The Montreal 2007 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2007) provides an international forum that brings together those actively involved in areas of interest to the IEEE Systems, Man, and Cybernetics Society, to report on up-to-the-minute innovations and developments, to summarize the state of-the-art, and to exchange ideas and advances in all aspects of systems engineering, human machine interface, and emerging cybernetics.

Bio-Informatics Biomedical Cybernetics Bio-systems Computational Life Sciences Conflict Resolution Control of Uncertain Systems Cooperative Systems and Control Data Mining & Management Decision Support Systems Dependable and Autonomous Systems Discrete Event Systems Distributed Intelligent Systems Expert and Knowledge Based Systems Fault Diagnosis Fuzzy Logic Systems Homeland Security Cryptology Human Machine Interaction and Systems Human-Computer Interface Hybrid Systems

Industrial Applications Intelligent Communications Systems Intelligent Devices/Consumer Electronics Intelligent Information Systems Intelligent Systems and Soft Computing Intelligent Transportation Internet/Electronic Commerce Knowledge Acquisition in Intelligent Systems Large-Scale Systems Machine Learning Manufacturing Systems Multi-agent Systems & Distributed AI Multi-player gaming Multimedia Systems Petri Nets Policy Analysis Quality/Reliability & Systems Engineering Risk Management Robotic Systems

 

Machine learning is an area of particular interest as it permeates most of the subjects in the above list. Applications have a wide range encompassing topics from information filtering methodologies that discover user intent and preferences in online systems to data-mining multi-user gaming software large historical data sets. Sessions include learning sets of rules dynamically, analytical learning and evolutionary and population-based methods applied to the game of blackjack. Social signal processing is a related session where methodologies dealing with interpretation of social interralations are put forward. This cutting-edge research addresses the rising concensus that modern computers will need some form of social intelligence in order to be more efficient, with applications in gaming, politics and psychology.

In fact it is believed that the human brain itself follows machine learning principles in order to learn about life. Likewise animals sophisticated like higher mammals or simple ones like viruses thinner than an hair all follow a machine learning methodology to test, decipher and learn about their surroundings. It is not by solving the complex equations of mechanics that a child learns how to perform efficient locomotion of his own body, but but a trial and error process akin to machine learning. In this sense machine learning is a more potent method of learning than any other known computerized technique.