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.

Abductive Reasoning: Challenges Ahead

Abstract: The motivation behind the collection of papers presented in this THEORIA forum on Abductive reasoning is my book Abductive Reasoning: Logical Investigations into the Processes of Discovery and Explanation. These contributions raise fundamental questions. One of them concerns the conjectural character of abduction. The choice of a logical framework for abduction is also discussed in detail, both its inferential aspect and search strategies. Abduction is also analyzed as inference to the best explanation, as well as a process of epistemic change, both of which chal- lenge the argument-like format of abduction. Finally, the psychological question of whether humans reason abduc- tively according to the models proposed is also addressed. I offer a brief summary of my book and then comment on and respond to several challenges that were posed to my work by the contributors to this issue.

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.

Abductive reasoning as a basis to reproduce expert criteria in ECG atrial fibrillation identification.

Abstract: Objective This work aims at providing a new method for the automatic detection of atrial fibrillation, other arrhythmia and noise on short single-lead ECG signals, emphasizing the importance of the interpretability of the classification results. Approach A morphological and rhythm description of the cardiac behavior is obtained by a knowledge-based interpretation of the signal using the Construe abductive framework. Then, a set of meaningful features are extracted for each individual heartbeat and as a summary of the full record. The feature distributions can be used to elucidate the expert criteria underlying the labeling of the 2017 PhysioNet/CinC Challenge dataset, enabling a manual partial relabeling to improve the consistency of the training set. Finally, a tree gradient boosting model and a recurrent neural network are combined using the stacking technique to provide an answer on the basis of the feature values. Main results The proposal was independently validated against the hidden dataset of the Challenge, achieving a combined F 1 score of 0.83 and tying for the first place in the official stage of the Challenge. This result was even improved in the follow-up stage to 0.85 with a significant simplification of the model, attaining the highest score so far reported on the hidden dataset. Significance The obtained results demonstrate the potential of Construe to provide robust and valuable descriptions of temporal data, even with the presence of significant amounts of noise. Furthermore, the importance of consistent classification criteria in manually labeled training datasets is emphasized, and the fundamental advantages of knowledge-based approaches to formalize and validate those criteria are discussed.

Formalizing reasoning about change: a qualitative reasoning approach

Abstract: The development of a formal logic for reasoning about change has proven to be surprisingly difficult. Furthermore, the logics that have been developed have found surprisingly little application in those fields, such as Qualitative Reasoning, that are concerned with building programs that emulate human common-sense reasoning about change. In this paper, we argue that a basic tenet of qualitative reasoning practice--the separation of modeling and simulation--obviates many of the difficulties faced by previous attempts to formalize reasoning about change. Our analysis helps explain why the QR community has been nonplussed by some of the problems studied in the nonmonotonic reasoning community. Further, the formalism we present provides both the beginnings of a formal foundation for qualitative reasoning, and a framework in which to study a number of open problems in qualitative reasoning.