Computational Logic as the Missing Link between Humans and Computers
Abstract:
Artificial Intelligence (AI) is the attempt to program computers to behave
intelligently, as judged by human standards. Its results, over the past 50
years or so, have disappointed its most enthusiastic supporters, and its
goals have been attacked by its fiercest critics. But despite these upsets,
AI has made important contributions both to our understanding of human
thinking, as studied in the Cognitive Sciences, and to our understanding of
how to structure more powerful computations, as studied in Computer Science.
Among the most useful of these contributions are the notions of
Computational Logic, intelligent agent, and multi-agent systems.
Computational Logic, developed in AI, was the basis of the Japanese Fifth
Generation Project of the 1980s. Arguably, the Fifth Generation Project
failed to achieve its goals because it was too far ahead of its time, and
only recently has it become possible to understand its true potential.
Computational Logic is a practical, simplified form of Symbolic Logic, which
combines the use of logic to represent information with the use of inference
to derive logical consequences and to perform computations. In this
capacity, it is well-suited to the task of regulating the behaviour of an
intelligent agent, interacting with other agents in a dynamically changing
environment. I will argue that this view of Computational Logic in
multi-agent systems makes it both a powerful model of human behaviour and a
suitable candidate for future generation computer systems.
Keynote Speakers 2
Name:
Professor Hiroaki Kikuchi
Department:
Department of Communication and Network Enginnering
Faculty:
School of Information and Telecommunication Engineering
Privacy-Preserving Data Mining - Concepts and Issues
Abstract:
Privacy-Preserving technique allows to compute any useful
collaborative computation without revealing confidential inputs.
The candidate computations include classification, association rule
mining, clustering, decision tree learning, and so on.
For instance, the privacy-preserving recommendation system
enables us to get the recommended value without leaking the private
preference on items to service providers. To preserve the
confidentiality, we have two approaches; the cryptographic
protocols and the randomization technique.
In this talk, after describing fundamental notion of
privacy-preserving data mining (PPDM) for both approaches,
we discuss issues in adopting PPDM to practical applications.
Keynote Speakers 3
Name:
Dr. Simon See
Position and Organization:
1. Director, High Performance Computing, Sun Microsystems Inc. Asia Pacific
2. Associate Professor, Nanyang Technological University
3. Visiting Professor, Shanghai Jiatong University
Trends in Computing for Science, Engineering and Social Science Research
Abstract:
Information Technology has been one of the key tools used in research whether it is Science, engineering, finance, economics or social science. The need for more compute power, storage and other technology have grown expoentially over the years given that researchers have find new ways of using IT to enhance their productivity. With the commodization of microprocessor, storage, memory and others, high performance computing become affordable to many researchers. However this posed a potential problem- Energy.
In this talk, the author give an overview of the trends on how high performance computing is used . He is give an view of where high performance computing is going and the challenges that HPC faces.
NCSEC 2009: Green Computing Technology
Hosted by Computer Engineering Department, Faculty of Engineering, King Mongkut's University of Technology Thonburi (KMUTT).
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