Showing papers on "Abductive reasoning published in 2010"

Validation in interpretive management accounting research

Abstract: This paper extends and contributes to emerging debates on the validation of interpretive research (IR) in management accounting. We argue that IR has the potential to produce not only subjectivist, emic understandings of actors’ meanings, but also explanations, characterised by a certain degree of “thickness”. Mobilising the key tenets of the modern philosophical theory of explanation and the notion of abduction, grounded in pragmatist epistemology, we explicate how explanations may be developed and validated, yet remaining true to the core premises of IR. We focus on the intricate relationship between two arguably central aspects of validation in IR, namely authenticity and plausibility. Working on the assumption that validation is an important, but potentially problematic concern in all serious scholarly research, we explore whether and how validation efforts are manifest in IR using two case studies as illustrative examples. Validation is seen as an issue of convincing readers of the authenticity of research findings whilst simultaneously ensuring that explanations are deemed plausible. Whilst the former is largely a matter of preserving the emic qualities of research accounts, the latter is intimately linked to the process of abductive reasoning, whereby different theories are applied to advance thick explanations. This underscores the view of validation as a process, not easily separated from the ongoing efforts of researchers to develop explanations as research projects unfold and far from reducible to mere technicalities of following pre-specified criteria presumably minimising various biases. These properties detract from a view of validation as conforming to pre-specified, stable, and uniform criteria and allow IR to move beyond the “crisis of validity” arguably prevailing in the social sciences.

Abductive inference and delusional belief.

Abstract: Delusional beliefs have sometimes been considered as rational inferences from abnormal experiences. We explore this idea in more detail, making the following points. First, the abnormalities of cognition that initially prompt the entertaining of a delusional belief are not always conscious and since we prefer to restrict the term "experience" to consciousness we refer to "abnormal data" rather than "abnormal experience". Second, we argue that in relation to many delusions (we consider seven) one can clearly identify what the abnormal cognitive data are which prompted the delusion and what the neuropsychological impairment is which is responsible for the occurrence of these data; but one can equally clearly point to cases where this impairment is present but delusion is not. So the impairment is not sufficient for delusion to occur: a second cognitive impairment, one that affects the ability to evaluate beliefs, must also be present. Third (and this is the main thrust of our paper), we consider in detail what the nature of the inference is that leads from the abnormal data to the belief. This is not deductive inference and it is not inference by enumerative induction; it is abductive inference. We offer a Bayesian account of abductive inference and apply it to the explanation of delusional belief.

Advanced Models of Cognition for Medical Training and Practice

Abstract: I: Philosophical Issues: Information Representation.- Epistemological Constraints on Medical Knowledge-Based Systems.- Abductive Reasoning: Philosophical and Educational Perspectives in Medicine.- The Language of Medicine and the Modeling of Information.- II: Artificial Intelligence Issues: Knowledge-Based Systems.- AI Meets Decision Science: Emerging Synergies For Decision Support.- Computational Models of Cased-Based Reasoning for Medicine.- The Evaluation of Medical Expert Systems.- III: Technology and Artificial Intelligence Issues: Implementations.- Dynamic Decision-Making in Anesthesiology: Cognitive Models and Training Approaches.- From Expert Systems to Intelligent Tutoring Systems.- Expert Systems in Teaching Electrocardiography.- Review of Technological Products for Training.- IV: Psychological Issues: Medical Cognition.- Cognitive Frameworks for Clinical Reasoning: Application for Training and Practice.- Knowledge Application and Transfer for Complex Tasks in Ill-Structured Domains: Implications for Instruction and Testing in Biomedicine.- Psychological Modeling of Cognitive Processes in Knowledge Assessment by Experts: Some Convergent Issues with Psychological Modeling in Medical Reasoning.- Models of Cognition and Educational Technologies: Implications for Medical Training.- Encapsulation of Biomedical Knowledge.- V: Psychological Issues: Teaching and Learning in Medicine.- How One Learns Graph-Reading Skills for Solving Biochemistry Problems.- Who Will Catch the Nagami Fever? Causal Inferences and Probability Judgment in Mental Models of Diseases.- Mental and Qualitative (AI) Models of Cardiac Electrophysiology: An Exploratory Study in Comparative Cognitive Science.- Cognitive Effects of Practical Experience.- VI: Reflections on Practice: The Medical School Perspective.- The Dean and the Bear.- The European Medical Education Perspective.- Reflections on Practice in Medical Education: Perspectives from Spain.- Hungarian Medical Education: Present Problems and Future Plans for Eastern European Medical Schools.- List of Author Participants.- List of Other Participants.

Abductive reasoning and the formation of scientific knowledge within nursing research

Abstract: Peirce's notion of abductive reasoning and the way this reasoning can enhance forming of scientific knowledge within nursing research is of great importance. Abduction is the first stage of inquiry within which hypotheses are invented; they are then explicated through deduction and verified through induction. In an abductive model, new ideas emerge by taking various clues and restrictions into account, and by searching and combining existing ideas in novel ways. Thus, abduction can be developed further as a 'pure' form of inference and this gives means for analysing and organizing the abductive search explicitly within the research community.

Defeasible Classifications and Inferences from Definitions

Abstract: We contend that it is possible to argue reasonably for and against arguments from classifications and definitions, provided they are seen as defeasible (subject to exceptions and critical questioning). Arguments from classification of the most common sorts are shown to be based on defeasible reasoning of various kinds represented by patterns of logical reasoning called defeasible argumentation schemes. We show how such schemes can be identified with heuristics, or short-cut solutions to a problem. We examine a variety of arguments of this sort, including argument from abductive classification, argument from causal classification, argument from analogy-based classification and arguments from classification based on generalizations.

Bayesian abductive logic programs

Abstract: In this paper, we introduce Bayesian Abductive Logic Programs (BALPs), a new formalism that integrates Bayesian Logic Programs (BLPs) and Abductive Logic Programming (ALP) for abductive reasoning. Like BLPs, BALPs also combine first-order logic and Bayesian networks. However, unlike BLPs that use logical deduction to construct Bayes nets, BALPs employ logical abduction. As a result, BALPs are more suited for solving problems like plan/activity recognition and diagnosis that require abductive reasoning. First, we present the necessary enhancements to BLPs in order to support logical abduction. Next, we apply BALPs to the task of plan recognition and demonstrate its efficacy on two data sets. We also compare the performance of BALPs with several existing approaches for abduction.

Automata-Based Abduction for Tractable Diagnosis.

Abstract: Abductive reasoning has been recognized as a valuable com- plement to deductive inference for tasks such as diagnosis and integration of incomplete information despite its inherent computational complex- ity. This paper presents a novel, tractable abduction procedure for the lightweight description logic EL. The proposed approach extends recent research on automata-based axiom pinpointing (which is in some sense dual to our problem) by assuming information from a predefined ab- ducible part of the domain model if necessary, while the remainder of the domain is considered to be fixed. Our research is motivated by the need for efficient diagnostic reasoning for large-scale industrial systems where observations are partially incomplete and often sparse, but nevertheless the largest part of the domain such as physical structures is known. Tech- nically, we introduce a novel pattern-based definition of abducibles and show how to construct a weighted automaton that commonly encodes the definite and abducible part of the domain model. We prove that its behavior provides a compact representation of all possible hypotheses explaining an observation, and is in fact computable in PTime. Abductive reasoning is a method for generating hypotheses that explain an obser- vation based on a model of the domain, typically in the presence of incomplete data. Its non-monotonicity and explorative nature make abduction a promis- ing candidate for the interpretation of potentially incomplete information - a task which is much harder to accomplish using established monotonic inference methods such as deduction or the more elaborate axiom pinpointing. The appli- cations of abductive inference are diverse, ranging from text interpretation (1) to plan generation and analysis (2), and interpretation of sensor (3) or multimedia data (4). Our research on abductive inference is motivated by industrial applica- tions in Ambient Assisted Living and assistive diagnosis for complex technical devices. In these scenarios we found the underlying models being typically large, though not overly complex in their structure. The main consideration is therefore scalability with respect to the size of the domain model; to effectively support humans or to avoid consequential damage to machinery, information processing is subject to soft realtime constraints. Proc. 23rd Int. Workshop on Description Logics (DL2010), CEUR-WS 573, Waterloo, Canada, 2010.

Logical Leaps and Quantum Connectives: Forging Paths through Predication Space

Abstract: The Predication-based Semantic Indexing (PSI) approach encodes both symbolic and distributional information into a semantic space using a permutation-based variant of Random Indexing. In this paper, we develop and evaluate a computational model of abductive reasoning based on PSI. Using distributional information, we identify pairs of concepts that are likely to be predicated about a common third concept, or middle term. As this occurs without the explicit identification of the middle term concerned, we refer to this process as a “logical leap”. Subsequently, we use further operations in the PSI space to retrieve this middle term and identify the predicate types involved. On evaluation using a set of 1000 randomly selected cue concepts, the model is shown to retrieve with accuracy concepts that can be connected to a cue concept by a middle term, as well as the middle term concerned, using nearest-neighbor search in the PSI space. The utility of quantum logical operators as a means to identify alternative paths through this space is also explored.

Memory leaks detection in java by bi-abductive inference

Abstract: This paper describes a compositional analysis algorithm for statically detecting leaks in Java programs The algorithm is based on separation logic and exploits the concept of bi-abductive inference for identifying the objects which are reachable but no longer used by the program

Abductive Reasoning and Similarity: Some Computational Tools

Abstract: Abductive reasoning includes discovering new hypotheses or explanations. This chapter identifies several factors involved in abductive reasoning in an effort to move toward a theory of such reasoning. The chapter has an ultimate focus on the nature and influence of similarity. A major goal of our work is to develop computational tools that provide intelligent abductive suggestions to people engaged in discovering new knowledge. Novel abductive inferences often exhibit interesting similarities to the phenomena under investigation. Such similarities are not strong or direct but rather are often only obvious once the inference has been drawn. Some of our research is directed at discovering indirect similarities from text by using measures that are sensitive to indirect relations between terms in the text. By focusing on terms that are related but do not co-occur, potentially interesting indirect relations can be explored. Our work employs Random Indexing methods and Pathfinder networks to identify and display relations among terms in a text corpus. These displays are provided to individuals to suggest possible abductive inferences. We explore a variety of methods for identifying indirect similarities. The degree to which surprising and interesting inferences are suggested is the primary measure of success. Several examples are presented to illustrate the method: An analysis showing a positive relationship between (a) the strength of indirect similarity in one period of time and (b) the likelihood that the terms involved become directly related in future time This correlation supports the hypothesis that discoveries may be latent in such indirect similarities. Presumably, noticing such similarity brings indirectly related concepts together suggesting a new idea.

Informatics creativity: a role for abductive reasoning?

Abstract: IntroductionDespite the fact that creative problem solving is desirable at all levels, it is constantly in short supply Technology developers always wish they, and their peers, could devise novel solutions to the problems at hand Yet too often, common brainstorming efforts generate only fog or drizzle Part of the problem may be that while some technology trainers advocate the search for creative solutions, the learning side of the equation is little changed by a "be creative" lecture component This inquiry attempts to equip computer educators, and practitioners, with a broadened approach to fostering creativity, targeted at expanding discovery within Informatics areas Particularly, it is suggested that promoting abductive reasoning might help computer professionals understand the benefits of wider investigations within an expanded range of topics Although discussed additionally later, a short overview of the abductive reasoning approach is that "abduction, or inference to the best explanation, is a form of inference that goes from data describing something to a hypothesis that best explains or accounts for the data"Few professionals would suggest there is no need for new, creative solutions to the problems they encounter An example of the recognition of such needs is highlighted in industry Consider that each year Procter and Gamble spends nearly $2 billion looking for innovative ideas This R&D budget spans 150 science areas including biotechnology, imaging, and robotics Aside from pure research, P&G pursues "aggressive mining of the scientific literature," as well as employs upwards of 70 "technology entrepreneurs" around the world who analyze local markets to see what has been created elsewhere that might be acquired, modified, or simply spark new ideas internallySparking new computing ideas is the intent of this discourse Various overlapping and synergistic issues come to mind when considering what has been called "discovery informatics" Concepts suggested therein are extended here to consider the possible value of abductive reasoning in Informatics Specifically, how might abduction help generate creative approaches to problems as Informatics reaches into the fabric of so many disciplines Understanding this technique might assist Informatics workers to progress from being data rich and discovery poor, to a state of information wealth, as professionals benefit from applying new ideasIn 1990, abductive-related efforts in computing had already been occurring for nearly 20 years And yet, after another 15 years has passed, the awareness of the potential of abductive reasoning in computing fields remains relatively spotty The current growth of Informatics programs may offer a chance to change that profile Since Informatics programs tend to be more cross-disciplinary, it might achieve the goal of that 1990 Automated Abduction symposium---to help a wider range of researchers to recognize that "they might benefit from work on abduction by people in other areas" Overall, it may turn out that this diffusion cycle will be reminiscent of how slowly object-oriented programming came to be understood, let alone embracedWe set out to consider the potential of abductive reasoning to promote creativity within the interdisciplinary field of Informatics As will be presented in the following sections, we believe in the applicability of this reasoning style as a tool for expanding the power of Informatics as it seeks to solve complex problems in a wide range of disciplines Abductive reasoning is well suited for facing problems that are vague, or even unrecognized The goal? Turning massive amounts of data lead into tiny, although much more valuable, quantities of information gold

Bedside-to-Bench Pharmacology: A Complementary Concept to Translational Pharmacology

Abstract: Translational pharmacology is contemporary; it efficiently covers the progress of research from cellular and animal experiments to the patient. Its logical reasoning is deductive by definition. Abductive reasoning in pharmacological research is often placed second. Although straightforward processes might suit some organizational models, a reasoning process preferring only one direction may sacrifice opportunities. Both directions of reasoning may lead to independent yet comparable or even identical successes, and it is only by using both together that one gains the full benefit of human intellectual resources.

Approaching proof in school: from guided conjecturing and proving to a story of proof construction

Abstract: School approach to theorems has been a subject of major concern for mathematics education in the last two decades. Students' learning to produce proofs and their understanding of what does proof consist in (Balacheff, 1987) have been considered under different perspectives and with different aims: among them, how to make the students aware of the differences between proof and ordinary argumentation (Duval, 1991, 2007); how to favour students' access to the theoretical character of proof (M.A.Mariotti, 2000); how to exploit "cognitive unity" (which for some theorems allows students to exploit the arguments they produced in the conjecturing phase to construct the proof) in order to smooth the school approach to theorems (Boero, Garuti & Lemut, 2007); in what cases of cognitive unity do students meet difficulties in the passage from an inductive or abductive reasoning, to the deductive organization of arguments (lack of structural continuity: Pedemonte, 2007, 2008); what are the common aspects between ordinary argumentation and proving, and how to prepare students to proving by relying on those aspects (Douek, 1999a, 1999b; Boero, Douek & Ferrari, 2008). Previous research work helps us to formulate and situate some educational problems that arise in the school approach to theorems: how to tackle theorems for which cognitive unity does not work, or (if cognitive unity can work) when students meet important difficulties due to the lack of structural continuity? How to make the students aware of some salient characters of proving and proof? And how to lead them into some specific competencies of proving activity? In this paper we propose a possible way to tackle these problems in an integrated way. The idea is to guide students' constructive work on proving, then to help them focusing on the characteristics of the organisation of proof.

A dialogue model of belief

Abstract: This paper offers a new model of belief by embedding the Peircean account of belief into a formal dialogue system that uses argumentation schemes for practical reasoning and abductive reasoning. A belief is characterised as a stable proposition that is derived abductively by one agent in a dialogue from the commitment set (including commitments derived from actions and goals) of another agent. On the model (to give a rough summary), a belief is defined as a proposition held by an agent that (1) is not easily changed (stable), (2) is a matter of degree (held more or less weakly or strongly), (3) guides the goals and actions of the agent, and (4) is habitually or tenaciously held in a manner that indicates a strong commitment to defend it. It is argued that the new model overcomes the pervasive conflict in artificial intelligence between the belief-desire-intention model of reasoning and the commitment model.

Logic and the Law: Crossing the Lines of Discipline

Abstract: The present chapter is a small part of an effort to expose the logical structure of English criminal law. Our purpose here is to lay to rest some objections that might be raised against the project. A further aim is to show that, in particular cases, legal concepts actually respond well to logical analysis. We demonstrate this as regards the legal concept of proof beyond a reasonable doubt.

Abductive Belief Revision in Science

Abstract: The chapter starts from the observation that neither belief revision in the AGM tradition nor belief base revision contain mechanisms for learning new hypotheses from new evidences. After a discussion of two approaches to implement such learning mechanisms into input-driven belief revision, and of alternative accounts in terms of deliberate belief revision, an account of input-driven abductive belief expansion and revision is develop which intends to be as close as possible to belief revision in science and in common sense cognition. For this purpose, the chapter draws on a theory of abduction developed elsewhere. Abductive expansion and revision functions, including induction as a special case, are described within three specific domains: inductive generalization, factual abduction, and theoretical model abduction. It turns out that abductive belief revision does not satisfy the Levi-identity.

Speculative abductive reasoning for hierarchical agent systems

Abstract: Answer sharing is a key element in multi-agent systems as it allows agents to collaborate towards achieving a global goal. However exogenous knowledge of the world can influence each agent's local computation, and communication channels may introduce delays, creating multiple partial answers at different times. Agent's answers may, therefore, be incomplete and revisable, giving rise to the concept of speculative reasoning, which provides a framework for managing multiple revisable answers within the context of multi-agent systems. This paper extends existing work on speculative reasoning by introducing a new abductive framework to hierarchical speculative reasoning. This allows speculative reasoning in the presence of both negation and constraints, enables agents to receive conditional answers and to continue their local reasoning using default answers, thus increasing the parallelism of agents collaboration. The paper describes the framework and its operational model, illustrates the main features with an example and states soundness and completeness results.

Abductive Research Methods

Abstract: Three abductive research methods are described: (1) The multivariate statistical method of exploratory factor analysis is presented as an abductive method of theory generation that exploits an important principle of scientific inference known as the principle of the common cause. (2) The theory of explanatory coherence is an abductive method for evaluating the explanatory worth of competing theories. (3) Grounded theory method promotes the inductive generation of theories grounded in qualitative data. However, it can be plausibly reconstructed as an abductive account of scientific method. It is recommended that these methods should be part of the methodological armamentarium of educational and social science researchers.

A Logic of Ethical Information

Abstract: The work of Luciano Floridi lies at the interface of philosophy, information science and technology, and ethics, an intersection whose existence and significance he was one of the first to establish. His closely related concepts of a philosophy of information (PI), informational structural realism, information logic (IL), and information ethics (IE) provide a new ontological perspective from which moral concerns can be addressed, especially but not limited to those arising in connection with the new information and communication technologies. In this paper, I relate Floridi's approach to another novel perspective, namely, that of an extension of logic to complex real processes, including those of information production and transfer. This non-propositional, non-truth-functional logic (logic in reality (LIR)) is grounded in the fundamental dualism (dynamic opposition) inherent in energy and accordingly present at all levels of reality. The LIR description of the dynamics of processes and their evolution is relevant to what Floridi refers to as the possible non-linguistic aspects of information. It suggests answers to some of Floridi's “outstanding problems” in PI related to the ontological status of information and how it is used in cognition. Floridi's IL retains the formal structure of the doxastic and epistemic logics from which he correctly distinguishes it and is the basis for his conceptual PI. However, LIR fulfills Floridi's implied requirement that logic be regarded as a natural phenomenon dealing with other natural phenomena, recovering its original philosophical function. LIR provides a logical foundation for discussion of ethical questions based on kinds of information that complements IL. Both are reconsiderations of logic that, as Marijuan suggests, may be necessary for the advancement of information technology in an ethical direction (cf. also Brenner). IE focuses on entities as constituted by information in an overall strategy that generalizes the concept of moral agents. LIR and its related ontology naturalize critical aspects of Floridi's theses, especially, the moral value of being as such and a non-separable joint responsibility of individuals and groups. I compare IE to other current approaches to ethics and information technology (e.g., phenomenological and social constructivist). Ethical information is defined “ecologically” in process terms as reality in a physical space (cf. Floridi), with an intentional “valence,” positive and negative, in the morally valued interaction between producer and receiver. LIR is neither topic-neutral nor context independent and can support an ethics involving apparently contradictory perspectives (e.g., internalist and externalist). Ethics involves practical reasoning, and unlike standard logics, LIR supports Magnani's approach to abductive reasoning in rational moral decision making. The basis of moral responsibility and the consequent behavior of individuals involved in information and communications technologies is the same logical–metaphysical principle of dynamic opposition instantiated at other levels of reality. The way moral responsibilities are actively accepted (or not) by individuals supervenes on their primitive psychological structure, which in turn reflects an evolutionary development grounded in the fundamental dualism of the physical world. The paper concludes with some suggestions of areas of philosophical research, such as causality, identity, and the ontological turn, where convergence of the Floridi and LIR approaches might be envisaged. Their overall motivation is the same, namely, the development of strategies for reinforcing and increasing ethical sensitivity wherever possible. The ethical information concept outlined in the paper supports the function of IE, assigned to it by Floridi, of potentially determining what is right and what is wrong.

Abduction and Meaning in Evolutionary Soundscapes

Abstract: The creation of an artwork named RePartitura is discussed here under principles of Evolutionary Computation (EC) and the triadic model of thought: Abduction, Induction and Deduction, as conceived by Charles S. Peirce. RePartitura uses a custom-designed algorithm to map image features from a collection of drawings and an Evolutionary Sound Synthesis (ESSynth) computational model that dynamically creates sound objects. The output of this process is an immersive computer generated sonic landscape, i.e. a synthesized Soundscape. The computer generative paradigm used here comes from the EC methodology where the drawings are interpreted as a population of individuals as they all have in common the characteristic of being similar but never identical. The set of specific features of each drawing is named as genotype. Interaction between different genotypes and sound features produces a population of evolving sounds. The evolutionary behavior of this sonic process entails the self-organization of a Soundscape, made of a population of complex, never-repeating sound objects, in constant transformation, but always maintaining an overall perceptual self-similarity in order to keep its cognitive identity that can be recognize for any listener. In this article we present this generative and evolutionary system and describe the topics that permeates from its conceptual creation to its computational implementation. We underline the concept of self-organization in the generation of soundscapes and its relationship with computer evolutionary creation, abductive reasoning and musical meaning for the computational modeling of synthesized soundscapes.

Abductive Inference in Probabilistic Logic Programs

Abstract: Action-probabilistic logic programs (ap-programs) are a class of probabilistic logic programs that have been extensively used during the last few years for modeling behaviors of entities. Rules in ap-programs have the form "If the environment in which entity E operates satisfies certain conditions, then the probability that E will take some action A is between L and U". Given an ap-program, we are interested in trying to change the environment, subject to some constraints, so that the probability that entity E takes some action (or combination of actions) is maximized. This is called the Basic Probabilistic Logic Abduction Problem (Basic PLAP). We first formally define and study the complexity of Basic PLAP and then provide an exact (exponential) algorithm to solve it, followed by more efficient algorithms for specific subclasses of the problem. We also develop appropriate heuristics to solve Basic PLAP efficiently.

Model round-trip engineering

Abstract: In the quest for shorter time-to-market, higher quality and reduced cost, model-driven software development has emerged as a promising approach to software engineering. The central idea is to promote models to first-class citizens in the development process. Starting from a set of very abstract models in the early stage of the development, they are refined into more concrete models and finally, as a last step, into code. As early phases of development focus on different concepts compared to later stages, various modelling languages are employed to most accurately capture the concepts and relations under discussion. In light of this refinement process, translating between modelling languages becomes a time-consuming and error-prone necessity. This is remedied by model transformations providing support for reusing and automating recurring translation efforts. These transformations typically can only be used to translate a source model into a target model, but not vice versa. This poses a problem if the target model is subject to change. In this case the models get out of sync and therefore do not constitute a coherent description of the software system anymore, leading to erroneous results in later stages. This is a serious threat to the promised benefits of quality, cost-saving, and time-to-market. Therefore, providing a means to restore synchronisation after changes to models is crucial if the model-driven vision is to be realised. This process of reflecting changes made to a target model back to the source model is commonly known as Round-Trip Engineering (RTE). While there are a number of approaches to this problem, they impose restrictions on the nature of the model transformation. Typically, in order for a transformation to be reversed, for every change to the target model there must be exactly one change to the source model. While this makes synchronisation relatively “easy”, it is ill-suited for many practically relevant transformations as they do not have this one-to-one character. To overcome these issues and to provide a more general approach to RTE, this thesis puts forward an approach in two stages. First, a formal understanding of model synchronisation on the basis of non-injective transformations (where a number of different source models can correspond to the same target model) is established. Second, detailed techniques are devised that allow the implementation of this understanding of synchronisation. A formal underpinning for these techniques is drawn from abductive logic reasoning, which allows the inference of explanations from an observation in the context of a background theory. As non-injective transformations are the subject of this research, there might be a number of changes to the source model that all equally reflect a certain target model change. To help guide the procedure in finding “good” source changes, model metrics and heuristics are investigated. Combining abductive reasoning with best-first search and a “suitable” heuristic enables efficient computation of a number of “good” source changes. With this procedure Round-Trip Engineering of non-injective transformations can be supported.

Temporal Abductive Reasoning Based Diagnosis and Alarm for Power Grid

Abstract: When faults occur in power grid, a lot of alarms are generated There are temporal constraints among these alarms and such temporal information is essential for fault diagnosis As one of basic inference methods for diagnosis problems, abductive reasoning is known as using domain theories to infer about reasons or explanations of observations and results Abductive reasoning based inference methods can find causes and evolution processes, which is useful for power grid diagnosis and alarm problems In this paper, abductive reasoning was introduced into power grid diagnosis and alarm Firstly, the temporal and logical relationships of faults and alarms in power grid were formulated as temporal constraints and inference formulas Then a suit of temporal abductive reasoning based algorithms were proposed to solve fault evolution process using observed alarms The proposed algorithms can identify break alarms, exception alarms and missing alarms which can reduce the uncertainty of diagnosis results These algorithms can be developed as a function of intelligent alarm method for power grid, or used for further fault diagnosis accompanying with other methods

Hume: the power of abduction and simple observation in economics

Abstract: In Hume’s epistemology, induction leads to discovery in matters of fact. However, because of the poor data Hume analyzes the balance of trade with a thought experiment, doing what Mill makes explicit afterwards: reason from assumptions, to reach conclusions which are true in the abstract. Hume’s potential explanation, what Peirce later calls abduction, is backed by a case study, the price revolution of the 16th century, which supports half his abductive inference, when money supply is multiplied fivefold. Given that economics reasons abductively, Hume’s attention to realistic hypotheses and the adjustment process matters.

Abductive reasoning for continual dialogue understanding

Abstract: In this paper we present a continual context-sensitive abductive framework for understanding situated spoken natural dialogue. The framework builds up and refines a set of partial defeasible explanations of the spoken input, trying to infer the speaker's intention. These partial explanations are conditioned on the eventual verification of the knowledge gaps they contain. This verification is done by executing test actions, thereby going beyond the initial context. The approach is illustrated by an example set in the context of human-robot interaction.

Mining timed sequences with tom4l framework

Abstract: We introduce the problem of mining sequential patterns in large database of sequences using a Stochastic Approach. An example of patterns we are interested in is : 50% of cases of engine stops in the car are happened between 0 and 2 minutes after observing a lack of the gas in the engine, produced between 0 and 1 minutes after the fuel tank is empty. We call this patterns “signatures”. Previous research have considered some equivalent patterns, but such work have three mains problems : (1) the sensibility of their algorithms with the value of their parameters, (2) too large number of discovered patterns, and (3) their discovered patterns consider only ”after“ relation (succession in time) and omit temporal constraints between elements in patterns. To address this issue, we present TOM4L process (Timed Observations Mining for Learning process) which uses a stochastic representation of a given set of sequences on which an inductive reasoning coupled with an abductive reasoning is applied to reduce the space search. The results obtained with an application on very complex real world system are also presented to show the operational character of the TOM4L process.

Subjective Logic Based Approach to Modeling Default Reasoning for Visual Surveillance

Abstract: In forensic analysis of visual surveillance data, ‘default reasoning’ can play an important role for deriving plausible semantic conclusions under incomplete and contradictory information about scenes. In this paper, we present an inference framework for default reasoning using Subjective Logic theory. Subjective Logic is a relatively new branch of probabilistic logic that allows explicit representation of ignorance about knowledge in a model called subjective opinion and that also comes with a rich set of operators thereby, having big potential as a tool for belief representation and reasoning. However, its application to visual surveillance is in its infancy and its use for default reasoning is not reported yet. Therefore, the aim of this paper is to bestow the ability of default reasoning on subjective logic and show the feasibility of using the introduced inference framework for visual surveillance. Among the approaches to enable default reasoning, the Bilattice framework is one that is well known and demonstrated for visual surveillance. For deriving the usage of subjective logic for default reasoning, we first discuss the similarity between the partial ignorance concept in subjective logic and the concept of degree of information in Bilattice based structure for multivalued default logic. Then we introduce the inference mechanism for default reasoning by mapping multi-logic-values into subjective opinion and combining operators in subjective logic. Finally, we present some illustrative reasoning examples in typical visual surveillance scenarios.

Distributed abductive reasoning with constraints

Abstract: Abductive inference has many known applications in multi-agent systems. However, most abductive frameworks rely on a centrally executed proof procedure whereas many of the application problems are distributed by nature. Confidentiality and communication overhead concerns often preclude transmitting all the knowledge required for centralised reasoning. We present in this paper a novel multi-agent abductive reasoning framework underpinned by a flexible and extensible distributed proof procedure that permits collaborative abductive reasoning with constraints between agents over decentralised knowledge.

Disease Models, Part II: Querying & Applications

Abstract: In the previous chapter, the mathematical formalisms that allow us to encode medical knowledge into graphical models were described. Here, we focus on how users can interact with these models (specifically, belief networks) to pose a wide range of questions and understand inferred results - an essential part of the healthcare process as patients and healthcare providers make decisions. Two general classes of queries are explored: belief updating, which computes the posterior probability of the network variables in the presence of evidence; and abductive reasoning, which identifies the most probable instantiation of network variables given some evidence. Many diagnostic, prognostic, and therapeutic questions can be represented in terms of these query Types. For models that are complex, exact inference techniques are computationally intractable; instead, approximate inference methods can be leveraged. We also briefly cover special classes of belief networks that are relevant in medicine: probabilistic relational models, which provide a compact representation of large number of propositional variables through the use of first-order logic; influence diagrams, which provide a means of selecting optimal plans given cost/preference constraints; and naive Bayes classifiers. Importantly, the question of how to validate the accuracy of belief networks is explored through cross validation and sensitivity analysis. Finally, we explore how the intrinsic properties of a graphical model (e.g., variable selection, structure, parameters) can assist users with interacting with and understanding the results of a model through feedback. Applications of Bayesian belief networks in image processing, querying, and case-based retrieval from large imaging repositories are demonstrated.

Musical listening and abductive reasoning: Contributions of C.S. Peirce’s

Abstract: Background in music philosophy. Questions about musical meaning are usually discussed within the area of philosophy of music. These questions gained particular urgencyin the Modern Age, when music had lost its connection with the old cosmologies that assured its position among the other disciplines related to harmony and numbers. In the last centuries philosophers and composers have tried to explain music as art and one of the most prominent attempts was the formalist perspective advocated by Hanslick. From that perspective music is considered on its own without any required connection with something non-musical, and its meaning or its content consists of the very unfolding of musical structures over time that are intelligible to the intellect through some form of reasoning. Background in music psychology. We consider two psychological theories of musical meaning that have been developed by two authors: Leonard Meyer and David Huron. Meyer created a theory of musical meaning based on the Gestalt principles and the practice of music analysis; Huron has constructed a theory based on experimental psychology and statistical analysis of music. On the one hand, both theories are complementary, especially regarding the role hypotheses have in the process of music signification; on the other hand, both lack an explanation of how hypotheses are generated. Aims. This paper aims at connecting the contributions of C.S. Peirce’s philosophy to the studies and investigations of musical meaning. Firstly, we consider his pragmatic concept of meaning; secondly, we analyze the role abductive reasoning has in his logic of discovery, outlining how the generation and evaluation of hypotheses can help to explain an encountered phenomenon. Thirdly, we apply the insights derived from this to an analysis of musical meaning, by indicating how a meaningful interpretation of a musical piece can be provided through the generation of hypotheses about its underlying structure. Main contribution. If the assumption is correct that hypotheses formulation is at the basis of music signification processes, we believe that Peircean philosophy, especially his semiotics, can help to elucidate how hypotheses are generated during music listening, furnishing an interesting and fruitful picture of musical meaning and complementing the psychological perspective on it with a logical and pragmatic point of view. Implications. C.S. Peirce’s thought is extremely interdisciplinary. The Peircean approach to musical meaning in collaboration with empirical studies of music psychology, can offer a more complete logical description of hypothesis generation (the basis of music signification). Moreover, the Peircean approach can strengthen the speculative practice of music philosophy, by providing a pragmatic and logical concept of meaning in music in close dialogue with scientific approaches.