Edited by
Bernadette Bouchon-Meunier, CNRS-UPMCS, LIP6, Paris, France
Giulianella Coletti, University of Perugia, Italy
Ronald Yager, Iona College, Machine Intelligence Institute, New York, U.S.A.
Description
The volume "Modern Information Processing: From Theory to Applications," edited by Bernadette Bouchon-Meunier, Giulianella Coletti and
Ronald Yager, is a collection of carefully selected papers drawn from the program of IPMU'04, which was held in Perugia, Italy.
The book represents the cultural policy of IPMU conference which is not focused on narrow range of methodologies, but on the contrary
welcomes all the theories for the management of uncertainty and aggregation of information in intelligent systems, providing a medium
for the exchange of ideas between theoreticians and practitioners in these and related areas.
The book is composed by 7 sections:
UNCERTAINTY
PREFERENCES
CLASSIFICATION AND DATA MINING
AGGREGATION AND MULTI-CRITERIA DECISION MAKING
KNOWLEDGE REPRESENTATION
•The book contributes to enhancement of our ability to deal effectively with uncertainty in all of its manifestations.
•The book
can help to build brigs among theories and methods methods for the management of uncertainty.
•The book addresses issues which have
a position of centrality in our information-centric world.
•The book presents interesting results devoted to representing knowledge:
the goal is to capture the subtlety of human knowledge (richness) and to allow computer manipulation (formalization).
•The book contributes
to the goal: an efficient use of the information for a good decision strategy.
APPLIED DOMAINS
Audience:
Researchers oriented to theory and application of methods for handling partial knowledge in intelligent systems. Also for Ph.D. students
in mathematics and computer science. Nevertheless, the book is intended for a wider audience ranging from graduate students with proper
background in mathematics and/or informatics.
