AUTOMATION OF COMMONSENSE REASONING: A STUDY ON FEASIBLE KNOWLEDGE REPRESENTATION TECHNIQUES

Article Sidebar

PDF
Published: Dec 22, 2017
DOI: https://doi.org/10.26483/ijarcs.v8i9.5158
Keywords:
commonsense reasoning, artificial intelligence, intelligent agent, knowledge representation

Main Article Content

Chandan Hegde
Surana PG Centre
Dr. Ashwini K

Abstract

Commonsense reasoning is a branch of artificial intelligence that deals with the simulation of human ability to make decisions during the situations that we encounter every day. This paper presents a feasibility study of necessary knowledge representation techniques used in commonsense reasoning study. Simulating the reasoning is a very challenging task since an intelligent agent that is ought to implement reasoning is not more than a computer itself. This paper is also intended at projecting some hurdles in the choice of knowledge representation. The diversity of issues that makes commonsense reasoning very hard to implement are definitely not few in number. Hence, the effort is to address only the problem of knowledge representation for commonsense reasoning by considering some of the major representation techniques which is the main point of discussion of this paper.

Downloads

Download data is not yet available.

Article Details

Issue
Vol. 8 No. 9 (2017): NOVEMBER-DECEMBER 2017
Section
Articles

COPYRIGHT

Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
  • The journal allows the author(s) to retain publishing rights without restrictions.
  • The journal allows the author(s) to hold the copyright without restrictions.

References

Marvin Minsky, The Emotion Machine: Commonsense Thinking, Artificial Intelligence and The Future of the Human Mind, Simon & Schuster, 2006.

S Natale, A Ballatore, “Imagine the Thinking Machine: Technological Myths and the Rise of Artificial Intelligenceâ€, International Journal of Research into New Media Technologies, June 2017.

JR Hattrick-Simpers, JM Gregoire, AG Kusne, “Perspective: Compisition-structure-property mapping in high throughout experiments: Turining data into knowledgeâ€, APL Materials, May 2016.

Ernest Davis, “Logical Formulizations of Commonsense Reasoning: A Surveyâ€, Journal of Artificial Intelligence Research, Vol 59, pp 651-723, August 2017.

P Kapanipathi, P Jain, C Venkataramani, A Sheth, “User Interests Identification on Twitter Hiierarchical Knowledge Baseâ€, ESWC: The Semantic Web: Trends and Challenges, pp 99-113, 2014.

B Kamsu-Foguem, G Tchuente-Foguem, C Foguem, “Using Conceptual Graphs for clinical guidelines Representation and Knowledge Visualizationâ€, Information System Frontiers, Vol 16, Issue 4, pp 571-589, September 2014.

BL Webber, NJ Nilsson, Readings in Artificial Intelligence, Morgan Kaufmann, 2014.

P Norvig, SJ Russel, Artificial Intelligence: A Modern Approach, 3rd Edition, Prentice Hall, 2009.

JF Sowa, Principles of Semantic Networks: Exploration in the Representation of Knowledge, Morgan Kaufmann, 2014.

W Huang, X Lou, “Commonsense Reasoning in a Deeper Way: by Discovering Relations Between Predicatesâ€, International Conference on Agents and Artificial Intelligence, 2017.