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Showing papers on "Abductive reasoning published in 2001"


Journal ArticleDOI
TL;DR: The logical and philosophical foundations of the competitive advantage hypothesis are explored, locating its philosophical foundations in the epistemologies of Bayesian induction, abductive inference and an instrumentalist, pragmatic philosophy of science.
Abstract: Strategic management theories invoke the concept of competitive advantage to explain firm performance, and empirical research investigates competitive advantage and describes how it operates. But as a performance hypothesis, competitive advantage has received surprisingly little formal justification, particularly in light of its centrality in strategy research and practice. As it happens, the core hypothesis - that competitive advantage produces sustained superior performance - finds little support in formal deductive or inductive inference, and the leading theories of competitive advantage incorporate refutation barriers that preclude meaningful empirical tests. The logical and philosophical foundations of the competitive advantage hypothesis are explored, locating its philosophical foundations in the epistemologies of Bayesian induction, abductive inference and an instrumentalist, pragmatic philosophy of science.

681 citations


Proceedings Article
04 Aug 2001
TL;DR: This paper presents a new system, called the A- System, performing abductive reasoning within the framework of Abductive Logic Programming, based on a hybrid computational model that implements the abductive search in terms of two tightly coupled processes.
Abstract: This paper presents a new system, called the A- System, performing abductive reasoning within the framework of Abductive Logic Programming. It is based on a hybrid computational model that implements the abductive search in terms of two tightly coupled processes: a reduction process of the highlevel logical representation to a lower-level constraint store and a lower-level constraint solving process. A set of initial "proof of principle" experiments demonstrate the versatility of the approach stemming from its declarative representation of problems and the good underlying computational behaviour of the system. The approach offers a general methodology of declarative problem solving in AI where an incremental and modular refinement of the high-level representation with extra domain knowledge can improve and scale the computational performance of the framework.

93 citations


Journal ArticleDOI
TL;DR: In this article, the author deals with the operational core of logic, i.e., its diverse procedures of inference, in order to show that logically false inferences may actually enlarge our knowledge of the world.
Abstract: The author deals with the operational core oflogic, i.e. its diverse procedures ofinference, in order to show that logicallyfalse inferences may in fact be right because –in contrast to logical rationality – theyactually enlarge our knowledge of the world.This does not only mean that logically trueinferences say nothing about the world, butalso that all our inferences are inventedhypotheses the adequacy of which cannot beproved within logic but only pragmatically. Inconclusion the author demonstrates, through therelationship between rule-following andrationality, that it is most irrational to wantto exclude the irrational: it may, at times, bemost rational to think and infer irrationally. Focussing on the operational aspects of knowingas inferring does away with the hiatus betweenlogic and life, cognition and the world(reality) – or whatever other dualism one wantsto invoke –: knowing means inferring, inferringmeans rule-governed interpreting, interpretingis a constructive, synthetic act, and aconstruction that proves adequate (viable) inthe ``world of experience'', in life, in thepraxis of living, is, to the constructivistmind, knowledge. It is the practice of livingwhich provides the orienting standards forconstructivist thinking and its judgments ofviability. The question of truth is replaced bythe question of viability, and viabilitydepends on the (right) kind of experiential fit.

90 citations



Proceedings Article
04 Aug 2001
TL;DR: In this paper, the problem of generating explanations is formalized as rewriting with confluent and terminating rewrite systems, where the set of minimal explanations can be viewed as a succinct representation of all explanations.
Abstract: A long outstanding problem for abduction in logic programming has been on how minimality might be defined. Without minimality, an abductive procedure is often required to generate exponentially many subsumed explanations for a given observation. In this paper, we propose a new definition of abduction in logic programming where the set of minimal explanations can be viewed as a succinct representation of the set of all explanations. We then propose an abductive procedure where the problem of generating explanations is formalized as rewriting with confluent and terminating rewrite systems. We show that these rewrite systems are sound and complete under the partial stable model semantics, and sound and complete under the answer set semantics when the underlying program is so-called odd-loop free. We discuss an application of abduction in logic programming to a problem in reasoning about actions and provide some experimental results.

44 citations


Journal ArticleDOI
TL;DR: In this article, the authors describe a model of multicausal abductive reasoning that makes two predictions regarding the use of the current explanation, i.e., if a simple hypothesis can account for new data, then the current hypothesis is not used to explain new evidence.

44 citations


Book ChapterDOI
17 Sep 2001
TL;DR: The A-system is a new system for performing abductive reasoning within the framework of Abductive Logic Programming (ALP), founded by work on two earlier systems ACLP and SLDNFA(C).
Abstract: The A-system [4] is a new system for performing abductive reasoning within the framework of Abductive Logic Programming (ALP). The principles behind the system are founded by work on two earlier systems ACLP [2],[3] and SLDNFA(C) [1],[6]. The basic inference mechanism of the system combines abductive logicp rogramming and constraint logicp rogramming. In its computation it reduces the high level specification of the problem and goal at hand to a lower level constraint store. This constraint store is managed by an efficient constraint solver returning information to the abductive reduction process in order to help this in its search for a solution.

35 citations


Book ChapterDOI
01 Jan 2001
TL;DR: In this paper, the authors explore common descriptions of design, such as abductive reasoning, construction, ill-defined problem solving skills, or wicked problem-solving skills, and argue that they are not always sharp enough to distinguish design from other types of problem solving.
Abstract: Publisher Summary This chapter explores common descriptions of design—that design involves abductive reasoning, construction, ill-defined problem solving skills, or wicked problem solving skills—and argues that they are not always sharp enough to both distinguish design from other types of problem solving and unite design across different design-related disciplines. It is proposed that research and education focusing on mid-level constructs, such as analogy, may help to solidify design research by yielding results that cross design disciplines. The dominant approach in design studies has been to claim design as a unique type of problem solving with unique features. Along these lines, a major goal of design studies has been to verify and catalog these features, many of which deal with the structure of design problems and attributes of design behavior. An alternative to starting with complete and distinct notions of design is to treat design problem solving as problem solving in general, and focus individually on the reasoning processes that are involved. This does not mean taking a bottom–up cognitive approach in which basic cognitive parameters, like memory and attention are clarified and integrated into more complex processes. The idea, rather, is to focus on the types of reasoning—on things like analogy, coherence seeking, mental simulation, dynamic modeling, argumentation, decision making, and so forth—as they contribute to the generation of new artifacts.

29 citations


Journal ArticleDOI
TL;DR: In this paper, the problem of how to track a student's real progress in learning, which cannot be absolutely quantified at any given point as a result of a particular intervention, is addressed.
Abstract: This article grapples with the problem of how to track a student's real progress in learning, which cannot be absolutely quantified at any given point as a result of a particular intervention. Results are presented for a long-term qualitative and quantitative classroom study, during which the method of concept mapping was applied and interpreted in light of the semiotic paradigm developed by Charles Sanders Peirce (1931–1958). Peirce's semiotic paradigm was thought to have sufficient intellectual rigour and flexibility to give new access to the multiplicity of processes at work in the learning environment.

25 citations


Journal ArticleDOI
TL;DR: Some criteria to simplify the explanations of Bayesian belief networks in such a way that the resulting configurations are still accounting for the observed facts are proposed.
Abstract: Abductive inference in Bayesian belief networks is intended as the process of generating the K most probable configurations given an observed evidence. These configurations are called explanations and in most of the approaches found in the literature, all the explanations have the same number of literals. In this paper we propose some criteria to simplify the explanations in such a way that the resulting configurations are still accounting for the observed facts. Computational methods to perform the simplification task are also presented. Finally the algorithms are experimentally tested using a set of experiments which involves three different Bayesian belief networks.

24 citations


Journal ArticleDOI
TL;DR: In this article, the structure of defeasible arguments such as 'If Bob has red spots, Bob has the measles; Bob had red spots; therefore Bob had the measles' is examined.
Abstract: This article concerns the structure of defeasible arguments like: 'If Bob has red spots, Bob has the measles; Bob has red spots; therefore Bob has the measles.' The issue is whether such arguments have the form of modus ponens or not. Either way there is a problem. If they don't have the form of modus ponens, the common opinion to the contrary taught in leading logic textbooks is wrong. But if they do have the form of modus ponens, doubts are raised about the conventional dogma that all arguments having the form of modus ponens are deductively valid. By carefully examining arguments on both sides of the issue, reasonable doubts are raised about the view that all arguments having a modus ponens form are valid.

Proceedings ArticleDOI
28 May 2001
TL;DR: Cognitive modelling is introduced - a novel logical approach based on a highly developed logical theory of actions, i.
Abstract: Recent work in animated human-like agent has made impressive progress toward generating agents with believable appearances and realistic motions for the interactive applications of inhabited virtual worlds. It remains difficult, however, to instruct animated agents to perform specific tasks or take initiatives. This paper addresses the challenge of instructability by introducing cognitive modelling - a novel logical approach based on a highly developed logical theory of actions, i. e. Event Calculus. Cognitive models go beyond behavioural models in that they govern an agent's behaviour by reasoning about its knowledge, actions and events. To facilitate the construction of the language (BSL) from the event calculus formalism. Using BSL, we can specify and agent's domain knowledge, design behaviour controllers and then control the agent's behaviour in terms of goals and/ or goals and/ or user's instructions. This approach allows agent's behaviours to be specified and controlled more naturally and intuitively, more succinctly and at a much highter level of abstraction than would otherwise be possible. It als provides a logical characterisation of planning via abductive reasoning process. Furthermore, we integrate sensing capability into our underlying theoretical framework, thus enabling animated agents to generate appropriate behaviour even in complex, dynamic virtual worlds. An animated human- like interface agent for virtual environments is used to demonstrate the approach. The architecture for implementing the approach is also described.

Journal ArticleDOI
TL;DR: The design and implementation of an air-crew assignment system, for producing and refining a solution to this problem, based on the artificial intelligence principles and techniques of abduction as captured by the framework of abductive logic programming (ALP).
Abstract: This article presents the design and implementation of an air-crew assignment system, for producing and refining a solution to this problem, based on the artificial intelligence principles and techniques of abductive reasoning as captured by the framework of abductive logic programming (ALP). The system offers a high level of flexibility in addressing both the tasks of crew scheduling and rescheduling. Itcan be used to generate a valid and good quality initial solution and then help the human operators adjust and refine further this solution in order to meet extra requirements of the problem. These additional needs can arise either due to new foreseen requirements that the company wants to have or experiment with for a particular period in time, or due to unexpected events that have occurred while the solution (crew-roster) is in operation. This work shows the ability and flexibility of abduction, and, more specifically, of ALP, in tackling problems of this type with complex and changing requirements.

Journal ArticleDOI
TL;DR: LAILA is described, a language that can be used by logic-based agents capable of abductive reasoning, by enabling them to express at a high level several ways to join and coordinate with one another.

Journal ArticleDOI
TL;DR: This paper describes an approximate method to perform partial abductive inference in BBNs based on the simulated annealing (SA) algorithm, which can be applied to multiple connected networks and for any value of K .

Book ChapterDOI
26 Nov 2001
TL;DR: This paper discusses abductive reasoning--that is, reasoning in which explanatory hypotheses are formed and evaluated; and uses multiadjoint logic programming to introduce and study a model of abduction problem.
Abstract: Multi-adjoint logic programs has been recently introduced [9, 10] as a generalization of monotonic logic programs [2, 3], in that simultaneous use of several implications in the rules and rather general connectives in the bodies are allowed.This paper discusses abductive reasoning--that is, reasoning in which explanatory hypotheses are formed and evaluated. To model uncertainty in human cognition and real world applications; we use multiadjoint logic programming to introduce and study a model of abduction problem.


Journal ArticleDOI
TL;DR: This paper presents a qualitative approach of classical probability theory in the particular case where the set of probability degrees is replaced by a totally ordered set of symbolic values and proposes an axiomatic approach.

Book ChapterDOI
01 Jul 2001
TL;DR: GenePath is an automated system for reasoning on genetic networks, wherein a set of genes have various influences on one another and on a biological outcome, and a combination of constraint satisfaction and qualitative reasoning produces a small set of plausible networks.
Abstract: GenePath is an automated system for reasoning on genetic networks, wherein a set of genes have various influences on one another and on a biological outcome It acts on a set of experiments in which genes are knocked out or overexpressed, and the outcome of interest is evaluated Implemented in Prolog, GenePath uses abductive inference to elucidate network constraints based on prior knowledge and experimental results Two uses of the system are demonstrated: synthesis of a consistent network from abduced constraints, and qualitative reasoning-based approach that generates a set of networks consistent with the data In practice, illustrated by an example using Dictyostelium aggregation, a combination of constraint satisfaction and qualitative reasoning produces a small set of plausible networks


Book ChapterDOI
03 Dec 2001
TL;DR: This work introduces an abductive method for coherent composition of distributed data that is more expressive (thus more general) than any other existing application for coherent data integration.
Abstract: We introduce an abductive method for coherent composition of distributed data. Our approach is based on an abductive inference procedure that is applied on a meta-theory that relates different, possibly inconsistent, input databases. Repairs of the integrated data are computed, resultingin a consistent output database that satisfies the meta-theory. Our framework is based on the A-system, which is an abductive system that implements SLDNFA-resolution. The outcome is a robust application that, to the best of our knowledge, is more expressive (thus more general) than any other existing application for coherent data integration.

Proceedings ArticleDOI
02 Dec 2001
TL;DR: The proposed approach demonstrates that the integration of abductive CBR and deductiveCBR is of practical significance in problem solving such as system diagnosis and analysis.
Abstract: Abduction and deduction play a fundamental role in problem solving. The paper extends abduction and deduction to abductive CBR (case-based reasoning) and deductive CBR and shows that abductive CBR and deductive CBR can be integrated in problem solving. Then it provides a unified formalization for integration of abduction, abductive CBR, deduction and deductive CBR. The paper also investigates abductive case retrieval and deductive case retrieval using similarity relations, fuzzy similarity relations and similarity metrics. The proposed approach demonstrates that the integration of abductive CBR and deductive CBR is of practical significance in problem solving such as system diagnosis and analysis.

Journal ArticleDOI
TL;DR: This article describes an expertise model in the domain of plant health, which was obtained applying the CommonKADS methodology and the abductive method proposed is sufficiently generalized to be applied to different domains.
Abstract: Generic reasoning models facilitate the construction of knowledge-based systems for solving complex problems, such as therapy planning in an agricultural context. The basic features of these models are problem-solving methods, which are proposed to carry out the tasks ordinarily done by experts in solving a specific problem. This article describes an expertise model in the domain of plant health, which was obtained applying the CommonKADS methodology. The most important result is the abductive method proposed for supplying a solution to treatment problems in domains where there is no protocol for therapy planning tasks. The description of the method is based on the algebraic formulation of a domain-independent general treatment. Therefore, the abductive method proposed is sufficiently generalized to be applied to different domains.

01 Jan 2001
TL;DR: In this article, a discussion of the relationship between question answering and abduction is presented, where the question is of the form Why P?, where P is an observation or fact that has come to an agent's attention.
Abstract: Abduction can be viewed as self-questioning, where the search for an explanation is analogous to answering a “why” question. The question is of the form Why P?, where P is an observation or fact that has come to an agent’s attention. An abductive hypothesis is a possible answer to the question Why P? When abduction is viewed as a type of question answering, abductive hypotheses can be seen as a subset of hypothetical (or conditional) answers. These answers are comprised of two components: a hypothesis that is consistent with an agent’s knowledge, but whose validity can not be determined, and an associated specific or generic answer, whose correctness hinges on the validity of the associated hypoth esis. Hypothetical answering and abductive reasoning are both ways of coping with incomplete information. The difference between the two lies in the way in which the hypothetical information is employed. In abduction, a reasoner chooses to believe an abductive hypothesis based on factors such as belonging to a set of abducibles and informativeness. In question answering (excepting the case of self-questioning), t he answering agent is reasoning in service of a distinct questioner, and has no reason to adopt or consider the hypothesis associated with a hypothetical answer as an update to its knowledge base. It is in the interest of an abductive reasoner to place higher value an abductive hypothesis that is more informative than other abductive hypotheses. This is true for the questioner also. The difference between abduction and question answering is that the answering agent does not customarily have access to the questioner’s knowledge base. This means that the informativeness of a hypothetical answer can not be determined by an answering agent, making it necessary to provide hypothetical answers at all possible levels of generality. It is generally not possible for the answering agent to measure the “informativeness” of an abductive hypothesis. This paper presents a discussion of the relationship between question answering and abduction. 2 Background

Proceedings ArticleDOI
01 Sep 2001
TL;DR: This paper describes several methods of creating abductive explanations, exploring term reweighting and query reformulation techniques and demonstrating their suitability for relevance feedback.
Abstract: In this paper we report on a series of experiments designed to investigate query modification techniques motivated by the area of abductive reasoning. In particular we use the notion of abductive explanation, explanations being a description of data that highlight important features of the data. We describe several methods of creating abductive explanations, exploring term reweighting and query reformulation techniques and demonstrate their suitability for relevance feedback.

Book ChapterDOI
TL;DR: It is shown that by abductive inference it is possible to learn enough simple phonotactics to distinguish words from non-words for a simplified set of Dutch, the monosyllables, by comparing different machine learning techniques on linguistic data.
Abstract: We report on experiments which demonstrate that by abductive inference it is possible to learn enough simple phonotactics to distinguish words from non-words for a simplified set of Dutch, the monosyllables. The monosyllables are distinguished in input so that segmentation is not problematic. Frequency information is withheld as is negative data. The methods are all tested using ten-fold cross-validation as well as a fixed number of randomly generated strings. Orthographic and phonetic representations are compared. The work presented in this chapter is part of a larger project comparing different machine learning techniques on linguistic data.

Journal ArticleDOI
01 Feb 2001
TL;DR: Diagnostic problem solving aims to account for, or explain, a malfunction of a system (human or other), and any plausible potential diagnostic solution must satisfy some minimum criteria relevant to the application.
Abstract: Diagnostic problem solving aims to account for, or explain, a malfunction of a system (human or other). Any plausible potential diagnostic solution must satisfy some minimum criteria relevant to the application. Often there will be several plausible solutions, and further criteria will be required to select the “best” explanation. Expert diagnosticians may employ different, complex criteria at different stages of their reasoning. These criteria may be combinations of some more primitive criteria, which therefore should be represented separately and explicitly to permit their flexible and transparent combined usage. In diagnostic reasoning there is a tight coupling between the formation of potential solutions and their evaluation. This is the essence of abductive reasoning. This article presents an abductive framework for diagnostic problem solving. Time-objects, an association of a property and an existence, are used as the representation formalism and a number of primitive, general evaluation criteria into which time has been integrated are defined. Each criterion provides an intuitive yardstick for evaluating the space of potential solutions. The criteria can be combined as appropriate for particular applications to define plausible and best explanations. The central principle is that when time is diagnostically significant, it should be modeled explicitly to enable a more accurate formulation and evaluation of diagnostic solutions. The integration of time and primitive evaluation criteria is illustrated through the Skeletal Dysplasias Diagnostician (SDD) system, a diagnostic expert system for a real-life medical domain. SDD's notions of plausible and best explanation are reviewed so as to show the difficulties in formalizing such notions. Although we illustrate our work by medical problems, it has been motivated by consideration of problems in a number of other domains (fermentation monitoring, air and ground traffic control, power distribution) and is intended to be of wide applicability.

Journal ArticleDOI
TL;DR: This paper tries to enlarge the class of solvable problems by reducing the size of the graphical structure in which probabilities propagation will be carried out by presenting a method that yields a (forest of) clique tree from which the variables of the explanation set have been removed, but in which configurations of these variables can be evaluated.

Dissertation
01 Jan 2001
TL;DR: The authors argue that the positivistic use o f logical objectivity epitomized in constrained inference and consequence relations, as a model for rationality is a narrower conception of rationality and that such a conception only holds, and relatively so, in formal aspects of discourses in science.
Abstract: This study attempts to show that the positivistic use o f logical objectivity epitomized in constrained inference and consequence relations, as a model for rationality is a narrower conception of rationality. The study argues that such a conception only holds, and relatively so, in formal aspects of discourses in science. The study marshals the argument that an absolute exclusion of sentiments and morality from human existence, the implication of the positivistic ideal o f rationality, is an arbitrary and unwarranted reductionist undertaking that begs the question as to what rationality is. The postulation o f the study is that sentiments and morality are irreducible aspects of human social life and any conception and ascription of rationality to humans must ipso facto take cognizance o f these aspects. It thus suffices that contrary to positivistic thinking, the wider and more comprehensive conception of rationality is one which transcends mere logic and includes morality and sentiments in its theoretical construct. The study argues that the positivistic conception, though valid in the light o f cognitive dictates and the scientific assumptions of causality and the uniformity of nature, transgresses ontological confines and imperatives. Such a conception is out o f line with human nature and the essence of human social life. It can only apply to humans secundum quid but not simpliciter because it at best merely epitomizes artificial intelligence. Therefore, the positivistic conception o f rationality relatively defines scientific rationality but should not be taken to define rationality in general. Following the positivistic ideal o f logical objectivity, legal positivism excludes morality and sentiments from its conception of the law. However, the study attempts to rebut this thinking on grounds that morality and sentiments are irreducible aspects of human social life. If man is a rational

01 Jan 2001
TL;DR: The approach to Cognitive Robotics has been to apply abductive reasoning procedures using the Event Calculus, an extension to First Order Predicate Calculus (FOPC), to provide a unified view of several related mobile robotics tasks: sensor data assimilation, map-building and planning.
Abstract: This paper describes some aspects of recent and ongoing work in the area of Cognitive Robotics in the Department of Electrical and Electronic Engineering at Imperial College. Our approach to Cognitive Robotics has been to apply abductive reasoning procedures using the Event Calculus, an extension to First Order Predicate Calculus (FOPC), to provide a unified view of several related mobile robotics tasks: sensor data assimilation, map-building and planning. Cognitive robotics depends on an explicit declarative representation. While this greatly facilitates reasoning about domain knowledge, it comes with an extra c omputational overhead. This is the basis of the semantic knife-edge, maintaining a delicate balance between expressivity and efficient implementation.