Showing papers on "Abductive reasoning published in 2008"

Artificial Reasoning with Subjective Logic

Abstract: This paper defines a framework for artificial reasoning called Subjective Logic, which consists of a belief model called opinion and set of operations for combining opinions. Subjective Logic is an extension of standard logic that uses continuous uncertainty and belief parameters instead of only discrete truth values. It can also be seen as an extension of classical probability calculus by using a second order probability representation instead of the standard first order representation. In addition to the standard logical operations, Subjective Logic contains some operations specific for belief theory such as consensus and recommendation. In particular, we show that Dempster’s consensus rule is inconsistent with Bayes’ rule and therefore is wrong, and provide an alternative rule with a solid mathematical basis. Subjective Logic is directly compatible with traditional mathematical frameworks, but is also suitable for handling ignorance and uncertainty which is required in artificial intelligence.

Semantic preferential subsumption

Abstract: We present a general preferential semantic framework for plausible subsumption in description logics, analogous to the KLM preferential semantics for propositional entailment. We introduce the notion of ordered interpretations for description logics, and use it to define two mutually dual non-deductive subsumption relations ***⊑ and ***⊑*. We outline their properties and explain how they may be used for inductive and abductive reasoning respectively. We show that the preferential semantics for subsumption can be reduced to standard semantics of a sufficiently expressive description logic. This has the advantage that standard DL algorithms can be extended to reason about our notions of plausible subsumption.

From Ugly Duckling to Swan: C. S. Peirce, Abduction, and the Pursuit of Scientific Theories

Abstract: Jaakko Hintikka (1998) has argued that clarifying the notion of abduction is the fundamental problem of contemporary epistemology. One traditional interpretation of Peirce on abduction sees it as a recipe for generating new theoretical discoveries . A second standard view sees abduction as a mode of reasoning that justifies beliefs about the probable truth of theories. While each reading has some grounding in Peirce's writings, each leaves out features that are crucial to Peirce's distinctive understanding of abduction. I develop and defend a third interpretation, according to which Peirce takes abductive reasoning to lead to judgments about the relative pursuitworthiness of theories; conclusions that can be thoroughly disconnected from assessments of truth-value. Even if Peirce's use of "abduction" slides around among each of these three importantly different though potentially compatible senses, this neglected third understanding makes sense of a large number of Peirce's remarks and directs our attention to the cognitive structure of judgments that scientists face after the initial proposal of explanatory hypotheses but prior to their experimental testing; a topic which should be of interest to contemporary philosophers of science.

Ontology-Based Reasoning Techniques for Multimedia Interpretation and Retrieval

Abstract: In this chapter, we show how formal knowledge representation and reasoning techniques can be used for the retrieval and interpretation of multimedia data. This section explains what we mean by an “interpretation” using examples of audio and video interpretation. Intuitively, interpretations are descriptions of media data at a high abstraction level, exposing interrelations and coherencies. In Section 3.2.3, we introduce description logics (DLs) as the formal basis for ontology languages of the OWL (web ontology language) family and for the interpretation framework described in subsequent sections. As a concrete example, we consider the interpretation of images describing a sports event in Section 3.3. It is shown that interpretations can be obtained by abductive reasoning, and a general interpretation framework is presented. Stepwise construction of an interpretation can be viewed as navigation in the compositional and taxonomical hierarchies spanned by a conceptual knowledge base. What do we mean by “interpretation” of media objects? Consider the image shown in Fig. 3.1. One can think of the image as a set of primitive objects such as persons, garbage containers, a garbage truck, a bicycle, traffic signs, trees, etc. An interpretation of the image is a description which “makes sense” of these primitive objects. In our example, the interpretation could include the assertions “two workers empty garbage containers into a garbage truck” and “a mailman distributes mail” expressed in some knowledge representation language. When including the figure caption into the interpretation process, we have a multimodal interpretation task which in this case involves visual and textual media objects. The result could be a refinement of the assertions above in terms of the location “in Hamburg”. Note that the interpretation describes activities extending in time although it is only based on a snapshot. Interpretations may generally include

An abductive perspective on clinical reasoning and case formulation.

Abstract: This chapter presents a framework for clinical reasoning and case formulation that is largely based on the abductive theory of scientific method presented in chapter three. Clinical reasoning has traditionally been understood in terms of the hypothetico-deductive method. Occasionally, Bayesian methods have been used as a resource. However, it is suggested that clinical psychology requires an organizing framework that goes beyond the strictures of these two methods and characterizes the full range of reasoning processes involved in the description, understanding, and formulation of the difficulties presented by clients. In the abductive theory of method, the processes of phenomena detection and theory construction are articulated and combined. Both of these processes are applied to clinical reasoning and case formulation, and a running case example is provided to illustrate the application.

Deductive reasoning: in the eye of the beholder

Abstract: This study examines ways of approaching deductive reasoning of people involved in mathematics education and/or logic. The data source includes 21 individual semi-structured interviews. The data analysis reveals two different approaches. One approach refers to deductive reasoning as a systematic step-by-step manner for solving problems, both in mathematics and in other domains. The other approach emphasizes formal logic as the essence of the deductive inference, distinguishing between mathematics and other domains in the usability of deductive reasoning. The findings are interpreted in light of theory and practice.

Reasoning: Mental Models and Deductive Reasoning

Abstract: How do people reason? The view that I learned at my mother's knee was that they rely on logic. During the 1960s and 1970s when the study of thinking had become respectable again after the Dark Ages of Behaviorism, psychologists – including the present author – took this view for granted. The idea that logic provided the norms of reasoning can be traced back to the rise of modern logic, and was defended in the nineteenth century by both Boole (1854) and Mill (1874). In the twentieth century, the Swiss psychologist, Jean Piaget, and his colleagues argued that the construction of a formal logic in the mind was the last great step in children's intellectual development, and that it occurred at about the age of twelve (see, e.g., Inhelder and Piaget, 1958). And so thirty years ago the task for psychologists appeared to be to determine which particular formal logic was laid down in the mind and which particular rules of inference were used in its mental formulation. That, at least, was how several like-minded authors conceived their research (see, e.g., Osherson 1974–1976; Johnson-Laird 1975; Braine 1978; Rips 1983). In the parallel “universe” of artificial intelligence, researchers were similarly developing computer programs that proved theorems relying on formal rules of inference (e.g., Bledsoe 1977). The main skeptics were those engaged in trying to analyze everyday arguments. They discovered that it was extraordinarily difficult to translate such arguments into formal logic.

Abductive Inference: Implications for ‘Linnean’ and ‘Phylogenetic’ Approaches for Representing Biological Systematization

Abstract: A common claim among advocates of the ‘phylogenetic’ system of nomenclature (PN) is that the ‘Linnean’ system (LN) is defective because it fails to promote stability, universality, and unambiguous meaning in the naming of phylogenetic hypotheses. This claim rests on the premise that biological systematization should be phylogenetic. The foundation for systematization lies beyond the principles of phylogenetics, as systematization is a goal common to all fields of science. Namely, that explanatory hypotheses should be communicated as accurately as possible. The basis for this claim is established through a brief review of the relations between causal questions and the abductive inferences that lead to phylogenetic hypotheses. Six implications ensue from this analysis. (1) The LN requirement that species hypotheses be presented in conjunction with genus-level (phylogenetic) hypotheses is unfounded. The basis for the inference of a species hypothesis stands entirely separate from any phylogenetic hypothesis. (2) Monotypic supraspecific hypotheses have no epistemic basis. (3) While comparisons of phylogenetic hypotheses based on rank assignments in the LN are denied for the fact that these distinct explanatory accounts are irrelevant to one another, this does not preclude the use of ranks. The ranking of hypotheses has the utility of communicating their relative explanatory inclusiveness. But, since there are no requirements that all hypotheses be formally named, there can be no consistent application of ranks, thereby obviating their use. (4) The argument for nomenclatural stability, used by advocates of both the LN and PN, is shown to be an arbitrary and unfounded criterion. (5) The distinctions between diagnosis, description, and definition are examined in light of the abductive nature of phylogenetic inference. The classes of definition promoted by advocates of the PN, i.e., node-, stem-, and apomorphy-based, are shown to be defective. (6) The association of species and phylogenetic hypotheses with types in the LN and specifiers in the PN is shown to be inadequate. The basis for formal names that represent species and phylogenetic hypotheses is the totality of observed specimens that prompted inferences of those hypotheses. Finally, universality and unambiguous meaning are shown to be unnecessary criteria, as each is subsumed by the broader principles of abductive inference.

Scientific method, abduction, and clinical reasoning.

Abstract: This special issue of the Journal of Clinical Psychology comprises six theoretical papers that are concerned with the interconnected topics of scientific method, abductive inference, and clinical reasoning. The first four papers deal with the nature and limitations of a broad abductive theory of scientific method, and its application to clinical reasoning and case formulation. These are followed by three papers which in turn consider the prospects of using explanatory criteria to appraise competing models of psychopathy, examine the merits of a number of different psychometric perspectives on the assessment of psychopathology, and reject a core supposition of the orthodox approach to hypothesis testing. © 2008 Wiley Periodicals, Inc. J Clin Psychol 64: 1–6, 2008.

Text Knowledge Mining: An Alternative to Text Data Mining

Abstract: In this paper we introduced an alternative view of text mining and we review several alternative views proposed by different authors. We propose a classification of text mining techniques into two main groups: techniques based on inductive inference, that we call text data mining (TDM, comprising most of the existing proposals in the literature), and techniques based on deductive or abductive inference, that we call text knowledge mining (TKM). To our knowledge, the TKM view of text mining is new though, as we shall show, several existing techniques could be considered in this group. We discuss about the possibilities and challenges of TKM techniques. We also discuss about the application of existing theories in possible future research in this field.

Précis of 'an abductive theory of scientific method'.

Abstract: This short article is a precis of the author's (2005a) abductive theory of scientific method. This theory of method assembles a complex of specific strategies and methods of relevance to psychology that are employed 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.

Common understanding as a basis for coordination

Abstract: Purpose – The purpose of this paper is to develop further a theoretical framework of common understanding and explore the role of common understanding in coordinationDesign/methodology/approach – A constructive action research approach was employed applying abductive reasoning to develop new models with practical relevanceFindings – A new framework of the elements of common understanding and a new theory of communication as a mechanism for coordinationResearch limitations/implications – As a longitudinal case study and part of a multiple case‐study, the findings are generalized to theory which should be further developedPractical implications – Presents a framework for developing shared meanings to achieve better coordination in collaborative service provisioningOriginality/value – Presents a new model of common understanding, a refined approach to coordination

DARE: a system for distributed abductive reasoning

Abstract: Abductive reasoning is a well established field of Artificial Intelligence widely applied to different problem domains not least cognitive robotics and planning. It has been used to abduce high-level descriptions of the world from robot sense data, using rules that tell us what sense data would be generated by certain objects and events of the robots world, subject to certain constraints on their co-occurrence. It has also been used to abduce actions that might result in a desired goal state of the world, using descriptions of the normal effects of these actions, subject to constraints on the action combinations. We can generalise these applications to a multi-agent context. Several robots can collaboratively try to abduce an agreed higher-level description of the state of the world from their separate sense data consistent with their collective constraints on the abduced description. Similarly, multi-agent planning can be accomplished by the abduction of the actions of a collective plan where each agent uses its own description of the effect of its actions within the plan, such that the constraints on the actions of all the participating agents are satisfied. To address this class of problems, we need to generalise the single agent abductive reasoning algorithm to a distributed abductive inference algortihm. In addition, if we want to investigate applications in which the set of collaborating robots/agents is open, we need an algorithm that allows agents to join or leave the collaborating group whilst a particular inference is under way, but which still produces sound abductive inferences. This paper describes such a distributed abductive reasoning system, which we call DARE, and its implementation in the multi-threaded Qu-Prolog variant of Prolog. We prove the soundness of the algorithm it uses and we discuss its completeness in relation to non-distributed abductive reasoning. We illustrate the use of the algorithm with a multi-agent meeting scheduling example. The task is open in that the actual agents who need to attend is not determined in advance. Each individual agent has its own constraints on the possible meeting time and concerning which other agents must or must attend the meeting, if it attends. The algorithm selects the agents to attend and ensures that the constraints of each of the attending agents are satisfied.

Human reasoning and argumentation: the probabilistic approach

Abstract: Book synopsis: This interdisciplinary work is a collection of major essays on reasoning: deductive, inductive, abductive, belief revision, defeasible (non-monotonic), cross cultural, conversational, and argumentative. They are each oriented toward contemporary empirical studies. The book focuses on foundational issues, including paradoxes, fallacies, and debates about the nature of rationality, the traditional modes of reasoning, as well as counterfactual and causal reasoning. It also includes chapters on the interface between reasoning and other forms of thought. In general, this last set of essays represents growth points in reasoning research, drawing connections to pragmatics, cross-cultural studies, emotion and evolution.

Did He Jump or Was He Pushed? Abductive Practical Reasoning

Abstract: In this paper we present an approach to abductive reasoning in law by examining it in the context of an argumentation scheme for practical reasoning We present a particular scheme, based on an established scheme for practical reasoning, that can be used to reason abductively about how an agent might have acted to reach a particular scenario, and the motivations for doing so Plausibility here depends on a satisfactory explanation of why this particular agent followed these motivations in the particular situation The scheme is given a formal grounding in terms of Action-based Alternating Transition Systems and we illustrate the approach with a running legal example

Abductive reasoning, interpretation and collaborative processes

Abstract: In this paper we want to examine how the mutual understanding of speakers is reached during a conversation through collaborative processes, and what role is played by abductive inference (in the Peircean sense) in these processes We do this by bringing together contributions coming from a variety of disciplines, such as logic, philosophy of language and psychology When speakers are engaged in a conversation, they refer to a supposed common ground: every participant ascribes to the others some knowledge, belief, opinion etc on which to rely in order to reach mutual understanding As the conversation unfolds, this common ground is continually corrected and reshaped by the interchanges An abductive reasoning takes place, in a collaborative setting, in order to build new possible theories about the common ground In reconstructing this process through the use of a working example, we argue that the integration of a collaborative perspective within the Peircean theory of abduction can help to solve some of the drawbacks that the critics of the latter have outlined, for example its permissivity and non generativity

Investigating Stories in a Formal Dialogue Game

Abstract: In this paper we propose a formal dialogue game in which two players aim to determine the best explanation for a set of observations. By assuming an adversarial setting, we force the players to advance and improve their own explanations as well as criticize their opponent's explanations, thus hopefully preventing the well-known problem of 'tunnel vision'. A main novelty of our approach is that the game supports the combination of argumentation with abductive inference to the best explanation.

Explaining the Past in the Geosciences

Abstract: Abductive reasoning is central to reconstructing the past in the geosciences. This paper outlines the nature of the abductive method and restates it in Bayesian terms. Evidence plays a key role in this working method and, in particular, traces of the past are important in this explanatory framework. Traces, whether singularly or as groups, are interpreted within the context of the event for which they have evidential claims. Traces are not considered as independent entities but rather as inter-related pieces of information concerning the likelihood of specific events. Exemplification of the use of such traces is provided by dissecting an example of their use in the environmental reconstruction of mountain climate.

Abductive Reasoning with Uncertainty

Abstract: Abductive reasoning in general describes the process of discovering hypotheses and rules that would entail a given conclusion. Abductive reasoning consists of assessing the likelihood that a specific hypothesis entails a given conclusion. Abductive reasoning based on probabilities is used in many disciplines, such as medical diagnostics, where medical test results combined with conditional probabilities are used to determine the likelihood of possible diseases. In this paper we focus on abductive reasoning in subjective logic. The advantage of our approach over a purely probabilistic approach is that degrees of ignorance can be explicitly included as input and during the analysis.

A Call for an Abductive Reasoning Feature in OWL-Reasoning Tools toward Ontology Quality Control.

Abstract: It is widely touted that OWL reasoners are able to improve the quality of ontologies by making new inferences from and detecting inconsistencies among the assertions that have been explicitly stated in the ontologies. These reasoner actions are based on standard deductive reasoning, which operates on the principle that assertions inferred from premises that are assumed to be true also must be true. Deductive reasoning is similarly the basis for OBO­Edit, the primary ontology­management tool of the Open Biomedical Ontologies (OBO), the most prominent and highly used set of ontologies in the biomedical domain. OBOs have typically been created modularly and with informal, natural­language definitions; this is largely due to the fact that they are mostly developed by different groups (and have varying levels of funding, which partly accounts for the varying levels of quality among the OBOs). There have been recent efforts to formalize and link the disparate ontologies, and there is now an extensive effort within the OBO Consortium to create formal definitions of the component terms using more atomic terms. Running a deductive reasoner over these so­called cross­product definitions has resulted in improved ontology quality, usually in the form of inferred is_a links among terms. This has the added effect of aligning the linked subject and object terms; thus, the inferred is_a links point to what we refer to as nonalignments, in which either the subject terms are subsumptively linked and the object terms are not, or vice versa. We have additionally noticed that a form of abductive reasoning not currently widely used has also proven useful in aligning linked terms and thus further improving ontology quality. First, as an example of a nonalignment that can be deductively detected: Class(positive regulation of hydrolase activity complete biological_process restriction(regulates some hydrolase activity)) Class(ATPase activator activity complete molecular_function

Abductive inferences to psychological variables: Steiger's question and best explanations of psychopathy.

Abstract: Abductive inference often involves inference to the best explanation. A focus on the bestness of explanations facilitates a comparative analysis of how abductive inference would differ if approached with four contrasting sets of assumptions about how scientific inference works: positivism, realism, and two kinds of pragmatism. As a thought experiment, one can imagine a situation in which competing models of psychopathy differ in parsimony and fit to the data, but produce a tie when considering both virtues in combination. The thought experiment demonstrates that Steiger's (1990) question about how best to combine competing virtues in scientific inference applies to abductive inference and that the answers depend upon other assumptions about how science works. The comparative analysis helps focus some of the issues that require clarification before abductive inference can enter the Pantheon of standard research methods in psychology. More constructively, the analysis also demonstrates that one need not accept scientific realism to accept and use abductive inference.

The Abductive Loop: Tracking Irrational Sets

Abstract: We argue from the Church-Turing thesis (Kleene Mathematical logic. New York: Wiley 1967) that a program can be considered as equivalent to a formal language similar to predicate calculus where predicates can be taken as functions. We can relate such a calculus to Wittgenstein’s first major work, the Tractatus, and use the Tractatus and its theses as a model of the formal classical definition of a computer program. However, Wittgenstein found flaws in his initial great work and he explored these flaws in a new thesis described in his second great work; the Philosophical Investigations. The question we address is “can computer science make the same leap?” We are proposing, because of the flaws identified by Wittgenstein, that computers will never have the possibility of natural communication with people unless they become active participants of human society. The essential difference between formal models used in computing and human communication is that formal models are based upon rational sets whereas people are not so restricted. We introduce irrational sets as a concept that requires the use of an abductive inference system. However, formal models are still considered central to our means of using hypotheses through deduction to make predictions about the world. These formal models are required to continually be updated in response to peoples’ changes in their way of seeing the world. We propose that one mechanism used to keep track of these changes is the Peircian abductive loop.

A global model of sequences of discrete event class occurrences

Abstract: This paper proposes a global model of a set of alarm sequences that are generated by knowledge based system monitoring a dynamic process. The modelling approach is based on the Stochastic Approach to discover timed relations between discrete event classes from the representation of a set of sequences under the dual form of a homogeneous continuous time Markov chain and a superposition of Poisson processes. An abductive reasoning on these representations allows discovering chronicle models that can be used as diagnosis rules. Such rules subsume a temporal model called the average time sequence that sums up the initial set of sequences. This paper presents this model and the role it play in the analysis of an industrial process monitored with a network of industrial automata.

Everyday life reasoning, possible worlds and cultural processes.

Abstract: Discussing Faiciuc's paper, I first tackle the problem of fallacies in deductive reasoning showing how, in a possible world theory, non correct forms of reasoning can be useful strategies for discovery, providing these strategies remain at a hypothesis level. Secondly, everyday reasoning and its specificity in comparison to logical-normative one are analyzed. This topic stresses the notion of interpretation and, in this context, the role of the community and of cultural canons shared by the subject. From this point of view, reasoning does not occur, only, in the brain of a person but in everyday exchanges occurring between individuals and the history of their community.