Showing papers on "Abductive reasoning published in 1998"

Case-Based Reasoning Technology

Abstract: Extending some Concepts of CBR - Foundations of Case Retrieval Nets.- Diagnosis and Decision Support.- Intelligent Sales Support with CBR.- Textual CBR.- Using Configuration Techniques for Adaptation.- CBR Applied to Planning.- CBR for Design.- CBR for Experimental Software Engineering.- CBR for Tutoring and Help Systems.- CBR in Medicine.- Methodology for Building CBR Applications.- Related Areas.

Seeking explanations: abduction in logic, philosophy of science and artificial intelligence

Abstract: In this dissertation I study abduction, that is, reasoning from an observation to its possible explanations, from a logical point of view. This approach naturally leads to connections with theories of explanation in the philosophy of science, and to computationally oriented theories of belief change in Artificial Intelligence. Many different approaches to abduction can be found in the literature, as well as a bewildering variety of instances of explanatory reasoning. To delineate our subject more precisely, and create some order, a general taxonomy for abductive reasoning is proposed in chapter 1. Several forms of abduction are obtained by instantiating three parameters: the kind of reasoning involved (e.g., deductive, statistical), the kind of observation triggering the abduction (novelty, or anomaly w.r.t. some background theory), and the kind of explanations produced (facts, rules, or theories). In chapter 2, I choose a number of major variants of abduction, thus conceived, and investigate their logical properties. A convenient measure for this purpose are so-called 'structural rules' of inference. Abduction deviates from classical consequence in this respect, much like many current non-monotonic consequence relations and dynamic styles of inference. As a result we can classify forms of abduction by different structural rules. A more computational analysis of processes producing abductive inferences is then presented in chapter 3, using the framework of semantic tableaux. I show how to implement various search strategies to generate various forms of abductive explanations. Our eventual conclusion is that abductive processes should be our primary concern, with abductive inferences their secondary 'products'. Finally, chapter 4 is a confrontation of the previous analysis with existing themes in the philosophy of science and artificial intelligence. In particular, I analyse two well-known models for scientific explanation (the deductive-nomological one, and the inductive-statistical one) as forms of abduction. This then provides them with a structural logical analysis in the style of chapter 2. Moreover, I argue that abduction can model dynamics of belief revision in artificial intelligence. For this purpose, an extended version of the semantic tableaux of chapter 3 provides a new representation of the operations of expansion, and contraction.

SLDNFA: An abductive procedure for abductive logic programs

Abstract: We present SLDNFA, an extension of SLDNF resolution for abductive reasoning on abductive logic programs. SLDNFA solves the floundering abduction problem: nonground abductive atoms can be selected. SLDNFA also provides a partial solution for the floundering negation problem. Different abductive answers can be derived from an SLDNFA refutation; these answers provide different compromises between generality and comprehensibility. Two extensions of SLDNFA are proposed that satisfy stronger completeness results. The soundness of SLDNFA and its extensions is proved. Their completeness for minimal solutions with respect to implication, cardinality, and set inclusion is investigated. The formalization of SLDNFA presented here is an update of an older version and does not rely on skolemization of abductive atoms.

Ontology of Tasks and Methods

Abstract: Much of the work on ontologies in AI has focused on describing some aspect of reality: objects, relations, states of affairs, events, and processes in the world. A goal is to make knowledge sharable, by encoding domain knowledge using a standard vocabulary based on the ontology. A parallel attempt at identifying the ontology of problem-solving knowledge has a goal of sharable problem-solving methods. For example, when one is dealing with abductive inference problems, the following are some of the terms that occur in the representation of problem-solving methods: hypotheses, explanatory coverage, evidence, likelihood, plausibility, composite hypothesis, etc. Method ontology is, in good part, goal- and method-specific. ``Generic Tasks,'' ``Heuristic Classification,'' ``Task-specific Architectures,'' ``Task-method Structures,'' ``Inference Structures'' and ``Task Structures'' are representative bodies of work in the knowledge-systems area that have focused on domain-independent problem-solving methods. However, connections have not been made to work that is explicitly concerned with domain ontologies. Making such connections is the goal of this paper. This paper is part review and part synthesis.

The Extraordinary Ordinary Powers of Abductive Reasoning

Abstract: The psychology of cognition has been influenced by semiotic models of representation, but little work has been done relating semiotics and the process of cognition proper. In this paper, I argue that the semiotically relevant concept of abduction is crucial for this effort. Abduction is identified as the ground-state, or default, mode of cognition. As such, it deals with the issue of reasoning toward meaning and away from what Peirce called `genuine doubt'. In this fashion, abduction is shifted from being solely a logical and semiotic concept to a psychological concept. Abduction is first examined historically, and then further compared to the more traditional reasoning modes of deduction and induction to show how all three are necessary for any complete model of cognition and research into cognition. Different modes of abduction, including detection, diagnosis and divination, and different directions of abductive research in psychology are detailed.

Abductive Analysis of Modular Logic Programs

Abstract: We introduce a practical method for abductive analysis of modular logic programs. This is obtained by reversing the deduction process, which is usually applied in static-dataflow analysis of logic programs, on generic, possibly abstract, domains for analysis. The approach is validated in the framework of abstract interpretation. The abduced information provides an abstract specification for program modules which can be of assistance both in top-down development of programs and in compile-time optimization. To the best of our knowledge this is the first application of abductive reasoning in dataflow analysis of logic programs.

Automated reasoning with a constraint-based metainterpreter

Abstract: Using constraint logic techniques, it is made possible to use a well-known metainterpreter backwards as a device for generating programs. A metainterpreter is developed, which provides a sound and complete implementation of the binary demo predicate. Based on it, a general methodology for automated reasoning is proposed and it turns out that a wide range of reasoning tasks, normally requiring different systems, can be defined in a concise manner in this framework. Examples are shown of abductive and inductive reasoning in the usual first-order setting as well as in contexts of default reasoning and linear logic. Furthermore, examples of diagnosis and natural language analysis are shown.

A logic for reasoning about action, preference, and commitment

Abstract: In this paper, we study the logical semantics of actions, preference, and commitment. The semantics is based on the possible world models for dynamic logic of actions. A binary preference relation between possible worlds is associated with the model. Then the preference between actions are determined by comparing that of their consequences. While the preference will influence the agent’s choice of actions, commitment is a kind of actions that will change the agent’s preference. The integrated semantics make it possible to express and reasoning about the mutual relationship among these mental attitudes in a common logic.

Reflection as a Means of Developing Expertise in Problem Solving, Decision Making, and Complex Thinking of Designers.

Abstract: This paper focuses on reflection and reflective thinking as a means of developing expertise in instructional designers. The need for the reflective instructional designer is discussed, and reflective thinking is examined from several perspectives, i.e., controlled thinking, tacit knowledge, epistemic assumption, abductive reasoning, willingness to act, and social practice. A reflective thinking model made up of the following five phases is then described: problem recognition; problem clarification; hypothesis or suggestion formation and modification; mental elaboration of suggestions; and actions taken on the basis of the best supported hypothesis/suggestion. Six components explain the nature of reflective thinking: willingness to be thoughtful; willingness to recognize and understand the context in which assumptions and actions are formed; willingness to explore and imagine alternatives; understanding and accepting epistemic assumptions; using abductive inference; and exposure to a variety of interpretive considerations in dialogue with others. Three aspects of reflection identified by the preceding model--self-reconstruction; action-reconstruction; and social-reconstruction--are considered, and strategies for promoting reflective thinking in instructional designers based upon these aspects are offered. A figure illustrating the reflective thinking model is included. (Contains 26 references.) (DLS) ******************************************************************************** * Reproductions supplied by EDRS are the best that can be made * * from the original document. * ********************************************************************************

Abductive Reasoning for Syntactic Realization

Abstract: Abductive reasoning is •used in a bidirectional framework for syntactic realization and semantic interpretation. The use o f the framework is illustrated in a case study o f sentence generation, where different syntactic forms are generated depending on the status of discourse information. Examples are given involving three differen t syntactic constructions in German root clauses. 1 Pragmatics in Natural Language Generation The computational treatment of pragmatics in natural language generation is often---directly or indirect ly-• oriented around the Gricean maxims [Grice 7•5]. Their effects emerge from the pragmatic model of the generation system so that the generated text s satisfy these maxims. The texts should be a true characterization of a state of affairs, they should be as informative as possible, relevant, and perspicuous. While the first three maxims are related to what is said, the last maxim is related to how it is said. The category of perspicuity principles includes constraints on avoiding obscurity and ambiguity, or being brief and orderly. I t is anything but clear how these principles should be interpreted precisely. Several attempts have been • made to remedy this in computational work on generating texts that best satisfy these maxims, especially with respect to the generation of referring expressions (e.g. [Dale et al. 95]). However, there is•more t o pragmatics than satisfying Gricean maxims. In particular, the category of perspicuity principles does not usually cover the important fact that texts are tailored t o a specific addressee, not only in content,• i.e., with respect to her or his informational needs , but also in the linguistic form, i.e., word order, syntactic constructions, the choice of lexical items, and eventually prosodic information. This tailoring o f the linguistic form to the listener is termed "information structuring". In generating texts, information Structuring requires, among other things, the use of some listener model, which may include information about the listener's knowledge, goals, properties, etc. LinguiStic approaches to describing the principles of information structuring have sometimes characterized information structure as an instruction to the listener about how to construct a model of the communica ted state of affairs [Prince 81]~ In AI and Computational Linguistics, tailoring the message to the • . Usercomprises very often solely content planning, which only indirectly determines the linguistic output. : : ~:i. i . / For:~xaniple, Systems tailor the information "density" to the user (e.g.[Pads 93]), or they drive the dialogue • -, depending on an estimation of what the user might be interested in (e.g. [Jameson et al. 94]). Realizing texts . by determining the information structure of the respective sentences, which again is a reflex of addressee • orientation, has not yet.received its due attention. • *The authors would like to thank Bob Kasper, Nathan Vaillette, Shravan Vasishth, and twoanonymous referees for helpful comments and suggestions. All remaining mistake s are, of course,..our own.

A proposal of abductive inference with degrees of manifestations

Abstract: This paper proposes a fuzzy abductive inference with degrees of manifestation. The fuzzy logic is applied to Peng and Reggia's abductive inference for handling the manifestation degrees. This method infers irredundant combinations of candidates with degrees of belief for the manifestations. A learning algorithm for updating the fuzzy causations and t-conorm parameter is also presented in this paper. Application of the new method to a diagnostic problem is shown and the effectiveness of the proposed method is demonstrated.

Abductive Knowledge Base Updates for Contextual Reasoning

Abstract: We show in this paper how procedures that update knowledge bases can naturally be adapted to a number of problems related to contextual reasoning. The fact that the update procedures are abductive in nature is favourably exploited to tackle problems related to human-computer dialogue systems. We consider as examples aspects of pronoun resolution,goal formulation , and the problem of restoring the consistency of a knowledge base after some knowledge update is carried out. We state these problems in terms of the update problem and abductive reasoning and show how procedures that update knowledge bases yield some interesting results. We also explain how these procedures can naturally be used to model various forms of hypothetical reasoning such as hypothesizing inconsistencies and performing some “look ahead” form of reasoning. We do not claim thaT the problems presented here are solved entirely within the update framework. However, we believe that the flexibility of the representation and of the problem-solving approach suggest that the problems could be solved by adding more details about each problem. What is most interesting in our understanding is that all the aforementioned problems are expressed and tackled within the same framework.

C. S. Peirce: Belief, Truth, and Going from the Known to the Unknown

Abstract: The third philosophical stratagem for cutting off inquiry consists in maintaining that this, that, or the other element of science is basic, ultimate, independent of aught else and utterly inexplicable— not so much from any defect in our knowing as because there is nothing beneath it to know The only type of reasoning by which such a conclusion could be reached is retroduction Now nothing justifies a retroductive inference except its affording an explanation of the farts It is, however, no explanation at all of a fact to pronounce it inexplicable That, therefore, is a conclusion which no reasoning can ever justify or excuse (Peirce, Collected Papers 1139) Abduction consists in studying facts and devising a theory to explain them Its only justification is that, if we are ever to understand things at all, it must be in that way (Peirce, Collected Papers, 5145) [Scientific procedure] will at times find a high probability established by a single confirmatory instance, while at others it will dismiss

Query expansion using fuzzy abductive inference for creative thinking support system

Abstract: Proposes a query expansion method using fuzzy abductive inference for a creative thinking support system. This query expansion method for information retrieval is useful, because unexpected related information can probably be obtained by adding new words to the query. The fuzzy abductive inference is the inference method based on Peng and Reggia's (1990) abductive inference. This method infers nonredundant combinations of concept words for the query. This paper also studies how to update the weights of concept-term relationships by the steepest descent method.

Abduction, that ubiquitous form of reasoning

Abstract: In recent years, attention has been devoted to abduction, a hypothetical form of non-monotonic reasoning that tries to fit the best ‘explanation’ to a given observation. In this paper we present a collection of applications of automated abductive reasoning developed in the Center for Artificial Intelligence of the ITESM (Monterrey, Mexico) in the last five years, covering a range from natural language understanding to software re-use.

An abductive method for solving a treatment problem

Abstract: The work analyzes a therapy planning problem in an agricultural domain in which where there are no standard treatment protocols. For this reason, the skeletal planning method cannot be applied, and the use of an abductive method is therefore proposed. This work is part of a broader project, the main goal of which is the development of a knowledge based system for phytosanitary advice. For this knowledge based system, the CommonKADS methodology has been selected. In order to apply the method, we present a general formalization of therapeutic problems that is domain independent, and afterwards we show how to find solutions for this problem using a treatment task decomposition.

The Role of Deductive Reasoning in Scientific Activities

Abstract: What does mean the statement that scientific reasoning is logical? In this study, we clarify the logical structure of the scientific explanation, prediction and the process of hypothesis testing. To simplify and identify the structure of scientific explanations and prediction more clearly, we used syllogism and presented various concrete examples. Especially, we showed that the logical structure of scientific explanation was well reflected in dynamics. Based on this analysis, it can be said that the deficit of students' understanding of dynamics is because that many scientific activities are focused on prediction rather than explanation. To explain the process of hypothesis testing, we reinterpreted the Wason's selection task as two stages: the process of prediction of experimental phenomena based on the presented hypothesis, and the process of the hypothesis testing based on the predicted experimental phenomena. And we suggested the reason of the logical fallacy of 'affirming the consequent' in science was because that many scientific relationships between the variables is one-to-one relationship, and compared this suggestion with the Lawon's multiple hypothesis theory. To check out the effect of content on the deductive reasoning, we reviewed some researches about psychology and psychology of science. And to understand the role of deductive reasoning in student's scientific activities, we reviewed researches about the analysis of students' responses in the task of conceptual change or evaluation of evidence and so on.

The Multi-Talented Artist: Toward an Abductive Approach

Abstract: Artists with multiple talents are frequently alluded to by historians of both literature and the visual arts: Michelangelo, William Blake, and Dante Gabriel Rossetti are but a few names that recur, and to this list one could also add such modern examples as Ernst Barlach, D. H. Lawrence, James Thurber, and A. R. Penck. Yet multiple talent in artists, as an issue of scholarly debate, is only recently beginning to be viewed as central to the broader theoretical concern of interrelationships between the arts. Even today, moreover, although mention is often made of individuals who were productive or creative in more than one medium, seldom are the relations among their works in the different media explored in any systematic fashion. As a way of addressing this situation, my essay will commence with a discussion of general views about the subject of the Doppelbegabung, or multi-talented artist, and why in the past it has tended to be relegated to the fringes of the interarts field. My main focus here will be on what might be termed failed solutions to the problem, and amongst these, I will argue, are those studies which rightly recognize the need to attend to formal similarities between the various media used but which lack an awareness of the kind of theoretical framework necessary for making such comparisons. Arguing then that such theorizing must begin at the most basic level of the sign and involve an examination of the interpretive process itself, I will attempt to outline the requisite framework by enlisting the ideas of the American philosopher Charles Sanders Peirce (1839-1914), primarily those concerned with semiotics and kinds of reasoning procedures. Although I will also be drawing on Umberto Eco's revisioning of Peirce's theories, I will conclude that when it comes to interarts issues Peirce's scientific view of abductive reasoning is more helpful than Eco's attempt to isolate a particular kind for creative works. In this way, although I will not myself provide a practical demonstration of how sign theory functions in the case of specific Doppelbegabungen, I hope to provide the methodology that will be conducive to further and more sound ways of investigating the role to be played by such artists in discussions of relations between the arts. Approaches to the issue of Doppelbegabung are often related to two opposing views regarding the relationship between literature and the visual arts. Those like Paul and Svetlana Alpers who argue for the autonomy of the pictorial (457), or like Wilhelm Waetzoldt who maintain that art history and literary history are "independent disciplines, each [of which] explores its own subjects and develops its own methodology" (3) tend to contend that even when the same person creates works in two or more media, differing interpretive methods are necessary.(1) Thus Rene Wellek, for instance, is skeptical with regard to the appropriateness and significance of studies analyzing persons with multiple artistic talents (Theory 129). Defenders of the opposing view, that which advocates a mutual illumination of the arts, hold that the multiple-talented artist is a prime instance where "the different arts merge in the personality of one person" (Wais 17). Similarly, according to Mario Praz, "if an artist is at the same time a writer, we should be likely to find in his work the surest test of the theory of a parallel between the arts," for there is "either a latent or a manifest unity in the productions of the same artist in whatever field he tries his hand" (40-54). Peter V. Zima concurs (ix), as does Thomas Jensen Hines: "Such variables as the artist's style, his development, and his idiosyncrasies tend to carry over from one art to the next, thus making discussion and understanding considerably easier" (15). Such extreme positions, however, are scarcely helpful in dealing with the issue of the Doppelbegabung, or as Ulrich Weisstein notes: "Wellek's view is just as one-sided as that which, at the other end of the spectrum, claims that all creative activities of an artist working in several media are perfectly aligned and hence aesthetically compatible" ("Literature and the Other Arts" 261). …

Philosophical Aspects of Scientific Discovery: A Historical Survey

Abstract: This paper is intended as an investigation of scientific discoveries from a philosophical point of view. In the first half of this century, most of philosophers rather concentrated on the logical analysis of science and set the problem of discovery aside. Logical positivists distinguished a context of discovery from a context of justification and excluded the former from their analysis of scientific theories. Although Popper criticized their inductivism and suggested methodological falsificationism, he also left the elucidation of discovery to psychological inquiries. The problem of scientific discovery was proprely treated for the first time by the "New Philosophy of Science" school in the 1960's. Hanson reevaluated the method of "retroduction" on the basis of Peirce's logical theory and analysed Kepler's astronomical discovery in detail as a typical application of it. Kuhn's theory of paradigm located discoveries in the context of scientific revolutions. Moreover, he paid attention to the function of metaphor in scientific discoveries. Metaphorical use of existing terms and concepts to overcome theoretical difficulties often plays a decisive role in developping new ideas. In the period of scientific revolution, theoretical metaphors can open up new horizons of scientific research by way of juxtapositions of heterogeneous concepts. To explicate such a complicated situation we need the "rhetoric" of science rather than the "logic" of science.

A system for abductive learning of logic programs

Abstract: We present the system LAP (Learning Abductive Programs) that is able to learn abductive logic programs from examples and from a background abductive theory. A new type of induction problem has been defined as an extension of the Inductive Logic Programming framework. In the new problem definition, both the background and the target theories are abductive logic programs and abductive derivability is used as the coverage relation. LAP is based on the basic top-down ILP algorithm that has been suitably extended. In particular, coverage of examples is tested by using the abductive proof procedure defined by Kakas and Mancarella [24]. Assumptions can be made in order to cover positive examples and to avoid the coverage of negative ones, and these assumptions can be used as new training data. LAP can be applied for learning in the presence of incomplete knowledge and for learning exceptions to classification rules.

Refining action theories through abductive logic programming

Abstract: Reasoning about actions and changes often starts with an action theory which is then used for planning, prediction or explanation. In practice it is sometimes not simple to give an immediately available action theory. In this paper we will present an abductive methodology for describing action domains. We start with an action theory which is not complete, i.e., has more than one model. Then, after some tests are done, we can abduce a complete action theory. Technically, we use a high level action language to describe incomplete domains and tests. Then, we present a translation from domain descriptions to abductive logic programs. Using tests, we then abductively refine an original domain description to a new one which is closer to the domain in reality. The translation has been shown to be both sound and complete. The result of this paper can be used not only for refinement of domain descriptions but also for abductive planning, prediction and explanation. The methodology presented in this paper has been implemented by an abductive logic programming system.

Abductive Reasoning over Temporal Specifications of Objects.

Abstract: In the context of reactive system speciication, the certiication task usually consists in ensuring that some envisaged property of the system behaviour follows from the speciication. Considering, in particular, object-oriented system speciication based on temporal logic, we have that, in general, the object speciications can only include certain kinds of formulae (e.g. constraints on action ocurrences and changes in attribute values) and the formulae expressing the intended properties of the system behaviour can be diierent. Therefore, whenever a property is not inferred from the speciication, we can not just add it as a new speciication axiom and traditional certiication approaches do not use to provide help on how the speciication could be enriched. Finding how to enrich a speciication with suitable new axioms is an important issue that has not been explored. This task has an abuctive avour and in this paper we address the problem of certiication of temporal object speciications from this abductive perspective: the properties that are not satissed are seen as abduction problems and the new axioms are seen explanations for these problems. The techniques we describe for the generation of explanations combine ideas from temporal logic proof techniques and from tableau based techniques for abductive reasoning. Given the particular relevance of certain properties, namely safety, response, persistence and guarantee properties, in the context of temporal speciication of reactive systems, we concentrate our attention on the development of abductive techniques for these kinds of properties. We also describe the extension of these techniques to the case of object community speciications.

Reasoning about Data

Abstract: Two factors have affected the work of modem data analysts more than any others. First, the size of machine-readable data sets has increased, especially during the last decade or so. Second, computational methods and tools are being developed that enhance traditional statistical analysis. These two developments have created a new range of problems and challenges for analysts, as well as new opportunities for intelligent systems in data analysis. To provide an international forum for the discussion of these topics, a series of symposia on Intelligent Data Analysis was initiated in 1995 [4]. The second Intelligent Data Analysis conference (IDA-97) was held at Birkbeck College, University of London, 4th-6th August 1997. Almost 130 people from twenty countries in four continents took part in the symposium. A total of 107 papers were submitted to the IDA-97 conference, of which 50 were accepted as either oral or poster presentations. After the conference, five papers were chosen from the conference program and their authors were invited to prepare extended versions for publication in the Intelligent Data Analysis (IDA) JournaL A second round of review provided additional feedback to the authors and the papers are now presented in this special Issue.

A Two-Tiered Reasoning and Learning Architecture

Abstract: The paper is an attempt to summarize the previous works of the author on integrating deductive and abductive reasoning paradigms for solving the classification task. A two-tiered reasoning and learning architecture in which Case-Based Reasoning (CBR) used both as a corrective of the solutions inferred by a deductive reasoning system and as a method for accumulating and refining knowledge is briefly described. As illustrative e~Amples the applications of the approach for problems of the case-bnsed maintenance of rnie-based systems and for case-based refinement of neural networks are presented.