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.

Acquisition and validation of expert knowledge by using causal models

Abstract: In this paper, we present techniques to support knowledge acquisition and validation within the framework of SGES Our approach can be called a causal model-based knowledge acquisition process It applies to a KBS composed of two knowledge bases: the expert knowledge base refering to the heuristic level is represented by production rules and will form the operational knowledge base, and the causal knowledge base composed of causal models When new expert knowledge (a production rule) is acquired, abductive reasoning based on causal models provides justifications which are then analyzed with appropriate criteria These justifications are useful for refining and extending an initial expert knowledge base: they can be used to propose explanations, to comment on rules, to control them, to suggest modifications or other rules Our approach has been applied to the design of a medical diagnostic reasoning system for electromyography Examples in this field are used in the paper

Multi-Agent Systems Specification and Certification: A Situation and State Calculus Approach

Abstract: We address the topic of specifying multi-agent systems using the situation and state calculus (SSC). SSC has been proposed as an extension of the situation calculus to overcome some limitations of the usual notion of state. The envisaged multi-agent system specification framework allows the uniform treatment of both local and global properties, providing also techniques for reasoning about such specifications. When a certain intended property is not inferred from a specification, we cannot always just add to it the corresponding formula. Indeed, it is often the case that specification axioms are required to be formulae of a certain kind. The task of identifying the new axioms that should be added to the specification in order to ensure the intended property has an abductive nature. Herein, we develop abductive reasoning techniques to tackle this problem.

Students’ Thinking Process When Using Abductive Reasoning in Problem Solving

Abstract: Background : Abductive reasoning is the process of making conjectures to explain surprising observations. Although this conjecture is not certain to be true, in solving a problem, this reasoning is very helpful to determine the best solution strategy. Objectives : The study aims to investigate whether all types of abductive reasoning lead to the formation of new schemes. Design : This research used a qualitative approach with a descriptive exploratory design. Setting and Participants : A total of 41 students of the bachelor of mathematics education program were involved in solving a task and then 8 persons were chosen to be deeply interviewed which representing the types of undercoded and overcoded abductive reasoning. Data collection and analysis : The collected data were the results of the students’ works and task-based interviews. Piaget's schema theory was used to analyze students' thinking processes using abductive reasoning. The analysis was carried out at all steps of problem solving, namely understanding the problem, devising a plan, carrying out the plan, and looking back. Results : Those who carried out overcoded abductive reasoning used this reasoning to determine problem solving strategies. Meanwhile, those who carried out undercoded abductive reasoning used it to determine problem solving strategies as well as to form new schemes. Conclusions : The results showed that students who did abductive reasoning did not always produce new schemes. This study also notes that the truth value of answers from the application of abductive reasoning in problem solving was open and the importance of the look back step to perform accommodation.

Case-Based Reasoning Systems

Abstract: This book reports on a set of recently implemented intelligent systems, having the case-based reasoning (CBR) methodology as their core. The selected works witness the heterogeneity of the domains in which CBR can be exploited, but also reveal some common directions that are clearly emerging in this specific research area. The present chapter provides a brief introduction to CBR, for readers unfamiliar with the topic. It then summarizes the main research contributions that will be presented in depth in the following chapters of this book.