Argumentation for Knowledge Representation, Conflict Resolution, Defeasible Inference and Its Integration with Machine Learning

Introduction: Artificial intelligence (AI) is widespread in many areas, including medicine. However, it is unclear what exactly AI encompasses. This paper aims to provide an improved understanding of medical AI and its constituent fields, and their interplay with knowledge representation (KR). Methods: We followed a Wittgensteinian approach (“meaning by usage”) applied to content metadata labels, using the Medical Subject Headings (MeSH) thesaurus to classify the field. To understand and characterize medical AI and the role of KR, we analyzed: (1) the proportion of papers in MEDLINE related to KR and various AI fields; (2) the interplay among KR and AI fields and overlaps among the AI fields; (3) interconnectedness of fields; and (4) phrase frequency and collocation based on a corpus of abstracts. Results: Data from over eighty thousand papers showed a steep, six-fold surge in the last 30 years. This growth happened in an escalating and cascading way. A corpus of 246,308 total words containing 21,842 unique words showed several hundred occurrences of notions such as robotics, fuzzy logic, neural networks, machine learning and expert systems in the phrase frequency analysis. Collocation analysis shows that fuzzy logic seems to be the most often collocated notion. Neural networks and machine learning are also used in the conceptual neighborhood of KR. Robotics is more isolated. Conclusions: Authors note an escalation of published AI studies in medicine. Knowledge representation is one of the smaller areas, but also the most interconnected, and provides a common cognitive layer for other areas.

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2. General intelligence as the Holy Grail

10.1093/actrade/9780199602919.003.0002 ◽

2018 ◽

Cited By ~ 1

Author(s):

Margaret A. Boden

Keyword(s):

Machine Learning ◽

Knowledge Representation ◽

Distributed Cognition ◽

State Of The Art ◽

General Intelligence ◽

Frame Problem ◽

Holy Grail ◽

Artificial General Intelligence ◽

The Frame Problem ◽

Forms Of Knowledge

A host of state-of-the-art AI applications exist, designed for countless specific tasks and used in almost every area of life, by laymen and professionals alike. Many outperform even the most expert humans. In that sense, progress has been spectacular. But the AI pioneers were also hoping for systems with general intelligence. ‘General intelligence as the Holy Grail’ explains why artificial general intelligence is still highly elusive despite recent increases in computer power. It considers the general AI strategies in recent research—heuristics, planning, mathematical simplification, and different forms of knowledge representation—and discusses the concepts of the frame problem, agents and distributed cognition, machine learning, and generalist systems.

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Conflict resolution using relation classification: High-level data fusion in data integration

Computer Science and Information Systems ◽

10.2298/csis200131014n ◽

2021 ◽

pp. 14-14

Author(s):

Zeinab Nakhaei ◽

Ali Ahmadi ◽

Arash Sharifi ◽

Kambiz Badie

Keyword(s):

Machine Learning ◽

Conflict Resolution ◽

Data Integration ◽

Data Fusion ◽

Estimated Parameters ◽

True Value ◽

Level Data ◽

Fusion Methods ◽

Relation Classification ◽

High Level

The aim of conflict resolution in data integration systems is to identify the true values from among different and conflicting claims about a single entity provided by different data sources. Most data fusion methods for resolving conflicts between entities are based on two estimated parameters: the truthfulness of data and the trustworthiness of sources. The relations between entities are however an additional source of information that can be used in conflict resolution. In this article, we seek to bridge the gap between two important broad areas, relation estimation and truth discovery, and to demonstrate that there is a natural synergistic relationship between machine learning and data fusion. Specifically, we use relational machine learning methods to estimate the relations between entities, and then use these relations to estimate the true value using some fusion functions. An evaluation of the results shows that our proposed approach outperforms existing conflict resolution techniques, especially where there are few reliable sources.

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Transport Infrastructure Connectivity and Conflict Resolution: A Machine Learning Comparison of Developed and Developing Countries

SSRN Electronic Journal ◽

10.2139/ssrn.3667398 ◽

2020 ◽

Author(s):

JI Luo ◽

Guijun Wang ◽

Greta Pesce ◽

Qiao Chen

Keyword(s):

Machine Learning ◽

Developing Countries ◽

Conflict Resolution ◽

Transport Infrastructure

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Inductive logic programming applied for knowledge representation in computer music/ Programação lógica indutiva aplicada para representação do conhecimento em música computacional

Brazilian Applied Science Review ◽

10.34115/basrv5n4-009 ◽

2021 ◽

Vol 5 (4) ◽

pp. 1840-1857

Author(s):

Clenio B. Gonçalves Junior ◽

Murillo Rodrigo Petrucelli Homem

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Knowledge Representation ◽

Logic Programming ◽

Inductive Logic Programming ◽

Computer Music ◽

Inductive Logic ◽

Essential Element ◽

Research Field ◽

Musical Knowledge

In Computer Music, the knowledge representation process is an essential element for the development of systems. Methods have been applied to provide the computer with the ability to generate conclusions based on previously established experience and definitions. In this sense, Inductive Logic Programming presents itself as a research field that incorporates concepts of Logic Programming and Machine Learning, its declarative character allows musical knowledge to be presented to non-specialist users in a naturally understandable way. The present work performs a systematic review based on approaches that use Inductive Logic Programming in the representation of musical knowledge. Questions that these studies seek to address were raised, as well as identifying characteristic aspects related to their application.

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