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.

Contextualized Possibilistic Networks with Temporal Framework for Knowledge Base Reliability Improvement

Abstract: Possibilistic abductive reasoning is particularly suited for diagnostic problem solving affected by uncertainty Being a Knowledge-Based approach, it requires a Knowledge Base consisting in a map of causal dependencies between failures (or anomalies) and their effects (symptoms) Possibilistic Causal Networks are an effective formalism for knowledge representation within this applicative field, but are affected by different issues This paper is focused on the importance of a proper management of explicit contextual information and of the addition of a temporal framework to traditional Possibilistic Causal Networks for the improvement of diagnostic process performances The necessary modifications to the knowledge representation formalism and to the learning approach are presented together with a brief description of an applicative test case for the concepts here discussed

End-of-Life Care and the Use of an Integrated Care Pathway

Abstract: Liverpool Care Pathway is an integrated care pathway (ICP) designed to ensure the provision of high-quality end-of-life care. However, the ICP has come under substantial criticism, suggesting that its use is related to poor care. This study explores nurses’ use of the ICP to dying patients in Norwegian nursing homes. We conducted a qualitative study using an abductive, mystery-focused method to analyze the experiences of 12 registered nurses. Our findings show that the nurses experienced the ICP as a very useful tool in end-of-life care, although they were actually working independently of the ICP in the provision of ongoing bedside care for the dying patients. This can be understood as following: (I) the ICP is not compatible with the complex problems of dying patients; therefore, nurses must tinker with the ICP in order to give dying patients proper and dignified care; (II) the ICP is a myth with symbolic power, legitimizing care makes nurses positive towards the ICP; and (III) using the ICP as a loosely coupled system creates novel effects on nursing practice. In this study, we have shown how the ICP creates a common culture through a process of individual and collective sensemaking, which we labelled clinical mindlines.

Fault diagnosis of telecommunication networks: Realistic abductive reasoning approach

Abstract: In this paper, we propose a method to diagnose faults in telecommunication networks by using the realistic abductive reasoning model (Prem and Venkataram, 1994). This model uses the abductive inference mechanism based on the parsimonious covering theory, with some new features added to the general model of diagnostic problem solving. The fault diagnosis knowledge is assumed to be represented in the form of causal chaining, namely, a hyper-bipartite network. The results obtained by the proposed model demonstrate its effectiveness in solving telecommunication network fault diagnostic problems

Bayesian Logic Programs for plan recognition and machine reading

Abstract: : Several real world tasks involve data that is uncertain and relational in nature. Traditional approaches like first-order logic and probabilistic models either deal with structured data or uncertainty, but not both. To address these limitations, statistical relational learning (SRL), a new area in machine learning integrating both first-order logic and probabilistic graphical models, has emerged in the recent past. The advantage of SRL models is that they can handle both uncertainty and structured/ relational data. As a result, they are widely used in domains like social network analysis, biological data analysis, and natural language processing. Bayesian Logic Programs (BLPs), which integrate both first-order logic and Bayesian networks are a powerful SRL formalism developed in the recent past. In this dissertation, we develop approaches using BLPs to solve two real world tasks plan recognition and machine reading.