Knowledge Management

Regular price €122.99
A01=Jay Liebowitz
architecture
Artificial Intelligence Field
artificial intelligence in industrial history
Author_Jay Liebowitz
Category=KJM
Category=UYQ
CBR
constellation
decision support frameworks
eq_bestseller
eq_business-finance-law
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Expert Choice
Expert System
expert systems methodology
FIPA
FIPA ACL
functions
genetic
IBM Laptop
information systems integration
intelligent agent applications
Jade
Km Field
KMS
Knowledge Acquisition
Knowledge Acquisition Sessions
Knowledge Audit
knowledge discovery techniques
Knowledge Management
Knowledge Management Life Cycle
Knowledge Management Strategy
Knowledge Map
Knowledge Ontologies
Knowledge Representation Approaches
Knowledge Taxonomy
master
Multi-agent System Frameworks
Multi-agent Systems
Multiagent System
organizational learning
satellite
scheduling
search
slave
Specialty Agent
tabu
UMBC

Product details

  • ISBN 9780849310249
  • Weight: 430g
  • Dimensions: 156 x 234mm
  • Publication Date: 28 Mar 2001
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
Delivery/Collection within 10-20 working days

Our Delivery Time Frames Explained
2-4 Working Days: Available in-stock

10-20 Working Days: On Backorder

Will Deliver When Available: On Pre-Order or Reprinting

We ship your order once all items have arrived at our warehouse and are processed. Need those 2-4 day shipping items sooner? Just place a separate order for them!

Knowledge Management (KM) is strongly rooted in the discipline of Knowledge Engineering (KE), which in turn grew partly out of the artificial intelligence field. Despite their close relationship, however, many KM specialists have failed to fully recognize the synergy or acknowledge the power that KE methodologies, techniques, and tools hold for enhancing the state of the art in Knowledge Management. Knowledge Management: Learning from Knowledge Engineering addresses this vacuum. It gives concise, practical information and insights drawn from the author's many years of experience in the fields of expert systems and Knowledge Management. Based upon research, analyses, and illustrative case studies, this is the first book to integrate the theory and practice of artificial intelligence and expert systems with the current organizational and strategic aspects of Knowledge Management. The time has come for Knowledge Management professionals to appreciate the synergy between their work and the work of their counterparts in Knowledge Engineering. Knowledge Management: Learning from Knowledge Engineering is the ideal starting point for those in KM to learn from and exploit advances in that field, and thereby advance their own.
Liebowitz, Jay