Showing papers on "Abductive reasoning published in 1997"

The 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 would make it possible .to share problem-solving methods. For example, when one is dealing with a type of problem known as abductive inference, the following are some of the terms that recur in the representation of problem-solving methods: hypotheses, explanatory coverage, evidence, degree of confidence, plausibility, composite hypothesis, etc. Method ontology, in good part, is task- and method-specific. "Generic Tasks," "Heuristic Classification," "Task-specific Architectures," and "Task Structures" are representative bodies of work in the knowledgesystems area that have focused on problem-solving methods. However, connections have not been made to work that is explicitly concerned with ontologies. Making such connections is the goal of this paper.

Abductive Reasoning: Logic, Visual Thinking, and Coherence

Abstract: This paper discusses abductive reasoning—that is, reasoning in which explanatory hypotheses are formed and evaluated. First, it criticizes two recent formal logical models of abduction. An adequate formalization would have to take into account the following aspects of abduction: explanation is not deduction; hypotheses are layered; abduction is sometimes creative; hypotheses may be revolutionary; completeness is elusive; simplicity is complex; and abductive reasoning may be visual and non-sentential. Second, in order to illustrate visual aspects of hypothesis formation, the paper describes recent work on visual inference in archaeology. Third, in connection with the evaluation of explanatory hypotheses, the paper describes recent results on the computation of coherence.

Reasoning with rules : an essay on legal reasoning and its underlying logic

Abstract: Preface. I: Introduction. II: Of Reasons. A. Philosophical Roots. B. A Causal Account of Reasons. C. Reasons and Principles. D. Kinds of Reasons. III: Reasoning with Rules. IV: Reason-Based Logic. V: Applications of Reason-Based Logic. VI: Concluding Observations. A. Possible Amendments to Reason-Based Logic. B. Semantics for Rules. C. Reason-Based Logic in Comparison. D. Two Metaphors of Reasoning. References. Index of Names. Index of Subjects.

Certainty, probability and abduction: why we should look to C.S. Peirce rather than Gödel for a theory of clinical reasoning

Abstract: This paper argues that Godel's proof does not provide the appropriate conceptual basis on which to counter the claims of evidence-based medicine. The nature of, and differences between, deductive, inductive and abductive inference are briefly surveyed. The work of the American logician C.S. Peirce is introduced as a possible framework for a theory of clinical reasoning which can ground the claims of both evidence-based medicine and its critics.

Fuzzy Modelling of Case-Based Reasoning and Decision

Abstract: This paper is an attempt at providing a fuzzy set-based formalization of case-based reasoning. The proposed approach, which does not take into account the learning aspects of case-based reasoning, assumes a principle stating that “the more similar are the problem description attributes, the more similar are the outcome attributes”. A weaker form of this principle is also considered. These two forms of the case-based reasoning principle are modelled in terms of fuzzy rules. Then an approximate reasoning machinery taking advantage of this principle enables us to apply the information stored in the memory of precedent cases to the current problem. A particular instance of case-based reasoning, named case-based decision, is especially investigated. A logical model of case-based inference is also described.

An Abductive Inference Technique for Fault Diagnosis in Electrical Power Transmission Networks

Abstract: This paper introduces a system modeling concept and a new algorithm based on abductive inference for handling diagnostic problems in electrical power transmission networks. The overall technique relies on the inherent functional and logical relationships between system elements such as lines, buses, relays and breakers to generate a diagnostic model rather than on experientially derived heuristics. This approach allows for a systematic implementation scheme that is more readily adapted to any power system. Logical models for relays, breakers, lines and buses are presented along with examples on how these models can be interconnected to represent a power system. The resulting system model can simulate the "forward" behavior of a power system. Inputs to the forward model consist of disorders or disturbances, and the outputs are manifestations or alarms. Using abductive logic, a "reverse" system model can be generated. This system model takes the alarms as inputs and produces a diagnosis by generating sets of disorders or disturbances that explain the observed alarms.

Abductive reasoning and clinical assessment

Abstract: Clinical reasoning is one of the central components of psychological assessment. The identification of a client's psychological difficulties and the subsequent depiction of their onset, development, and interrelationships enables clinicians to plan treatment in a systematic and effective manner. Traditionally, the hypothetico-deductive conception of scientific method, with its restricted focus on the testing of hypotheses, has provided the framework for assessment and the development of case conceptualisations. The framework has major limitations, and in this paper we present an alternative abductive theory of scientific method which we believe provides more appropriate guidance for clinicians' reasoning at the various stages of the assessment process. The guidance covers identification of a client's difficulties, the generation of a case formulation or embryonic clinical theory through abductive reasoning, and its subsequent development and evaluation in terms of multiple criteria.

An efficient algorithm for temporal abduction

Abstract: Abduction is the process of generating explanations for an observation, starting from a domain theory. We consider an extension of the classical definition of abduction in order to deal with temporal knowledge both in the theory and in the observations. This form of abduction is the basis of many problem solving activities such as temporal diagnosis and reasoning about actions and events. We show how the locality property can be used in order to compute temporal explanations efficiently. In fact, they could be computed using first an abductive reasoner as a generator of candidates and then a temporal reasoner for checking the consistency of each candidate.

How Not to Defend Constructive Empiricism: a Rejoinder

Abstract: Ladyman, Douven, Horsten and van Fraassen have attempted to defend van Fraassen’s critique of abductive reasoning against the arguments offered in a recent piece of mine. My short rejoinder shows two things. First, their counter-arguments fail to refute my original arguments. Their arguments casually move from the actuality of ‘empirically equivalent rivals’ to the possibility of ‘equally good rivals’. But pointing to the existence of the former would do nothing to establish that empirically equivalent rivals are ‘equally good’ or equally well supported by the evidence. Second, I show that a central claim of their paper, one which three of the four authors see as ‘possibly raising serious problems for constructive empiricism and for van Fraassen’s steps toward a new epistemology’, is very close to the conclusion of my original piece: if explanatory considerations are jettisoned, then even common-sense existential claims are in danger of being unfounded.

UEcho: A Model of Uncertainty Management in Human Abductive Reasoning

Abstract: This paper explores the uncertainty aspects of human abdu c- tive reasoning. Echo, a model of abduction based on the Th e- ory of Explanatory Coherence (Thagard, 1992), captures many aspects of human abductive reasoning, but fails to su f- ficiently manage the uncertainty in abduction. In particular, Echo does not handle belief acquisition and dynamic belief revision, two essential components of human abductive re a- soning. We propose a modified Echo model (UEcho), in which we add a learning mechanism for belief acquisition and a dynamic processing mechanism for belief revision. To evaluate the model, we report an empirical study in which base rate learning serves as a testbed for belief acquisition and the order effect serves as a testbed for belief r evision.

Towards a Logic of Hypotheses: Everyone Does Research

Abstract: Charles Peirce's 'retroduction' is a form of hypothesis generation that takes its place alongside deduction and induction as forms of enquiry and logical inference. It is the only one of the three that can generate innovation and advance knowledge. It is fundamentally tied to open systems theory and the world hypothesis of contextualism. In particular, retroduction is founded in ecological learning, our ability to directly extract meaningful knowledge about our world. Ecological learning and retroduction define the logic of discovery. This ability arises from adaptation through a process of coevolution rather than natural selection. The implication of this adaptation is that no firm barriers can be drawn between common sense and scholarly knowledge, nor between researcher and researched when the researched are human systems. There are three choices of relationship between action researchers and their researched human systems, only one of which - collaboration - respects the intrinsic nature of the people involved.

An Explicit Representation of Reasoning Failures

Abstract: This paper focuses upon the content and the level of granularity at which representations for the mental world should be placed in case-based explainers that employ introspective reasoning. That is, for a case-based reasoning system to represent thinking about the self, about the states and processes of reasoning, at what level of detail should one attempt to declaratively capture the contents of thought? Some claim that a mere set of two mental primitives are sufficient to represent the utterances of humans concerning verbs of thought such as “I forgot his birthday.” Alternatively, many in the CBR community have built systems that record elaborate traces of reasoning, keep track of knowledge dependencies or inference, or encode much metaknowledge concerning the structure of internal rules and defaults. The position here is that a system should be able instead to capture enough details to represent causally a common set of reasoning failure symptoms. I propose a simple model of expectation-driven reasoning, derive a taxonomy of reasoning failures from the model, and present a declarative representation of the failure symptoms that have been implemented in a CBR simulation. Such representations enable a system to explain reasoning failures by mapping from symptoms of the failures to causal factors involved.

Making Sense of Users’ Mouse Clicks: Abductive Reasoning and Conversational Dialogue Modeling

Abstract: Intelligent information systems are expected to interpret the users’ information needs semantically, taking the dialogue context into account Whereas many research prototypes attempt to address the semantic interpretation of queries, only a few try to reason about other aspects of the user’s individual dialogue behavior This paper introduces an approach to context-dependent interpretation of ambiguous user dialogue acts in information seeking interactions We illustrate the dialogue analysis and planning methods in the framework of the logic-based information retrieval system Miracle Based on a dialogue model which describes potential developments of the interaction and recommended problem-solving steps, the abductive dialogue component (ADC) deals with unexpected user inputs which are ambiguous with respect to the intended course of action Exploiting the dialogue history, the ADC uses abduction to generate interpretations of these inputs and thus to offer the user situation-dependent options for proceeding in the retrieval dialogue

Is default logic a reinvention of inductive-statistical reasoning?

Abstract: Currently there is hardly any connection between philosophy of science and Artificial Intelligence research. We argue that both fields can benefit from each other. As an example of this mutual benefit we discuss the relation between Inductive-Statistical Reasoning and Default Logic. One of the main topics in AI research is the study of common-sense reasoning with incomplete information. Default logic is especially developed to formalise this type of reasoning. We show that there is a striking resemblance between inductive-statistical reasoning and default logic. A central theme in the logical positivist study of inductive-statistical reasoning such as Hempel’s Criterion of Maximal Specificity turns out to be equally important in default logic. We also discuss to what extent the relevance of the results of Logical Positivism to AI research could contribute to a reevaluation of Logical Positivism in general.

Representing and reasoning about concurrent actions with abductive logic programs

Abstract: In this paper we extend Gelfond and Lifschitz’ action description language {\cal A} with concurrent actions and observation propositions to describe the predicted behaviour of domains of (concurrent) actions and actually observed behaviour, respectively, without requiring that the actually observed behaviour of a domain of actions be consistent with its predicted behaviour. We present a translation from domain descriptions and observations in the new action language to abductive normal logic programs. The translation is shown to be both sound and complete. From the standpoint of modeldbased diagnosis, in particular, we discuss the temporal explanation of inferring actions from fluent changes at two different levels, namely, at the domain description level and at the abductive logic programming level. The method is applicable to the temporal projection problem with incomplete information, as well as to the temporal explanation of inferring actions from fluent changes.

The Relevance of Logic to Reasoning and Beleif Revision: Harman on ‘Change in View’

Abstract: In Change of View: Principles of Reasoning, Gilbert Harman argues that (i) all genuine reasoning is a matter of belief revision, and that, since (ii) logic is not “specially relevant” to belief revision, (iii) logic is not specially relevant to reasoning, either. Thus, Harman suggests, what is needed is a “theory of reasoning”–which, incidentally, will be psychologistic, telling us both how we do and how we should reason. I argue that Harman fails to establish the need for such a theory, because (a) reasoning is not always a matter of belief revision, and (b) logic is, in fact, of the utmost relevance to both reasoning and belief revision.

Logic and scientific methods. Volume One of the Tenth International Congress of Logic, Methodology and Philosophy of Science, Florence, August 1995

Abstract: Editorial. President's Address. Section 1: Proof Theory and Categorical Logic. Proof-Theoretical Aspects of Self-Referential Truth A. Cantini. Free Lattices, Communication and Money Games A. Joyal. On Methods for Proving Lower Bounds in Propositional Logic J. Krajicek. On Bounded Set Theory V.Yu. Sazonov. Section 2: Model Theory, Set Theory and Formal Systems. Infinitary Logic in Finite Model Theory Ph.G. Kolaitis. Decision Problems for Second-Order Linear Logic P.D. Lincoln, et al. Comparing the Continuum with the First Two Uncountable Cardinals S. Todorcevic. Section 3: Recursion Theory and Constructivism. Church's Thesis and Hume's Problem K.T. Kelly, O. Schulte. The Logic of Functional Recursion Y.N. Moschovakis. From Higher Order Terms to Circuits H. Schwichtenberg, K. Stroetman. Computability and Enumerability R. Soare. Mechanistic Theories in Cognitive Science: The Import of Turing's Thesis G. Tamburrini. Section 5: Philosophical Logic. Conjoining and Disjoining on Different Levels E. Casari. Logical Consequence: A Turn in Style K. Dosen. Applying Normative Rules with Restraint S.O. Hansson, D. Makinson. Section 10: Philosophy of Logic, Mathematics and Computer Science. What Can We Do 'In Principle'? Ch.D. Parsons. Causation, Action, and Counterfactuals J. Pearl. Symposium 1: Logic and Philosophy of Science: Current Interfaces. Logic and Philosophy of Science: Current Interfaces T.A.F. Kuipers. Reliable Belief Revision K.T. Kelly, et al. Explanatory Translation: Beyond Functionalism and Reductionism V. Rantala. Abductive Reasoning: Logic, VisualThinking, and Coherence P. Thagard, C. Shelley. Can the Laws of Nature (Physics) be Complete? P. Weingartner. Symposium 4: Logic in Central and Eastern Europe. Logic in Central and Eastern Europe P. Hajek. Logic in Czechoslovakia and Hungary L. Bukovsky. Mathematical Logic in the Former Soviet Union: Brief History and Current Trends V.A. Uspensky. Logic in Central and Eastern Europe: Balkan Region D. Vakarelov. The Postwar Panorama of Logic in Poland R. Wojcicki. Closing Address. Structure and Perspective: Philosophical Perplexity and Paradox B.C. van Fraassen. Table of Contents Volume II.

Rule-based reasoning in connectionist networks

Abstract: This thesis addresses the problem of efficiently representing large knowledge bases and performing a class of inferences extremely fast. The speed of reasoning depends on a number of factors including the expressiveness of the system, the nature of the computational architecture and the representation methodology. A number of knowledge representation and reasoning schemes have given very high emphasis to just one of such issues while neglecting others. This dissertation work is based on the belief that it is beneficial to take an approach where all such issues are simultaneously addressed. With respect to the issue of computational architecture, it is argued that a connectionist architecture has some significant advantages. Having made that choice, we explore how to represent and reason with rules involving multi-place predicates and variables in a connectionist architecture. The main hurdle that needs to be crossed in order to be able to achieve this is the dynamic binding problem. In essence, the problem is that of representing the dynamic grouping of nodes located in different parts of the network. We use what we refer to as the synchronous activation approach to solve the binding problem. Simply stated, the idea is just that the dynamic grouping of a set of nodes is represented by the fact that all those nodes fire synchronously. This happens to be a solution that is technically attractive as well as biologically plausible. Incorporating the synchronous activation approach to solve the binding problem, rule-based forward and backward reasoning systems have been designed to perform deductive inferences. These systems represent knowledge very efficiently: the number of nodes and links required is only linear in the size of the knowledge base. They also perform inferences extremely fast: an inference takes time that is just linear in the length of the shortest proof. We also examine various ways of extending the expressiveness and reasoning abilities of these systems. An alternative representation scheme more amenable to learning is also presented along with a proposal for doing abductive reasoning in connectionist networks.

A parallel implementation of cost-based abductive reasoning

Abstract: This paper describes efficient parallel first-order cost-based abduct ive re~oning for distributed memory systems. A search control technique of parallel best-first search is introduced into abductive reasoning mechanism, thereby finding much more efficiently a minimal-cost explanation of a given observation. A dynamic approach to selecting processor to which work should be dwtributed is designed for parallel abductive reasoning. We propose a PARallel Costbaaed Abductive Reasoning system, PARCAR. We also implement PARCAR on an MIMD distributed memory parallel computer, Fujitsu AP 1000, and show some performance results with a speed-up comparable to the existing distributed heuristic search.

A fuzzy abductive inference model for diagnostic problem solving using tabu search approach

Abstract: In this paper, the well developed parsimonious set covering theory based abductive inference model for diagnostic problem solving is extended, in order to deal with degrees of cause-and-effect relationship between disorders and manifestations, and degrees of manifestations. A new fuzzy abductive inference model capable of handling these problems is developed, and a new criterion for describing the relative plausibility of different diagnosis hypotheses proposed. Based on this criterion, the diagnostic problem is then formulated as a 0-1 integer programming problem, and a tabu search (TS) approach is presented for solving the problem. Two sample studies are served for demonstrating the reasonableness of the developed fuzzy abductive inference model and the computational efficiency of the TS based method.

Emergence of Abductive Reasoning in Mathematical Problem Solving.

Abstract: This paper examines the novel problem solving actions of a pair of college students. The analysis highlights the role of the solvers' inferential processes including abductions, deductions, and inductions as structuring resources that contribute to both their understanding of the problems they face and the emerging novelty that constitutes their viable solution activity. The purpose of this research is to clarify the processes by which learners construct new knowledge in mathematical problem solving . .. with particular focus on instances where the learner.s emerging abductions help to facilitate development of novel problem solution activity. Findings indicate that: (1) abduction is characterized as an ongoing, sense-making process that constitutes the problem solver's source of ideas as to how to proceed when unexpected problems occur; (2) problem solvers' abductions aided their novel explorations, serving to organize and structure their subsequent solution activity; and (3) novelty demonstrated by the problem solvers through their abductive inferences suggests the need to rethink views toward teaching problem solving. Contains 23 references. (DDR) ******************************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. ******************************************************************************** Emergence of Abductive Reasoning in Mathematical Problem Solving Victor Cifarelli The University of North Carolina at Charlotte PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL S N GRA T BY TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement DUCATIONAL RESOURCES INFORMATION CENTER (ERIC) This document has been reproduced as ived from the person or organization originating it. Minor changes have been made to improve reproduction quality. Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. Paper prepared for the Special Interest Group/ Research in Mathematics Education and presented at the annual meeting of the American Educational Research Association, March 1997. This work was supported in part by funds provided by the University of North Carolina at Charlotte.

Reasoning about Actions with Abductive Logic Programming

Abstract: In order to construct a computer-based system which can reason and act intelligently in the real world, we have to develop a computational but provably correct methodology and its related software system which can reason about actions and changes in dynamic domain. For this purpose we propose to use abductive logic programming paradigm as the computational mechanism. Technically, we make use of a simple, but extensible if needed, action description language to describe the domain in question. Then we use a sound and complete translation algorithm to transform domain descriptions into abductive logic programs. And thus reasoning about actions is reduced to abductive queries against abductive logic programs. In this paper we will only address three important issues: knowledge assimilation, refinement of action theories, and concurrent actions. For the task of knowledge assimilation we will introduce a formal and computational methodology, called the possible causes approach, in contrast to Ginsberg's possible worlds approach and Winslett's possible models approach. For the refinement of a possibly incomplete action theory, we use tests on the domain, and then abductively refine the original domain description to a new one which is closer to the domain in reality. For concurrent actions, we introduce a new semantics by using three-valued fluents to resolve conflicts among atomic actions.