Showing papers on "Abductive reasoning published in 2000"

Practical reasoning for very expressive description logics

Abstract: Description Logics (DLs) are a family of knowledge representation formalisms mainly characterised by constructors to build complex concepts and roles from atomic ones. Expressive role constructors are important in many applications, but can be computationally problematical. We present an algorithm that decides satisfiability of the DL ALC extended with transitive and inverse roles and functional restrictions with respect to general concept inclusion axioms and role hierarchies; early experiments indicate that this algorithm is well-suited for implementation. Additionally, we show that ALC extended with just transitive and inverse roles is still in PSpace. We investigate the limits of decidability for this family of DLs, showing that relaxing the constraints placed on the kinds of roles used in number restrictions leads to the undecidability of all inference problems. Finally, we describe a number of optimisation techniques that are crucial in obtaining implementations of the decision procedures, which, despite the hight worst-case complexity of the problem, exhibit good performance with real-life problems.

ACLP: Abductive Constraint Logic Programming

Abstract: This paper presents the framework of Abductive Constraint Logic Programming (ACLP), which integrates Abductive Logic Programming (ALP) and Constraint Logic Programming (CLP). In ACLP, the task of abduction is supported and enhanced by its non-trivial integration with constraint solving. This integration of constraint solving into abductive reasoning facilitates a general form of constructive abduction and enables the application of abduction to computationally demanding problems. The paper studies the formal declarative and operational semantics of the ACLP framework together with its application to various problems. The general characteristics of the computation of ACLP and of its application to problems are also discussed. Empirical results based on an implementation of the ACLP framework on top of the CLP language of ECLiPSe show that ACLP is computationally viable, with performance comparable to the underlying CLP framework on which it is built. In addition, our experiments show the natural ability for ACLP to accommodate easily and in a robust way new or changing requirements of the original problem. ACLP thus combines the advantages of modularity and flexibility of the high-level representation afforded by abduction together with the computational effectiveness of low-level specialised constraint solving.

Theory Completion Using Inverse Entailment

Abstract: The main real-world applications of Inductive Logic Programming (ILP) to date involve the "Observation Predicate Learning" (OPL) assumption, in which both the examples and hypotheses define the same predicate. However, in both scientific discovery and language learning potential applications exist in which OPL does not hold. OPL is ingrained within the theory and performance testing of Machine Learning. A general ILP technique called "Theory Completion using Inverse Entailment" (TCIE) is introduced which is applicable to non-OPL applications. TCIE is based on inverse entailment and is closely allied to abductive inference. The implementation of TCIE within Progol5.0 is described. The implementation uses contra-positives in a similar way to Stickel's Prolog Technology Theorem Prover. Progol5.0 is tested on two different data-sets. The first dataset involves a grammar which translates numbers to their representation in English. The second dataset involves hypothesising the function of unknown genes within a network of metabolic pathways. On both datasets near complete recovery of performance is achieved after relearning when randomly chosen portions of background knowledge are removed. Progol5.0's running times for experiments in this paper were typically under 6 seconds on a standard laptop PC.

System and method of programming an implantable medical device

Abstract: A system and method of programming an implantable medical device is disclosed. The system includes a causal model coupled to an implantable medical device and capable of identifying at least one cause of an abnormal condition associated with the implantable medical device or the patient. An abductive inference engine is coupled to the causal model and is capable of identifying a suggested updated setting for the implantable medical device to alleviate the abnormal condition. A display is coupled to the causal model and displays the abnormal behavior and the suggested updated settings to the clinician such that the clinician may implement the suggested updated settings.

Abductive and inductive reasoning: background and issues

Abstract: The outline of this chapter is as follows. In Section 1.2 we discuss the philosophical and logical origins of abduction and induction. In Section 1.3 we analyse previous work on abduction and induction in the context of logic programming\indexlogic programming and artificial intelligence, and attempt a (partial) synthesis of this work. Section 1.4 considers the integration of abduction and induction in artificial intelligence, and Section 1.5 concludes.

Integrating Abduction and Induction in Machine Learning

Abstract: Abduction is the process of inferring cause from effect or constructing explanations for observed events and is central to tasks such as diagnosis and plan recognition. Induction is the process of inferring general rules from specific data and is the primary task of machine learning. An important issue is how these two reasoning processes can be integrated, or how abduction can aid machine learning and how machine learning can acquire abductive theories. The machine learning research group at the University of Texas at Austin has explored these issues in the development of several machine learning systems over the last ten years. In particular, we have developed methods for using abduction to identify faults and suggest repairs for theory refinement (the task of revising a knowledge base to fit empirical data), and for inducing knowledge bases for abductive diagnosis from a database of expert-diagnosed cases. We treat induction and abduction as two distinct reasoning tasks, but have demonstrated that each can be of direct service to the other in developing AI systems for solving real-world problems. This chapter reviews our work in these areas, focusing on the issue of how abduction and induction is integrated.1

Abduction, belief, and context in dialogue : studies in computational pragmatics

Abstract: 1. The ABC of Computational Pragmatics (by Bunt, Harry) 2. An activity-based approach to pragmatics (by Allwood, Jens) 3. Dialogue pragmatics and context specification (by Bunt, Harry) 4. Pragmatics in language understanding and cognitively motivated architectures (by Sabah, Gerard) 5. Dialogue analysis using layered protocols (by Taylor, M. Martin) 6. Coherence and structure in text and discourse (by Redeker, Gisela) 7. Discourse focus tracking (by Carter, David) 8. Speech act theory and epistemic planning (by Ramsay, Allan) 9. Context and form: declarative or interrogative, that is the question (by Beun, Robbert-Jan) 10. The doxastic-epistemic force of declarative utterances (by Thijsse, Elias C.G.) 11. A conceptual modelling approach to the implementation of beliefs and intentions (by Meyer, Ralph) 12. Abduction and induction: a real distinction? (by Neal, Philip) 13. Laconic discourses and total eclipses: abduction in DICE (by Oberlander, Jon) 14. Abductive reasoning with knowledge bases for context modelling (by Guessoum, Ahmed) 15. Abductive speech act recognition, corporate agents, and the COSMA system (by Hinkelman, Elizabeth) 16. List of contributors 17. Index

Abductive concept learning

Abstract: We investigate how abduction and induction can be integrated into a common learning framework. In particular, we consider an extension of Inductive Logic Programming (ILP) for the case in which both the background and the target theories are abductive logic programs and where an abductive notion of entailment is used as the basic coverage relation for learning. This extended learning framework has been called Abductive Concept Learning (ACL). In this framework, it is possible to learn with incomplete background information about the training examples by exploiting the hypothetical reasoning of abduction. We also study how the ACL framework can be used as a basis for multiple predicate learning. An algorithm for ACL is developed by suitably extending the top-down ILP method: the deductive proof procedure of Logic Programming is replaced by an abductive proof procedure for Abductive Logic Programming. This algorithm also incorporates a phase for learning integrity constraints by suitably employing a system that learns from interpretations like ICL. The framework of ACL thus integrates the two ILP settings of explanatory (predictive) learning and confirmatory (descriptive) learning. The above algorithm has been implemented into a system also called ACL Several experiments have been performed that show the effectiveness of the ACL framework in learning from incomplete data and its appropriate use for multiple predicate learning.

Computer-aided reasoning

Abstract: Find loads of the computer aided reasoning book catalogues in this site as the choice of you visiting this page. You can also join to the website book library that will show you numerous books from any types. Literature, science, politics, and many more catalogues are presented to offer you the best book to find. The book that really makes you feels satisfied. Or that's the book that will save you from your job deadline.

Smart Inductive Generalizations are Abductions

Abstract: To postpone entanglements with the abundant confusions surrounding various uses of the term “abduction,” for which Peirce himself seems to be largely responsible, and to proceed as directly as possible to engage the basic logical and computational issues, let us begin by examining a pattern of inference I will call “inference to the best explanation” and abbreviate as “IBE” (“IBEs” for the plural).1

Logic, Knowledge Representation, and Bayesian Decision Theory

Abstract: In this paper I give a brief overview of recent work on uncertainty in AI, and relate it to logical representations. Bayesian decision theory and logic are both normative frameworks for reasoning that emphasize different aspects of intelligent reasoning. Belief networks (Bayesian networks) are representations of independence that form the basis for understanding much of the recent work on reasoning under uncertainty, evidential and causal reasoning, decision analysis, dynamical systems, optimal control, reinforcement learning and Bayesian learning. The independent choice logic provides a bridge between logical representations and belief networks that lets us understand these other representations and their relationship to logic and shows how they can extended to first-order rule-based representations. This paper discusses what the representations of uncertainty can bring to the computational logic community and what the computational logic community can bring to those studying reasoning under uncertainty.

ACLP: Integrating Abduction and Constraint Solving

Abstract: ACLP is a system which combines abductive reasoning and constraint solving by integrating the frameworks of Abductive Logic Programming (ALP) and Constraint Logic Programming (CLP). It forms a general high-level knowledge representation environment for abductive problems in Artificial Intelligence and other areas. In ACLP, the task of abduction is supported and enhanced by its non-trivial integration with constraint solving facilitating its application to complex problems. The ACLP system is currently implemented on top of the CLP language of ECLiPSe as a meta-interpreter exploiting its underlying constraint solver for finite domains. It has been applied to the problems of planning and scheduling in order to test its computational effectiveness compared with the direct use of the (lower level) constraint solving framework of CLP on which it is built. These experiments provide evidence that the abductive framework of ACLP does not compromise significantly the computational efficiency of the solutions. Other experiments show the natural ability of ACLP to accommodate easily and in a robust way new or changing requirements of the original problem.

Abductive analogical reasoning

Abstract: If a knowledge base does not have all of the necessary clauses for reasoning, ordinary hypothetical reasoning systems cannot explain observations. In this case, it is necessary to explain such observations by abductive reasoning, supplemental reasoning, or approximate reasoning. The inference in this paper explains the observation by supplementing missing knowledge with reference to similar knowledge when an observation cannot be explained because necessary knowledge is lacking. However, it is somewhat difficult to find clauses to explain an observation without hints. Therefore, an abductive strategy (CMS) is used to find missing clauses. A piece of knowledge which is similar to the missing knowledge is sought in the knowledge base and mapped to the knowledge in the same problem domain as the missing knowledge. Then the observation is explained by generated hypotheses similar to the knowledge in the knowledge base. © 1999 Scripta Technica, Syst Comp Jpn, 31(1): 11–19, 2000

Abduction as Epistemic Change: A Peircean Model in Artificial Intelligence

Abstract: Charles S. Peirce’s abductive formulation ((Peirce, 1958, 5.189), reproduced on p.7), has been the point of departure of many recent studies on abductive reasoning in artificial intelligence, such as in logic programming (Kakas et al.,1992), knowledge acquisition (Kakas and Mancarella, 1994) and natural language processing (Hobbs et al.,1990).

Abduction: Between Conceptual Richness and Computational Complexity

Abstract: The aim of this chapter is two-fold: first, to explore the relationship between abduction and induction from a philosophical point of view; and second, to examine critically some recent attempts to provide computational models of abduction. Induction is typically conceived as the mode of reasoning which produces generalisations over domains of individuals based on samples. Abduction, on the other hand, is typically seen as the mode of reasoning which produces hypotheses such that, if true, they would explain certain phenomena or evidence. Recently there has been some increasing interest in the issue of how exactly, if at all, they are related. Two seem to be the main problems: first, whether or not induction and abduction are conceptually distinct modes of reasoning; second, whether or not they can be modelled computationally in the same, or similar, ways. The second issue is explored in some detail by several chapters in this collection (e.g. the contributions by Aliseda, Mooney and Poole). The first issue is what the present chapter will concentrate on. My suggestion will be that abduction is the basic type of ampliative reasoning. It comprises as special case both Induction and what the American philosopher Charles Peirce called “the Method of Hypothesis”.

Abduction: A Logical Criterion For Programme and Project Evaluation

Abstract: Evaluation is afflicted by a number of ethical and methodological problems. A major problem is the difficulty evaluation has in maintaining itself as an independent, autonomous discipline. An answer to these problems is often sought by recourse to the more structured field of research, even though the canonical logic of research and its criteria are not suitable for most work in which evaluation as a discipline is required. A possible solution for this dilemma can be found in the work of the American philosopher Charles Sanders Peirce. According to Peirce, the decision to adopt a new hypothesis by a scientist, researcher or, for our purposes, evaluator is as logical a process as deduction or induction. Peirce calls this process ‘abduction’. The evaluator, much like the scientist working through a process of discovery, raises hypotheses that stem from the field being evaluated. By adopting Peirce's methods we can build a logical methodological framework for the process of evaluation. Such a methodology can...

Abductive and Inductive Reasoning: Essays on their Relation and Integration

Abstract: This article discusses the integration of traditional abductive and inductive reasoning methods in the development of machine learning systems. In particular, it reviews our recent work in two areas: 1) The use of traditional abductive methods to propose revisions during theory reenement, where an existing knowledge base is modiied to make it consistent with a set of empirical data; and 2) The use of inductive learning methods to automatically acquire from examples a diagnostic knowledge base used for abductive reasoning. Experimental results on real-world problems are presented to illustrate the capabilities of both of these approaches to integrating the two forms of reasoning.

An overview of ordinal and numerical approaches to causal diagnostic problem solving

Abstract: Diagnostic problem solving aims at finding out disorders which may have caused observed symptoms of ill-behaviour. It is often called “abductive reasoning”. The pattern which, from the two premises “if disorder is present then symptom is observed” and “symptom is observed”, infers the conclusion “disorder is plausible”, can be viewed as the simplest pattern of abductive reasoning [Peirce, 1940]. This pattern can be contrasted with deductive inference which, from “if disorder is present then symptom is observed” and “disorder is present” infers that “symptom is observed”. Clearly, a key problem in abduction is to provide some status to the abductive conclusion “disorder is plausible” and maybe to assign some degree of plausibility to this disorder, or at least to be able to rank possible causes of an observed state of facts according to their relative plausibilities. The abductive pattern, although considered by many authors (e.g., [Polya, 19681), has not been given any logical or numerical formalization until recently, if we except the Bayesian model (which requires more information and where we compute the a posteriori probability of disorders on the basis of observations), and some heuristic, numerically quantified attempts (e.g., [Friedman, 1981; Bandler and Kohout, 1984; Hall, 1987]).

Toward a Perception-Based Theory of Probabilistic Reasoning

Abstract: The past two decades have witnessed a dramatic growth in the use of probability- based methods in a wide variety of applications centering on automation of decision-making in an environment of uncertainty and incompleteness of information.

AI approaches to abduction

Abstract: Abductive reasoning has gained increasing interest in many fields of AI research. Its utility was first observed for diagnostic tasks (cf. [Pople, 19731 or, e.g., [Console and Torasso, 1991; Console et al., 1991b]), but as many researchers have shown it is not limited to this use. Currently under investigation or suggested are such different applications as plan recognition (e.g., [Dragoni and Puliti, 1994; Helft and Konolige, 1990; Bauer and Paul, 1993; Bauer et al., 1993]), text understanding and generation (e.g., [Stickel, 1990]), program debugging (cf. [Charniak and McDermott, 1985]), test generation (see [Mcllraith, 1994]), planning (e.g., [Eshghi, 1991; Stone, 1998]), user modeling (cf. [Poole, 1988]), database updates (e.g., [Kakas and Mancarella, 1990a}), case-based reasoning (cf. [Leake, 1993; Satoh, 1998]), learning (cf. [Kakas et al., 1998; Lamma et al., 19971 or [Thompson and Mooney, 1990, temporal reasoning (e.g., [Li and Pereira, 19961), constraint handling (e.g., [Burckert and Nutt, 1992; Wetzel and Toni, 1998]) or vision (cf. [Charniak and McDermott, 1985]).

Using Abduction for Induction Based on Bottom Generalization

Abstract: Abduction is to find explanations which explain a given example assuming a background theory. Induction, often called inductive inference, means a process of generating general rules which given examples obey. From these simple definitions, we can expect such an inductive inference procedure that it generates rules by modifying explanations which some abductive inference generates from input examples. In this chapter we give such a procedure with the support of deductive inference and generalization. The procedure is a refinement of bottom generalization (Yamamoto, 1997; Yamamoto, 1999a), which was invented in the analysis of inverse entailment by (Muggleton, 1995). Because inverse entailment is an extension of bottom generalization, the results in this chapter show that inverse entailment also contains abduction potentially.

Conditional reasoning in logic programming

Abstract: We introduce a logic programming language which supports hypothetical and counterfactual reasoning. The language is based on a conditional logic which enables to formalize conditional updates of the knowledge base. Due to the presence of integrity constraints, alternative revisions of the knowledge base may result from an update. We develop an abductive semantics which captures different evolutions of the knowledge base. Furthermore, we provide a goal-directed abductive proof procedure to compute the alternative solutions for a goal. We finally analyze our conditional programming language in the context of belief revision theory, and we establish a connection with Nebel's prioritized base revision.

Abductive reasoning and learning

Abstract: On the Relation between Abduction and Inductive Learning P.A. Flach, A.C. Kakas. Part I: Logical Approaches. AI Approaches to Abduction G. Paul. Abduction in Labelled Deductive Systems D.M. Gabbay. Logical Characterisations of Inductive Learning P.A. Flach. Abduction in Machine Learning F. Bergadano, V. Cutello, D. Gunetti. Part II: Numerical Approaches. An Overview of Ordinal and Numerical Approaches to Causal Diagnostic Problem Solving D. Dubois, H. Prade. Abductive Inference with Probabilistic Networks C. Borgelt, R. Kruse. Learning from Data: Possibilistic Graphical Models J. Gebhardt. Independence in Uncertainty Theories and its Applications to Learning Belief Networks L.M. de Campos, J.F. Huete, S. Moral. Index.

A Modified Abductive Inference Model for Fault Section Estimation in Power Systems Using the Tabu Search Approach

Abstract: A new approach to fault section estimation in power systems is presented based upon a modified abductive inference model and the tabu search method. The developed fault section estimation model can simultaneously take into account the operating reliabilities of protective relays and circuit breakers and the degree of correctness of received and nonreceived alarm data in a formal and systematic manner. In the modified abductive inference model, a criterion for describing the relative plausibility of different diagnosis hypotheses is available. Based on this criterion, the fault section estimation problem is then formulated as a 0-1 integer programming problem, and a tabu search (TS) approach is presented for solving the problem. A sample power system is served for demonstrating the correctness of the developed abductive inference based fault section estimation model and the computational efficiency of the TS-based method.

An abductive approach to disjunctive logic programming

Abstract: Nonmonotonic reasoning has been explored as a form of abductive reasoning where default assumptions are treated as abductive hypotheses. While the semantics and proof theories under this approach have been studied extensively, the question of how disjunctive programs may be used to reason abductively has rarely been investigated. At the center of the question is how to embed disjunctive reasoning into that of negation-as-failure. A more concrete question is about whether the elegant abductive proof procedure by Eshghi and Kowalski can be extended to answer queries for disjunctive programs, and if yes, what is the semantics that such an extended procedure computes. In this paper we answer these questions by formulating a semantics, the regular extension semantics, for disjunctive programs, and by presenting a sound and complete extension of the Eshghi–Kowalski procedure, called disjunctive EK procedure, for query answering with respect to ground disjunctive programs under this semantics.

An abductive fuzzy knowledge based system for fault diagnosis in a power system

Abstract: This paper presents the design and evaluation of a novel, Al (artificial intelligence) based alarm processing and fault diagnosis system, for a 132 kV/12 bus-16 line sample power system. The work has been conducted in conjunction with Scottish Hydro Electric PLC. The fault diagnosis system is based on a hybrid object-oriented AI technique. The method developed utilises abductive inference. This technique is demonstrated to realise some improvements when compared with fuzzy logic and takes into account the current practical limitations in the design. The method is based on processing SCADA (supervisory control and data acquisition) messages, extending the arrangement of the knowledge acquisition process and applicability of circuit breakers and relays. The potential benefits and implications of adopting such an abductive fuzzy knowledge based system are demonstrated in this research, and include a user friendly inference engine, adaptability, and KBS update.