Showing papers on "Abductive reasoning published in 2014"

Causal reasoning with mental models

Abstract: This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex.

Realist explanatory theory building method for social epidemiology: a protocol for a mixed method multilevel study of neighbourhood context and postnatal depression

Abstract: A recent criticism of social epidemiological studies, and multi-level studies in particular has been a paucity of theory. We will present here the protocol for a study that aims to build a theory of the social epidemiology of maternal depression. We use a critical realist approach which is trans-disciplinary, encompassing both quantitative and qualitative traditions, and that assumes both ontological and hierarchical stratification of reality. We describe a critical realist Explanatory Theory Building Method comprising of an: 1) emergent phase, 2) construction phase, and 3) confirmatory phase. A concurrent triangulated mixed method multilevel cross-sectional study design is described. The Emergent Phase uses: interviews, focus groups, exploratory data analysis, exploratory factor analysis, regression, and multilevel Bayesian spatial data analysis to detect and describe phenomena. Abductive and retroductive reasoning will be applied to: categorical principal component analysis, exploratory factor analysis, regression, coding of concepts and categories, constant comparative analysis, drawing of conceptual networks, and situational analysis to generate theoretical concepts. The Theory Construction Phase will include: 1) defining stratified levels; 2) analytic resolution; 3) abductive reasoning; 4) comparative analysis (triangulation); 5) retroduction; 6) postulate and proposition development; 7) comparison and assessment of theories; and 8) conceptual frameworks and model development. The strength of the critical realist methodology described is the extent to which this paradigm is able to support the epistemological, ontological, axiological, methodological and rhetorical positions of both quantitative and qualitative research in the field of social epidemiology. The extensive multilevel Bayesian studies, intensive qualitative studies, latent variable theory, abductive triangulation, and Inference to Best Explanation provide a strong foundation for Theory Construction. The study will contribute to defining the role that realism and mixed methods can play in explaining the social determinants and developmental origins of health and disease.

New Light on Peirce's Conceptions of Retroduction, Deduction, and Scientific Reasoning

Abstract: We examine Charles S. Peirce's mature views on the logic of science, especially as contained in his later and still mostly unpublished writings (1907–1914). We focus on two main issues. The first concerns Peirce's late conception of retroduction. Peirce conceived inquiry as performed in three stages, which correspond to three classes of inferences: abduction or retroduction, deduction, and induction. The question of the logical form of retroduction, of its logical justification, and of its methodology stands out as the three major threads in his later writings. The other issue concerns the second stage of scientific inquiry, deduction. According to Peirce's later formulation, deduction is divided not only into two kinds (corollarial and theorematic) but also into two sub-stages: logical analysis and mathematical reasoning, where the latter is either corollarial or theorematic. Save for the inductive stage, which we do not address here, these points cover the essentials of Peirce's latest thinking on the l...

A concept analysis of abductive reasoning.

Abstract: Aim To describe an analysis of the concept of abductive reasoning. Background In the discipline of nursing, abductive reasoning has received only philosophical attention and remains a vague concept. In addition to deductive and inductive reasoning, abductive reasoning is not recognized even in prominent nursing knowledge development literature. Therefore, what abductive reasoning is and how it can inform nursing practice and education was explored. Design Concept analysis. Data sources Combinations of specific keywords were searched in Web of Science, CINAHL, PsychINFO, PubMed, Medline and EMBASE. The analysis was conducted in June 2012 and only literature before this period was included. No time limits were set. Methods Rodger's evolutionary method for conducting concept analysis was used. Results Twelve records were included in the analysis. The most common surrogate term was retroduction, whereas related terms included intuition and pattern and similarity recognition. Antecedents consisted of a complex, puzzling situation and a clinician with creativity, experience and knowledge. Consequences included the formation of broad hypotheses that enhance understanding of care situations. Overall, abductive reasoning was described as the process of hypothesis or theory generation and evaluation. It was also viewed as inference to the best explanation. Conclusion As a new approach, abductive reasoning could enhance reasoning abilities of novice clinicians. It can not only incorporate various ways of knowing but also its holistic approach to learning appears to be promising in problem-based learning. As nursing literature on abductive reasoning is predominantly philosophical, practical consequences of abductive reasoning warrant further research.

Identifying Kinds of Reasoning in Collective Argumentation

Abstract: We combine Peirce’s rule, case, and result with Toulmin’s data, claim, and warrant to differentiate between deductive, inductive, abductive, and analogical reasoning within collective argumentation. In this theoretical article, we illustrate these kinds of reasoning in episodes of collective argumentation using examples from one teacher’s practice. Examining different kinds of reasoning in collective argumentation can inform how students engage in generating and examining hypotheses using inductive and abductive reasoning and move toward the deductive reasoning required for proof. Mathematics educators can build on their understanding of these kinds of reasoning to support students in reasoning in productive ways.

Evolution, creativity, intelligence, and madness: "Here Be Dragons"

Abstract: One of the great joys of being a scien-tist is the hunt for an elusive signal withinthe noise of data, opinions, biases, andother human foibles associated with thepursuit of knowledge. It is inevitable thatthis imperfect quest will result in manyfalse starts along the way when looking“through a glass, darkly.” Our imperfectand incomplete knowledge of the worldmust look like an unpolished mirror,reflecting gibberish, at times. However, italsoreflectsanunderlyingsignalthatbearsfurther scrutiny, in spite of our instinctto discard a flawed image of reality. Thepursuit of the neural underpinnings ofcreative cognition is certainly that “darkglass” we peer into so intently, attempt-ing to grasp, through our meager instru-ments, some hidden truth. Many thinkersand researchers have found that creativityand madness seem somehow to be inter-twined, but the signal is weak, the imageblurry, and the propensity toward roman-tic stereotypes is high. And yet, as scien-tists, we can only follow the data, tryingtomakesenseofwhatittellsus.So,ratherthan entertain the premise outright let metake you on a bit of a journey (which willend back at madness, I promise).First: What if evolutionary processesselected for two types of reasoning?CosmidesandToobyhypothesizeda“ded-icated intelligence” that “refers to theability of a computational system tosolve predefined, target set of prob-lems.” These problems often involve wellestablished rules—like your mundanelife, and Raven’s Matrices problems, andacquiring a language (Pinker, 1991). Theother problems require “improvisationalintelligence” referring to “the ability of acomputational system to improvise solu-tions to novel problems” (Cosmides andTooby, 2002). These problems are moretransient and involve contingencies thatmay or may not persist over time—likefiguring out how to get into your car, hav-ing locked your keys inside. Philosopherscall the former type of problem solving“deductive reasoning”—the observationsnecessarily result in a conclusion beingmade based on the evidence. They are rulebased, deterministic, and the cause leadsnaturallytoeffect.Thelatterproblemsolv-ing is called “abductive reasoning”—thereare an infinite number of possible solu-tionstothemyriadchallengesfacedinthe world; therefore a theory best explainsthe observation, given the evidence. Thisreasoningisprobabilistic,involvesapprox-imation,and(importantly)guessing.Bothmethods are adaptive: one for problemsthat are familiar, the other for problemsthat have never been encountered before.Kanazawa (2004) views intelligence(incorrectly), the pinnacle of deductivereasoning, as THE domain-specific adap-tation to solving novel problems in theenvironment.However,itismycontentionthat intelligence and creativity occupy twoextremes of a dichotomy: intelligence sup-plies a “dedicated reasoning capacity” forproblems that possess rule-based, cause-effect relationships. Others have coveredwell, and provide empirical support for,the “general purpose problem solving”capacity of intelligence and “g” (Kaufmanet al., 2011): I am merely saying here thatthe mechanism is rather “dedicated” tocause-effectrelationships—acapacitywithbroad applicability to deductive reason-ing tasks. In contrast, creativity emergedas an adaptive cognitive mechanism forlow frequency, “improvisational reason-ing,” where solutions to problems areunsighted (Simonton, 2013), and proba-bilistic approximation could lead to novelsolutions. Creative reasoning solves theminority of problems that are unforeseenand yet of high adaptability: “The light-ning has struck the tree near the campand set it on fire. The fire is now spread-ing to the dry underbrush. What should Ido?” (Kanazawa, 2004). In this conceptu-alization, creativity is an evolved cognitivemechanism to abstract, to synthesize, tosolve non-recurrent problems in the envi-ronment. Finally, intelligence should beseen as a rather stable evolved mechanismover the last 1.6 million years (i.e., thesingular“innovation”beingtheAcheuleanhand ax), while creativity appears to haveappeared, in humans at least, in thelast ∼30,000 years (Gabora and Kaufman,2010). Intelligence may not be evolution-arily novel, but creativity certainly is.Perhaps the most parsimonious the-ory of creative cognition to incorporateevolutionary principles is that of BlindVariation and Selective Retention (BVSR)(Campbell, 1960). Indeed, his the-ory posits that creativity in humans“represent(s) cumulated inductiveachievements, stage by stage expansionsof knowledge beyond what could havebeen

Explanatory Reasoning for Image Understanding Using Formal Concept Analysis and Description Logics

Abstract: In this paper, we propose an original way of enriching description logics with abduction reasoning services. Under the aegis of set and lattice theories, we put together ingredients from mathematical morphology, description logics, and formal concept analysis. We propose computing the best explanations of an observation through algebraic erosion over the concept lattice of a background theory that is efficiently constructed using tools from formal concept analysis. We show that the defined operators are sound and complete and satisfy important rationality postulates of abductive reasoning. As a typical illustration, we consider a scene understanding problem. In fact, scene understanding can benefit from prior structural knowledge represented as an ontology and the reasoning tools of description logics. We formulate model based scene understanding as an abductive reasoning process. A scene is viewed as an observation and the interpretation is defined as the best explanation, considering the terminological knowledge part of a description logic about the scene context. This explanation is obtained from morphological operators applied on the corresponding concept lattice.

A tractable approach to abox abduction over description logic ontologies

Abstract: ABox abduction is an important reasoning mechanism for description logic ontologies. It computes all minimal explanations (sets of ABox assertions) whose appending to a consistent ontology enforces the entailment of an observation while keeps the ontology consistent. We focus on practical computation for a general problem of ABox abduction, called the query abduction problem, where an observation is a Boolean conjunctive query and the explanations may contain fresh individuals neither in the ontology nor in the observation. However, in this problem there can be infinitely many minimal explanations. Hence we first identify a class of TBoxes called first-order rewritable TBoxes. It guarantees the existence of finitely many minimal explanations and is sufficient for many ontology applications. To reduce the number of explanations that need to be computed, we introduce a special kind of minimal explanations called representative explanations from which all minimal explanations can be retrieved. We develop a tractable method (in data complexity) for computing all representative explanations in a consistent ontology. Experimental results demonstrate that the method is efficient and scalable for ontologies with large ABoxes.

Innovation in HCI: what can we learn from design thinking?

Abstract: Design thinking, a methodology originating from the design disciplines, oriented towards problem solving through a human-centered approach, rapid prototyping and abductive reasoning, has huge impact on innovation in business, education, health and other crucial domains. Many similarities, and differences, can be found between design thinking and a Human-Computer Interaction (HCI) design approach to innovation. The aim of the workshop is to explore these similarities and differences, with a goal of re-thinking possibilities, including combining practices towards increased impact of HCI in shaping innovative technologies for the future.

Introduction to Simulating the Past

Abstract: This is the introduction to a special issue of Journal of Archaeological Method and Theory focusing on modelling and simulation in archaeology. Archaeology is a discipline based on abductive reasoning, where the premises do not guarantee the conclusions. In other words, hypotheses in archaeology are generated on the basis of an incomplete set of observations, and the discovery or the acquisition of new information can modify the previously developed hypotheses. Abductive reasoning is a useful tool for developing explanations that are adequate to describe an J Archaeol Method Theory DOI 10.1007/s10816-014-9209-8

Prime Implicates and Prime Implicants: From Propositional to Modal Logic

Abstract: Prime implicates and prime implicants have proven relevant to a number of areas of artificial intelligence, most notably abductive reasoning and knowledge compilation. The purpose of this paper is to examine how these notions might be appropriately extended from propositional logic to the modal logic K. We begin the paper by considering a number of potential definitions of clauses and terms for K. The different definitions are evaluated with respect to a set of syntactic, semantic, and complexity-theoretic properties characteristic of the propositional definition. We then compare the definitions with respect to the properties of the notions of prime implicates and prime implicants that they induce. While there is no definition that perfectly generalizes the propositional notions, we show that there does exist one definition which satisfies many of the desirable properties of the propositional case. In the second half of the paper, we consider the computational properties of the selected definition. To this end, we provide sound and complete algorithms for generating and recognizing prime implicates, and we show the prime implicate recognition task to be PSPACE-complete. We also prove upper and lower bounds on the size and number of prime implicates. While the paper focuses on the logic K, all of our results hold equally well for multi-modal K and for concept expressions in the description logic ALC.

A conditional logic for abduction

Abstract: We propose a logic of abduction that (i) provides an appropriate formalization of the explanatory conditional, and that (ii) captures the defeasible nature of abductive inference. For (i), we argue that explanatory conditionals are non-classical, and rely on Brian Chellas’s work on conditional logics for providing an alternative formalization of the explanatory conditional. For (ii), we make use of the adaptive logics framework for modeling defeasible reasoning. We show how our proposal allows for a more natural reading of explanatory relations, and how it overcomes problems faced by other systems in the literature.

Quantum Structure in Cognition and the Foundations of Human Reasoning

Abstract: Traditional cognitive science rests on a foundation of classical logic and probability theory. This foundation has been seriously challenged by several findings in experimental psychology on human decision making. Meanwhile, the formalism of quantum theory has provided an efficient resource for modeling these classically problematical situations. In this paper, we start from our successful quantum-theoretic approach to the modeling of concept combinations to formulate a unifying explanatory hypothesis. In it, human reasoning is the superposition of two processes -- a conceptual reasoning, whose nature is emergence of new conceptuality, and a logical reasoning, founded on an algebraic calculus of the logical type. In most cognitive processes however, the former reasoning prevails over the latter. In this perspective, the observed deviations from classical logical reasoning should not be interpreted as biases but, rather, as natural expressions of emergence in its deepest form.

Perspective switching as event affordance: The ontogeny of abductive reasoning

Abstract: Abstract The present model identifies Index as the representational component driving children’s advances in abductive reasoning, capitalizing upon the means to subjunctivize within constructed events. As such, an essential semiotic device underlies the recognition of shifting perspectives to operationalize abductive reasoning within event profiles. Beyond using Index to establish the point of orientation (Origo), one “tries on” that Origo’s covert and overt orientation via deictic terms that encode Origo’s role as a conversational on-looker of an episode. This subjunctive competence entails taking note of cause-effect relations to anticipate the affective, social, cognitive, and physical viewpoints likely to be assumed by that Origo. Hypothesis-making then entails going beyond grounded experience to represent intermediate and final states of affairs for other Origos. Abductions require dynamically imaging how distinctive agents affect action schemes together with their relied-upon judgments to effectuate resultative states. The use of indexically grounded cognitions (given their role in preempting event relations) rivets the onlooker to the “why” of unexpected events and increases the likelihood that the guess of another within novel contexts is plausible. Shifting perspectives underlie abductions because they trigger defeasible but plausible explanations for puzzling events.

Creation of Need Knowledge in Organizations: An Abductive Framework

Abstract: A focus on needs and the ability to generate knowledge about needs is highly valuable for organizations because it extends the range of possible solutions and therefore enables them to create more innovative and sustainable products and services. Our paper will explore how a framework based on an abductive reasoning process for the creation and discovery of knowledge about needs in organizations can look like and what the main steps of such a framework are, in order to integrate this approach into the model of the knowledge-based firm. Moreover we will present empirical findings from a project with Austrian companies where this framework has been used.

X Goes First: Teaching Simple Games through Multimodal Interaction

Abstract: What would it take to teach a computer to play a game entirely through language and sketching? In this paper, we present an implemented program through which an instructor can teach the rules of simple board games using such input. We describe the architecture, information flow, and vocabulary of instructional events and walk through an annotated example. In our approach, the instructional and communication events guide abductive reasoning for language interpretation and help to integrate information from sketching and language. Having a general target representation enables the learning process to be viewed more as translation and problem solving than as induction. Lastly, learning by demonstration complements and extends instruction, resulting in concrete, operational rules.

Abductive Agents for Human Activity Monitoring

Abstract: We propose in this paper a novel architecture for human activity monitoring, following conceptual, technical and experimental claims. From a conceptual viewpoint, we propose to approach the interpretation of sensor data as embedded into a multidimensional frame involving functional and non-functional requirements. Functional requirements involve considering the monitored person's specicities as well as the task to be performed. Non-functional requirements qualify the system activity. This frame of interpretation is continuously rened, to cope with evolving situations or expectations from the Observer. From a technical viewpoint, we propose to develop a multi-Agent architecture as a means for dependable, exible monitoring. This paradigm allows to handle multiple, heterogeneous entities in a unied way. The Agents process incoming data with a dynamic population of hypotheses on several abstraction levels. This reasoning is abductive and fuzzy in nature. From the experimental viewpoint, we propose a dedicated evaluation approach to estimate the interpretative process unfolding. Functional and non-functional properties are presented to discuss the system's effectiveness, informativeness, sensitivity, eficiency and robustness, some of which are supported by qualitative, analytical discussions, others by quantitative measures.

How Analysts Think (?): Early Observations

Abstract: In this paper we describe work-in-progress to develop a description of the ways by which intelligence analysts engage in the thinking and reasoning processes when engaged in the intelligence analysis task. Such a model will be used to inform the design the interactive visual interfaces for a next generation intelligence analysis system. We introduce the concepts of fluidity and rigour as key characteristics of the analysts' thinking landscape.

An abductive reasoning approach to the belief-bias effect

Abstract: The tendency to accept or reject arguments based on own beliefs or prior knowledge rather than on the reasoning process is called the belief-bias effect. A psychological syllogistic reasoning task shows this phenomenon, wherein participants were asked whether they accept or reject a given syllogism. We discuss one case which is commonly assumed to be believable but not logically valid. By introducing abnormalities, abduction and background knowledge, we model this case under the weak completion semantics. Our formalization reveals new questions about observations and their explanations which might include some relevant prior abductive contextual information concerning some side-effect. Inspection points, introduced by Pereira and Pinto, allow us to express these definitions syntactically and intertwine them into an operational semantics.

Contextual Abductive Reasoning with Side-Effects

Abstract: The belief bias effect is a phenomenon which occurs when we think that we judge an argument based on our reasoning, but are actually influenced by our beliefs and prior knowledge. Evans, Barston and Pollard carried out a psychological syllogistic reasoning task to prove this effect. Participants were asked whether they would accept or reject a given syllogism. We discuss one specific case which is commonly assumed to be believable but which is actually not logically valid. By introducing abnormalities, abduction and background knowledge, we adequately model this case under the weak completion semantics. Our formalization reveals new questions about possible extensions in abductive reasoning. For instance, observations and their explanations might include some relevant prior abductive contextual information concerning some side-effect or leading to a contestable or refutable side-effect. A weaker notion indicates the support of some relevant consequences by a prior abductive context. Yet another definition describes jointly supported relevant consequences, which captures the idea of two observations containing mutually supportive side-effects. Though motivated with and exemplified by the running psychology application, the various new general abductive context definitions are introduced here and given a declarative semantics for the first time, and have a much wider scope of application. Inspection points, a concept introduced by Pereira and Pinto, allows us to express these definitions syntactically and intertwine them into an operational semantics.

Logical Foundations of Social Science Research

Abstract: In this chapter, I want to review the four inferential logics (1) induction, (2) deduction, (3) abduction, and (4) retroduction which we use to develop the conjectures or hypotheses when doing theory development.

Abductive Inference for Interpretation of Metaphors

Abstract: This paper presents a metaphor interpretation pipeline based on abductive inference. In this framework following (Hobbs, 1992) metaphor interpretation is modelled as a part of the general discourse processing problem, such that the overall discourse coherence is supported. We present an experimental evaluation of the proposed approach using linguistic data in English and Russian.

Generation and Selection of Abductive Explanations for Non-Omniscient Agents

Abstract: Among the non-monotonic reasoning processes, abduction is one of the most important. Usually described as the process of looking for explanations, it has been recognized as one of the most commonly used in our daily activities. Still, the traditional definitions of an abductive problem and an abductive solution mention only theories and formulas, leaving agency out of the picture. Our work proposes a study of abductive reasoning from an epistemic and dynamic perspective. In the first part we explore syntactic definitions of both an abductive problem in terms of an agent's information and an abductive solution in terms of the actions that modify the agent's information. We look at diverse kinds of agents, including not only omniscient ones but also those whose information is not closed under logical consequence and those whose reasoning abilities are not complete. In the second part, we look at an existing logical framework whose semantic model allows us to interpret the previously stated formulas, and we define two actions that represent forms of abductive reasoning.

Abductive understanding of dialogues about joint activities

Abstract: This paper examines the task of understanding dialogues in terms of the mental states of the participating agents. We present a motivating example that clarifies the challenges this problem involves and then outline a theory of dialogue interpretation based on abductive inference of these unobserved beliefs and goals, incremental construction of explanations, and reliance on domain-independent knowledge. After this, we describe UMBRA, an implementation of the theory that embodies these assumptions. We report experiments with the system that demonstrate its ability to accurately infer the conversants’ mental states even when some speech acts are unavailable. We conclude by reviewing related research on dialogue and discussing avenues for future study.

A Model Towards Using Evidence from Security Events for Network Attack Analysis

Abstract: Constructing an efficient and accurate model from security events to determine an attack scenario for an enterprise network is challenging. In this paper, we discuss how to use the information obtained from security events to construct an attack scenario and build an evidence graph. To achieve the accuracy and completeness of the evidence graph, we use Prolog inductive and abductive reasoning to correlate evidence by reasoning the causality, and use an anti-forensics database and a corresponding attack graph to find the missing evidence.

Automated reasoning using abduction for interpretation of medical signals

Abstract: This paper proposes an approach to leverage upon existing ontologies in order to automate the annotation of time series medical data. The annotation is achieved by an abductive reasoner using parsimonious covering theorem in order to determine the best explanation or annotation for specific user defined events in the data. The novelty of this approach resides in part by the system’s flexibility in how events are defined by users and later detected by the system. This is achieved via the use of different ontologies which find relations between medical, lexical and numerical concepts. A second contribution resides in the application of an abductive reasoner which uses the online and existing ontologies to provide annotations. The proposed method is evaluated on datasets collected from ICU patients and the generated annotations are compared against those given by medical experts.

Icon and Abduction: Situatedness in Peircean Cognitive Semiotics

Abstract: Differently from the anti-cartesianism defended by some embodied-situated cognitive scientists, which is predominantly anti-representationalist, for C. S. Peirce, mind is semiosis (sign-action) in a dialogical form, and cognition is the development of available semiotic material artifacts in which it is embodied as a power to produce interpretants (sign-effects). It takes the form of development of semiotic artifacts, such as writing tools, instruments of observation, notational systems, languages, and so forth. Our objective in this paper is to explore the connection between a semiotic theory of mind and the conception of situatedness and extended mind through the notions of iconicity and abductive inference, taking advantage of an empirical example of investigation in distributed problem solving (Tower of Hanoi).

Abductive sensemaking through sketching A categorization of the dimensions in sketching capacities in design

Abstract: This paper proposes design sketching as a way to make abductive reasoning manifest and concrete. Through sketching, the abductive sensemaking leaves the domain of abstract logics and becomes part of the researchers or practitioner’s reflective practice. This practice is especially evident through incorporating sketching as more than a specific technique, but also as ways of applying design thinking through acting upon the world. The paper presents sketching as an integral part of the design epistemology. Furthermore, a categorization of different dimensions in which sketching can be represented is presented. The main contribution is a discussion of whether this broader view on sketching capacities in design leaves room for further exploration into extended sketching capacities for design.