A conference as part of
IFIP World Computer Congress (WCC2008)
The Second IFIP International Conference on Artificial Intelligence in Theory and Practice (IFIP AI 2008) is an integral session in the IFIP World Computer Congress (WCC 2008) program. The conference will follow the same format as the highly successful “IFIP AI 2006” at the IFIP WCC 2006 in Santiago, Chile. Authors of accepted papers will present their work followed by open discussion.
Keynote Talks
We are delighted to announce that two eminent scientists have accepted invitations to present keynote talks at AI 2008.
- Professor Nick Kasabov [Auckland University of Technology]
- Integrative Intelligent Information Processing Systems based on Neural-, Genetic-, and Quantum Information Principles
- The talk presents a new direction towards building intelligent information processing systems that integrate neuronal-, genetic-, and quantum information principles. First, the talk reviews the main principles of information processing at neuronal-, genetic-, and quantum information levels. Each of these levels has already inspired the creation of efficient computational AI models, such as: artificial neural networks for learning; evolutionary computation for optimization; gene and protein interaction networks; quantum computation for fast parallel processing and for associative memories. The talk extends these paradigms to integrative information models and systems, so that they integrate in their structure and algorithms principles from different hierarchical levels of information processing in their dynamic interaction. Examples given include: evolving spiking neural networks, applied to adaptive multimodal audio-visual information processing; integrative computational neurogenetic models applied to modeling brain functions; quantum evolutionary algorithms for exponentially faster optimization; quantum neural networks for building exponentially larger associative memories. The new models are significantly faster in feature selection and learning and solve efficiently NP complete biological and engineering problems for adaptive, incremental learning in a large dimensional space. They can also help to better understand complex information processes in Nature and in the brain, especially how information processes at different information levels interact, and to extend our understanding on the fundamental concept of Information, leading to a new Integrative Information Theory. Open questions, challenges and directions for further research are presented.
- Professor Lorenza Saitta [University of Piemonte Orientale]
- Learning Machines and Complex Systems: Two Converging Paths?
- The talk explores a subject at the frontier between Complex Systems analysis and Learning. It will describe emergent properties in Relational Machine Learning (learning from structured data), investigated by means of techniques derived from Statistical Physics. More precisely, the emergence, in learning, of a phase transition (a phenomenon typically occurring in both combinatorial computational problems and complex systems) will be described. Finally, the impact of this phenomenon both on the design of learning machines and on learnability in complex system will be discussed.
All accepted papers will appear in the official IFIP WCC Conference proceedings produced by Springer Science and Business Media. The list of accepted papers is here.
All Congress delegates may attend the Conference as part of their Congress registration. Registration details will appear on the Congress web site in due course.
This conference is sponsored and organised by: