Abductive reasoning

About: Abductive reasoning is a research topic. Over the lifetime, 1917 publications have been published within this topic receiving 44645 citations. The topic is also known as: abduction & abductive inference.

Reasoning with models

Abstract: We develop a model-based approach to reasoning, in which the knowledge base is represented as a set of tnodels (satisfying assignments) rather then a logical formula, and the set of queries is restricted. We show that for every propositional knowledge base (KB) there exists a set of characteristic models with the property that a query is true in KB if and only if it is satisfied by the models in this set. We fully characterize a set of theories for which the model-based representation is compact and provides efficient reasoning. These include some cases where the formula-based representation does not support efficient reasoning. In addition, we consider the model-based approach to abductive reasoning and show that for any propositional KB, reasoning with its model-based representation yields an abductive explanation in time that is polynomial in its size.

Approaches to abductive reasoning: an overview

Abstract: Abduction is a form of non-monotonic reasoning that has gained increasing interest in the last few years. The key idea behind it can be represented by the following inference rule $$O = \mathop C\limits_| - N = \mathop P\limits_|^| - O - \mathop C\limits_|^| - .$$ i.e., from an occurrence of ω and the rule “ϕ implies ω”, infer an occurrence of ϕ as aplausible hypothesis or explanation for ω. Thus, in contrast to deduction, abduction is as well as induction a form of “defeasible” inference, i.e., the formulae sanctioned are plausible and submitted to verification. In this paper, a formal description of current approaches is given. The underlying reasoning process is treated independently and divided into two parts. This includes a description ofmethods for hypotheses generation andmethods for finding the best explanations among a set of possible ones. Furthermore, the complexity of the abductive task is surveyed in connection with its relationship to default reasoning. We conclude with the presentation of applications of the discussed approaches focusing on plan recognition and plan generation.

Problems with peirce's concept of abduction

Abstract: Abductive reasoning takes place in forming "hypotheses" in order to explain "facts." Thus, the concept of abduction promises an understanding of creativity in science and learning. It raises, however, also a lot of problems. Some of them will be discussed in this paper. After analyzing the difference between induction and abduction (1), I shall discuss Peirce's claim that there is a "logic" of abduction (2). The thesis is that this claim can be understood, if we make a clear distinction between inferential elements and perceptive elements of abductive reasoning. For Peirce, the creative act of forming explanatory hypotheses and the emergence of "new ideas" belongs exclusively to the perceptive side of abduction. Thus, it is necessary to study the role of perception in abductive reasoning (3). A further problem is the question whether there is a relationship between abduction and Peirce's concept of "theorematic reasoning" in mathematics (4). Both forms of reasoning could be connected, because both are based on perception. The last problem concerns the role of instincts in explaining the success of abductive reasoning in science, and the question whether the concept of instinct might be replaced by methods of inquiry (5).

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.