources prior to semantic representation learning. Multilingual knowledge bases may integrate preexisting structured resources available for resource-rich languages. We aim at performing cross-lingual inference on them to improve the low resource language by creating semantic relationships

    An anytime deduction heuristic for first order probabilistic logic

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    This thesis describes an anytime deduction heuristic to address the decision and optimization form of the First Order Probabilistic Logic problem which was revived by Nilsson in 1986. Reasoning under uncertainty is always an important issue for AI applications, e.g., expert systems, automated theorem-provers, etc. Among the proposed models and methods for dealing with uncertainty, some as, e.g., Nilsson's ones, are based on logic and probability. Nilsson revisited the early works of Boole (1854) and Hailperin (1976) and reformulated them in an AI framework. The decision form of the probabilistic logic problem, also known as PSAT, consists of finding, given a set of logical sentences together with their probability value to be true, whether the set of sentences and their probability value is consistent. In the optimization form, assuming that a system of probabilistic formulas is already consistent, the problem is: Given an additional sentence, find the tightest possible probability bounds such that the overall system remains consistent with that additional sentence. Solution schemes, both heuristic and exact, have been proposed within the propositional framework. Even though first order logic is more expressive than the propositional one, more works have been published in the propositional framework. The main objective of this thesis is to propose a solution scheme based on a heuristic approach, i.e., an anytime deduction technique, for the decision and optimization form of first order probabilistic logic problem. Jaumard et al. [33] proposed an anytime deduction algorithm for the propositional probabilistic logic which we extended to the first order context

    Machine learning and its applications in reliability analysis systems

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    In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

    Archaeological knowledge and its representation an inter-disciplinary study of the problems of knowledge representation

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    The thesis is a study of archaeology viewed from a perspective informed by (a) social constructionist theory and pragmatism; (b) techniques of Belief and Knowledge Representation developed by Artificial Intelligence research and (c) the conception of history and historical practice propounded by the philosopher, historian and archaeologist, R.G. Collingwood. It is argued that Gibsonian affordances and von Uexkull's notion of the Umwelt, recently discussed by Rom Harré, provide the basis for a description and understanding of human action and agency. Further, belief and knowledge representation techniques embodied in Expert Systems and Intelligent Tutoring Systems provide a means of implementing models of human action which may bridge intentionality and process and thereby provide a unifying learning environment in which the relationships of language, social action and material transformation of the physical world can be explored in a unified way. The central claim made by the thesis is that Collingwood's logic (dialectic) of Question & Answer developed in 1917 as a hermeneutic procedure, may be seen as a fore-runner of Newell and Simon's Heuristic Search, and thereby amenable to modem approaches to problem solving. Collingwood's own approach to History/ Archaeology is grounded on many shared ideas with pragmatism and a social constructionist conception of mind and is conducted within a problem solving framework. Collingwood is therefore seen as a three-way bridge between Social Psychology, Artificial Intelligence and Archaeology. The thesis concludes that Social Psychology, Artificial Intelligence and Archaeology can be integrated through the use of Intelligent Tutoring Systems informed by a Collingwoodian perspective on Archaeology, Mind and History - construed as Mind's self-knowledge

    Foundations of Fuzzy Logic and Semantic Web Languages

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    This book is the first to combine coverage of fuzzy logic and Semantic Web languages. It provides in-depth insight into fuzzy Semantic Web languages for non-fuzzy set theory and fuzzy logic experts. It also helps researchers of non-Semantic Web languages get a better understanding of the theoretical fundamentals of Semantic Web languages. The first part of the book covers all the theoretical and logical aspects of classical (two-valued) Semantic Web languages. The second part explains how to generalize these languages to cope with fuzzy set theory and fuzzy logic

    DFKI publications : the first four years ; 1990 - 1993

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