Showing papers on "Abductive reasoning published in 2005"

Abductive reasoning in logistics research

Abstract: Purpose – To construct a framework for exploring and discussing the use of different research approaches – deductive, inductive and abductive – in logistics.Design/methodology/approach – A review of research articles in three major logistics journals (International Journal of Logistics Management, International Journal of Physical Distribution & Logistics Management and Journal of Business Logistics) from 1998 to 2002.Findings – Recognizes the dominance of deductive research in logistics, and the need for more inductive and, in particular, abductive research for theory development. Discusses the use of the abductive research approach in logistics.Research limitations/implications – Keywords searches led to a small sample size; more thorough content analysis is needed to apply the findings from the constructed framework.Practical implications – Useful source of information on the three different research approaches, their possibilities and implications for research.Originality/value – The abductive researc...

An Abductive Theory of Scientific Method.

Abstract: A broad theory of scientific method is sketched that has particular relevance for the behavioral sciences. This theory of method assembles a complex of specific strategies and methods that are used in the detection of empirical phenomena and the subsequent construction of explanatory theories. A characterization of the nature of phenomena is given, and the process of their detection is briefly described in terms of a multistage model of data analysis. The construction of explanatory theories is shown to involve their generation through abductive, or explanatory, reasoning, their development through analogical modeling, and their fuller appraisal in terms of judgments of the best of competing explanations. The nature and limits of this theory of method are discussed in the light of relevant developments in scientific methodology.

Exploratory Factor Analysis, Theory Generation, and Scientific Method.

Abstract: This Chapter examines the methodological foundations of exploratory factor analysis (EFA) and suggests that it is properly construed as a method for generating explanatory theories. In the first half of the chapter, it is argued that EFA should be understood as an abductive method of theory generation that exploits an important precept of scientific inference known as the principle of the common cause. This characterization of the inferential nature of EFA coheres well with its interpretation as a latent variable method. The second half of the chapter outlines a broad theory of scientific method in which abductive reasoning figures prominently. It then discusses a number of methodological features of EFA in the light of that method. It is concluded that EFA, as a useful method of theory generation that can be profitably employed in tandem with confirmatory factor analysis and other methods of theory evaluation.

Robust textual inference via learning and abductive reasoning

Abstract: We present a system for textual inference (the task of inferring whether a sentence follows from another text) that uses learning and a logical-formula semantic representation of the text More precisely, our system begins by parsing and then transforming sentences into a logical formula-like representation similar to the one used by (Harabagiu et al, 2000) An abductive theorem prover then tries to find the minimum "cost" set of assumptions necessary to show that one statement follows from the other These costs reflect how likely different assumptions are, and are learned automatically using information from syntactic/semantic features and from linguistic resources such as WordNet If one sentence follows from the other given only highly plausible, low cost assumptions, then we conclude that it can be inferred Our approach can be viewed as combining statistical machine learning and classical logical reasoning, in the hope of marrying the robustness and scalability of learning with the preciseness and elegance of logical theorem proving We give experimental results from the recent PASCAL RTE 2005 challenge competition on recognizing textual inferences, where a system using this inference algorithm achieved the highest confidence weighted score

Argumentation Methods for Artificial Intelligence in Law

Abstract: Use of argumentation methods applied to legal reasoning is a relatively new field of study Many vitally important problems of legal reasoning can be profitably studied in light of these new methods, even if they cannot all be solved in any single monograph This book provides a survey of the leading problems, and outlines how future research using argumentation-based methods show great promise of leading to useful solutions The problems studied include not only these of argument evaluation and argument invention, but also analysis of specific kinds of evidence commonly used in law, like witness testimony, circumstantial evidence, forensic evidence and character evidence New tools for analyzing these kinds of evidence are introduced, like argument diagramming, abductive reasoning, an analysis of conditional relevance and a new dialectical model of explanation

Applying COGEX to recognize textual entailment

Abstract: This paper describes the system that LCC has devised to perform textual entailment recognition for the PASCAL RTE Challenge. Our system transforms each text-hypothesis pair into a two-layered logic form representation that expresses the lexical, syntactic, and semantic attributes of the text and hypothesis. A large set of natural language axioms are constructed for each text-hypothesis pair that help connect concepts in the hypothesis with concepts in the text. Our natural language logic prover is then used to prove entailment through abductive reasoning. The system's performance in the challenge resulted in an accuracy of 55%.

The SCIFF abductive proof-procedure

Abstract: We propose an operational framework which builds on the classical understanding of abductive reasoning in logic programming, and extends it in several directions. The new features include the ability to reason with a dynamic knowledge base, where new facts can be added anytime, the ability to generate expectations about such new facts occurring in the future (forecasting), and the process of confirmation/disconfirmation of such expectations.

An Abductive Theory of Scientific Reasoning

Abstract: Abstract More than a hundred years ago, the American philosopher C. S. Peirce suggested the idea of pragmatism as a logical criterion to analyze what words and concepts express through their practical meaning. Many words have been spent on creative processes and reasoning, especially in the case of scientific practices. In fact, philosophers have usually offered a number of ways of construing hypotheses generation, but all aim at demonstrating that the activity of generating hypotheses is paradoxical, illusory or obscure, and thus not analyzable. The “computational turn” gave us a new way to understand creative processes in a strictly pragmatic sense. Artificial Intelligence and Cognitive Science tools allow us to test concepts and ideas previously conceived in abstract terms. It is in the perspective of these actual models that we find the central role of abduction in the explanation of creative reasoning in science. What I call theoretical abduction (sentential and model-based) certainly illustrates much of what is important in abductive reasoning, especially the objective of selecting and creating a set of hypotheses that are able to dispense good (preferred) explanations of data, but fails to account for many cases of explanation occurring in science or in everyday reasoning when the exploitation of the environment is crucial. The concept of manipulative abduction is devoted to capture the role of action in many interesting situations: action provides otherwise unavailable information that enables the agent to solve problems by starting and performing a suitable abductive process of generation or selection of hypotheses. Many external things, usually inert from the epistemological point of view, can be transformed into what I call epistemic mediators, which are illustrated in the last part of the paper.

Abduction: Between Subjectivity and Objectivity

Abstract: Charles Sanders Peirce (1839–1914), creator of pragmatism, was a polymath. His contributions include such diverse areas of research as meteorology, experimental psychology, geodesics, astronomy, mathematical economy, philosophy of mathematics, theory of gravity, linguistics, history and philosophy of science, and the history and philosophy of logic (Fisch 1986: 376). In spite of the breadth of his academic purview, many Peirce scholars compress his work into the field of logic, which, for Peirce, was semiotic (Houser 1997: 1). There is some merit to this approach, since, according to Peirce, logic in its various forms includes all of the disciplines with which he was involved. Along with Gottlob Frege, Bertrand Russell, and David Hilbert, Peirce is considered one of the founders of modern logic (Lukasiewicz 1970: 111; Barwise and Etchemendy 1995: 211; Quine 1995: 23; Hintikka and Hilpinen 1997: ix). Independently of Frege, he developed the concepts of quantification and quantifying logic (Hintikka and Hilpinen 1997: ix; Quine 1985: 767, 1995: 31; Putnam 1982: 297). He was author of the terms ‘First Order Logic’ (Putnam 1988: 28), and ‘Trivalent Logic’ (Fisch and Turquette 1966; Lane 2001). He also anticipated Henry She¤er’s ‘Stroke Function’ by more than 30 years (W 4: 218–221; Houser 1997: 3); worked with what later came to be known as Claude Shannon’s correspondence between truth functions and electrical circuitry (W 5: 421–422; Gardner 1982); and developed a logical notation using topological forms (existential graphs) that anticipated hybrid systems of notation based on graphs, diagrams, and frames (Roberts 1973; Shin 1994, 2002; Barwise and Etchemendy 1995; Allwein and Barwise 1996; Hammer 1994, 1995). As if this were not enough, one of his most original contributions consists of his development of a logic of discovery based on the concept of abductive inference, as outlined by various scholars (Bernstein 1980;

Creativity: Surprise and Abductive Reasoning

Abstract: This paper investigates creativity focusing on the nature of abductive reasoning, as originally formulated by Peirce, situating it in the context of the theory of self -organization. An ancient question will be addressed: is it appropriate to investigate creative processes from a mechanistic perspective or do they involve subjective elements which cannot - in principle - be investigated from a mechanistic view? This question will guide our investigation, which has as an initial hypothesis that creativity starts with surprise and involves a self -organizing process in which abductive reasoning occurs allowing the expansion of well-structured set of beliefs. This process is considered a part of the establishment of habits in self -organizing systems. We argue that a deeper understanding of how self-organizing processes involving abductive reasoning may take place in creative systems could elucidate the complex debate about the mechanical versus nonmechanical ingredients of creativity.

Towards a Unifying Theory of Logical and Probabilistic Reasoning

Abstract: Logic and probability theory have both a long history in science. They are mainly rooted in philosophy and mathematics, but are nowadays important tools in many other fields such as computer science and, in particular, artificial intelligence. Some philosophers studied the connection between logical and probabilistic reasoning, and some attempts to combine these disciplines have been made in computer science, but logic and probability theory are still widely considered to be separate theories that are only loosely connected. This paper introduces a new perspective which shows that logical and probabilistic reasoning are no more and no less than two opposite extreme cases of one and the same universal theory of reasoning called probabilistic argumentation. 1

Abductive Case-Based Reasoning

Abstract: This article introduces abductive case-based reasoning (CBR) and attempts to show that abductive CBR and deductive CBR can be integrated in clinical process and problem solving. Then it provides a unified formalization for integration of abduction, abductive CBR, deduction, and deductive CBR. This article 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 deductive CBR and abductive CBR is of practical significance in problem solving such as system diagnosis and analysis, and will facilitate research of abductive CBR and deductive CBR. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 957–983, 2005.

Abductive logic programs with penalization: semantics, complexity and implementation

Abstract: Abduction, first proposed in the setting of classical logics, has been studied with growing interest in the logic programming area during the last years. In this paper we study abduction with penalization in the logic programming framework. This form of abductive reasoning, which has not been previously analyzed in logic programming, turns out to represent several relevant problems, including optimization problems, very naturally. We define a formal model for abduction with penalization over logic programs, which extends the abductive framework proposed by Kakas and Mancarella. We address knowledge representation issues, encoding a number of problems in our abductive framework. In particular, we consider some relevant problems, taken from different domains, ranging from optimization theory to diagnosis and planning; their encodings turn out to be simple and elegant in our formalism. We thoroughly analyze the computational complexity of the main problems arising in the context of abduction with penalization from logic programs. Finally, we implement a system supporting the proposed abductive framework on top of the DLV engine. To this end, we design a translation from abduction problems with penalties into logic programs with weak constraints. We prove that this approach is sound and complete.

Theory-building with system dynamics : Principles and practices

Abstract: System Dynamics is a discipline for the modeling, simulation and control of complex dynamic systems. In this contribution, the methodology of System Dynamics-based modeling is argued to be a powerful and rigorous approach to theory-building. The strength of the pertinent process of theory development lies in its high standards for model validation, and in a combination of abductive reasoning with induction and deduction. The argument of the paper is underpinned by an application of System Dynamics to the elaboration of a theory in the new field of Cultural Dynamics.

A creative abduction approach to scientific and knowledge discovery

Abstract: In this paper we explore the range of applicability of abductive reasoning for knowledge discovery. In particular, we discuss a novel form of abduction, called creative abduction, where new knowledge is generated in the process of explaining observed events, and demonstrate its relevance to knowledge discovery. The main contribution of this paper is twofold: First, we show that creative abduction can be used to infer a disposition explaining local temporal regularities. In the presence of multiple correlated regularities, this form abduction may significantly unify a given corpus of knowledge, corresponding to theory formation in scientific discovery. Second, we present a weaker form of creative abduction that infers a goal (e.g. interest) from simple 'condition-effect' rules called 'transitions'. If multiple transitions are correlated, the weaker form of creative abduction can be used to identify, e.g. clusters of Web users, as done in Web usage mining. We will focus on the formal underpinnings of this new form of abduction that seems readily applicable to a wide range of practical knowledge discovery problems.

Peirce and the Art of Reasoning.

Abstract: Drawing on Charles Peirce’s descriptions of his correspondence course on the “Art of Reasoning,” I argue that Peirce believed that the study of logic stands at the center of a liberal arts education. However, Peirce’s notion of logic included much more than the traditional accounts of deduction and syllogistic reasoning. He believed that the art of reasoning required a study of both abductive and inductive inference as well the practice of observation and imagination. Employing these other features of logic, his course foreshadowed a number of developments in twentieth century educational theory: the belief that non-traditional students should be educated, the claim that the art of reasoning (or critical reasoning) was important to all theoretical practices, and that the art of reasoning was important to the overall growth of a person. The upshot is that Peirce’s course in the art of reasoning should make us reconsider making logic courses, under Peirce’s broad conception of logic, required courses in high school and higher education.

Abductive inference in bayesian networks: finding a partition of the explanation space

Abstract: This paper proposes a new approach to the problem of ob- taining the most probable explanations given a set of observations in a Bayesian network. The method provides a set of possibilities ordered by their probabilities. The main novelties are that the level of detail of each one of the explanations is not uniform (with the idea of being as simple as possible in each case), the explanations are mutually exclusive, and the number of required explanations is not fixed (it depends on the particular case we are solving). Our goals are achieved by means of the construction of the so called explanation tree which can have asym- metric branching and that will determine the different possibilities. This paper describes the procedure for its computation based on information theoretic criteria and shows its behaviour in some simple examples.

Reasoning in Engineering Design

Abstract: Peirce, the American philosopher of the late 19th and early 20th centuries, is credited with first observing the triple of reasoning (deductive, inductive, and abductive). These three types of reasoning are discussed as they relate to the engineering design process. The reasoning classes are based upon distinctions between what is given and what is derived with respect to the grounds, the warrants, and the conclusions. Simple definitions are synthesized that agree well with the literature, while distinctions are made where overlapping and often conflicting definitions are found. This distinction leads to the need for separating abductive reasoning and retroductive reasoning. A generalized description of design agrees well with the definition for retroductive reasoning, as is demonstrated in this paper. A brief survey of “traditional” design reasoning methods (rule based reasoning, analogy based reasoning, simulation based reasoning, and constraint based reasoning) is developed to show that these design methods are equivalent or decomposable into the fundamental reasoning classes. This paper provides a discussion in a common framework for comparing design reasoning strategies found in automation systems based upon the fundamental classes.Copyright © 2005 by ASME

Subjective probabilities as basis for scientific reasoning

Abstract: Bayesianism is the position that scientific reasoning is probabilistic and that probabilities are adequately interpreted as an agent’s actual subjective degrees of belief, measured by her betting behaviour. Confirmation is one important aspect of scientific reasoning. The thesis of this paper is the following: If scientific reasoning is at all probabilistic, the subjective interpretation has to be given up in order to get right confirmation – and thus scientific reasoning in general.

Bayesian networks inference advanced algorithms for triangulation abd partical abduction

Abstract: Within the field of Artificial Intelligence (AI), Expert Systems stand out due to their proven utility and their numerous applications, These systems, which try to imitate human experts in a certain knowledge domain, will need to manage the uncertainty inherent in most real life problems One successful tool to treat uncertainty is the Probability Theory, which gives rise to Probabilistic Expert Systems (PES) Bayesian networks can be located in this PES framework They provide a quite powerful formalism that gives a representation of the modelled world, which is intuitive (graph structure) and adaptable (belief update) Another appealing feature is their capability of being constructed either by means of experts' contribution or automatically from data, or both In a general scheme of an Expert System, the Bayesian network (BN) is equivalent to the Knowledge Base indicating both variable relationships (presence/absence of graph arcs) and their strength (probability distributions) BNs answer queries also in the form of probabilities: given some observed facts, the user will want to know the resulting posterior probabilities for some other unobserved factors/variables of the problem That is what basically inference in Bayesian networks will attempt to do Moreover, the search of explanations for those given facts can also be of interest (abductive inference) Different and various inference methods, both approximate and exact, have been proposed in the literature Nevertheless, those using a secondary structure called junction or join tree are quite broadly applied The Join Tree (JT) is built from the corresponding BN and can be seen as the Inference Engine of the expert system The steps necessary to perform this construction are included in a process called compilation The complexity of compilation increases with the number of variables and depends on the graph structure Triangulation means a particular compilation stage that is practically unavoidable and

Unifying logical and probabilistic reasoning

Abstract: Most formal techniques of automated reasoning are either rooted in logic or in probability theory. These areas have a long tradition in science, particularly among philosophers and mathematicians. More recently, computer scientists have discovered logic and probability theory to be the two key techniques for building intelligent systems which rely on reasoning as a central component. Despite numerous attempts to link logical and probabilistic reasoning, a satisfiable unified theory of reasoning is still missing. This paper analyses the connection between logical and probabilistic reasoning, it discusses their respective similarities and differences, and proposes a new unified theory of reasoning in which both logic and probability theory are contained as special cases.

Abduction with hypotheses confirmation

Abstract: Abduction can be seen as the formal inference corresponding to human hypothesis making. It typically has the purpose of explaining some given observation. In classical abduction, hypotheses could be made on events that may have occurred in the past. In general, abductive reasoning can be used to generate hypotheses about events possibly occurring in the future (forecasting), or may suggest further investigations that will confirm or disconfirm the hypotheses made in a previous step (as in scientific reasoning). We propose an operational framework based on Abductive Logic Programming, which extends existing frameworks in many respects, including accommodating dynamic observations and hypothesis confirmation.

Abduction and Truthlikeness

Abstract: Earlier chapters deal with abductive inferences to explanations which are deductive or inductive-probabilistic. This more or less standard account has so far ignored the fact that explanatory and predictive success in science is often approximate. Therefore, the analysis of abduction should cover also approximate explanations, which is illustrated by Newton’s explanation of Kepler’s harmonic law (Sect. 8.1). The notions of approximate truth (closeness to being true), verisimilitude (closeness to complete qualitative or quantitative truth) and legisimilitude (closeness to the true law) are defined in Sect. 8.2. This leads us to generalize Peirce’s model of abduction to cases where the conclusion states that the best theory is truthlike or approximately true, with illustrations from idealized theories and models (Sect. 8.3). In a comparative formulation, if theory Y is a better explanation of the available evidence E than theory X, then conclude for the time being that Y is more truthlike than X. To justify such abduction, we need a method of estimating degrees of truthlikeness by their expected values. Another tool is the notion of probable approximate truth. Then, in order to answer to Laudan’s challenge, the probabilistic link between empirical success and truth has to be replaced with a fallible bridge from the approximate empirical success of a theory to its truthlikeness (Sect. 8.4). Section 8.5 gives some remarks on abductive belief revision, which is related to cases where the evidence is conflict with the theory. This theme extends Aliseda’s way of linking belief revision models with abductive reasoning.

Equivalence in abductive logic

Abstract: We consider the problem of identifying equivalence of two knowledge bases which are capable of abductive reasoning. Here, a knowledge base is written in either first-order logic or nonmonotonic logic programming. In this work, we will give two definitions of abductive equivalence. The first one, explainable equivalence, requires that two abductive programs have the same explainability for any observation. Another one, explanatory equivalence, guarantees that any observation has exactly the same explanations in each abductive framework. Explanatory equivalence is a stronger notion than explainable equivalence. In first-order abduction, explainable equivalence can be verified by the notion of extensional equivalence in default theories. In nonmonotonic logic programs, explanatory equivalence can be checked by means of the notion of relative strong equivalence. We also show the complexity results for abductive equivalence.

From Design to Self-organization, or: A Proper Structure for a Proper Function

Abstract: It is suggested that Charles Sanders Peirce's triadic semiotics provides a framework for a diagrammatic representation of a sign's proper structure. The action of signs is described at the logical and psychological levels. The role of (unconscious) abductive inference is analyzed, and a diagram of reasoning is offered. A series of interpretants transform brute facts into interpretable signs thereby providing human experience with value or meaning. The triadic structure helps in de-mystifying the relations between Penrose's three worlds when the latter are considered as constituting a semiotic triangle.

The Semiotic Universe of Abduction

Abstract: In the philosophical system of Charles Sanders Peirce (as emerges it, for example, from his writings collected in the volume Chance, Love and Logic, edited by M. R. Cohen [Peirce 1923]), ‘chance’, ‘love’ and ‘necessity’ indicate the three modes of development regulating evolution in the cosmos. Below we shall focus on the question of love, or Peirce’s agapasm, which is connected to the problem of abductive inference.

Abductive Reasoning in Peirce's and Davidson's Account of Interpretation

Abstract: Etude de la conception de l'interpretation comme processus inferentiel de selection et de formation d'une hypothese chez Peirce (in «Prolegomenes a une apologie du pragmatisme»), en tant qu'elle anticipe le modele de l'interpretation propose par Davidson (in «A nice derangement of epithaphs») en termes de transformation des theories premieres en theories admises. Comparant le role de la competence abductive dans le processus communicatif chez les deux theoriciens, l'A. montre qu'ils partagent la meme conception du raisonnement de la meilleure explication en tant que fondement de la comprehension, principe d'economie du discours et principe de charite