Knowledge Base Reasoning based on Artificial Neural Network

Qaysar Mahdi

ISBN 10: 6137348407 / ISBN 13: 9786137348406

Published by LAP LAMBERT Academic Publishing Feb 2018, 2018

About this Item

Neuware -The main aim of this book is to introduce the knowledge base reasoning using artificial intelligence in computer science. This book provides a proposed system that represents two important topics: a propositional logic and non-classical logic. Then represents them in artificial neural network so that to provide an easy way for reasoning and making a hybrid system. The first part of this work uses a Connectionist Inductive Learning and Logic Programming, (CILP) algorithm which is a method that translates a propositional logic to a three layers neural network. The second part of the book uses a Connectionist Modal Logic (CML) algorithm. This algorithm acts as an extension language added to a logic program to represent a non-classical logic program. This will add an ability to interconnect a number of possible words, stationeries, clusters, logic programs and so on to make representing of real world more accurate than poor propositional logic. The proposed system has been applied successfully on all tested logic programs. It proves that the neural network can represent logic language and have ability to reason rather than handling a missing data in absence of background knowledge.Books on Demand GmbH, Überseering 33, 22297 Hamburg 104 pp. Englisch.

Seller Inventory # 9786137348406

Bibliographic Details

• Title: Knowledge Base Reasoning based on Artificial Neural Network
• Publisher: LAP LAMBERT Academic Publishing Feb 2018
• Publication Date: 2018
• Language: English
• ISBN 10: 6137348407
• ISBN 13: 9786137348406
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 7D millimeters 220 millimeters width by 150 millimeters height by 7 millimeters depth
• Weight: 173 grams

About this title

Synopsis

The main aim of this book is to introduce the knowledge base reasoning using artificial intelligence in computer science. This book provides a proposed system that represents two important topics: a propositional logic and non-classical logic. Then represents them in artificial neural network so that to provide an easy way for reasoning and making a hybrid system. The first part of this work uses a Connectionist Inductive Learning and Logic Programming, (CILP) algorithm which is a method that translates a propositional logic to a three layers neural network. The second part of the book uses a Connectionist Modal Logic (CML) algorithm. This algorithm acts as an extension language added to a logic program to represent a non-classical logic program. This will add an ability to interconnect a number of possible words, stationeries, clusters, logic programs and so on to make representing of real world more accurate than poor propositional logic. The proposed system has been applied successfully on all tested logic programs. It proves that the neural network can represent logic language and have ability to reason rather than handling a missing data in absence of background knowledge.

About the Author

Prof. Dr. Qaysar Mahdi supervised many PhD, MSc students in the field of communication, radar and computer engineering since 2000. His MSc and PhD were obtained in 1988 and 2002, respectively. He is now working as IT director in Ishik University and researcher. He published two new books and many papers in IEEE and Lambert Academic Publishing.