The formation of disulphide bridges among cysteines is an important feature ofprotein structures. Here we develop new methods for the prediction ofdisulphide bond connectivity. We first build a large curated data set of proteins containing disulphide bridges and then use 2-Dimensional Recursive Neural Networks to predict bonding probabilities between cysteine pairs.These probabilities in turn lead to a weighted graph matching problem that can be addressed efficiently. We show how the method consistently achievesbetter results than previous approaches on the same validation data. In addition, the method can easily cope with chains with arbitrary numbers of bonded cysteines. Therefore, it overcomes one of the major limitations of previous approaches restricting predictions to chains containing no more than 10 oxidized cysteines. The method can be applied both to situations where the bonded state of each cysteine is known or unknown, in which case bonded state can be predicted with 85% precision and 90% recall. The method also yields an estimate for the total number of disulphide bridges in each chain.

The AAAI-05 workshops were held on Saturday and Sunday, July 9-10, in Pittsburgh, Pennsylvania. The thirteen workshops were Contexts and Ontologies: Theory, Practice and Applications, Educational Data Mining, Exploring Planning and Scheduling for Web Services, Grid and Autonomic Computing, Human Comprehensible Machine Learning, Inference for Textual Question Answering, Integrating Planning into Scheduling, Learning in Computer Vision, Link Analysis, Mobile Robot Workshop, Modular Construction of Humanlike Intelligence, Multiagent Learning, Question Answering in Restricted Domains, and Spoken Language Understanding.

In 1983, I gave the AAAI president's address titled "Artificial Intelligence Prepares for 2001." An article, based on that talk, was published soon after in "AI Magazine. In this article, I retract or modify some of the points made in that piece and reaffirm others. Specifically, I now acknowledge the many important facets of AI research beyond high-level reasoning but maintain my view about the importance of integrated AI systems, such as mobile robots.

This article provides a historical background on how AAAI came into existence. It provides a rationale for why we needed our own society. This article provides a brief description of the considerations that went into making the final choices. It also provides a description of the historic first AAAI conference and the people that made it happen.

Alonzo Church and Alan Turing The items in this collage were selected September 26, 2005 (www.latimes.com). But there is a realm beyond the exhibit chiefly covers the 50 years of Grand Challenge played out, with 195 the classical computer: the quantum. The efforts to teach a machine to play a teams entering the competition, five probabilistic nature of quantum theory allows quintessentially human pastime culminating teams successfully completing the course atoms and other quantum objects to in the Deep Blue-Kasparov match." The New York and even highschool students but can also be 0 and 1 at the same Times. What are the Limits of Learning .com). "The Stanford scientists who led the vehicles.

The AAAI-06 workshops will be held rather than a regular AAAI paper.

For more information on the BS in CS program, see Philip Flora, 1981 Rina Dechter, 2002 http://www.csd.cs.cmu.edu/education/bscs/index.html.

I systems, the importance of controlling the had previously worked in cybernetics, data acquisition, and introduced an new control theory, and pattern recognition, paradigm: active perception. We stated that where we modeled intelligence, perception, we not just see but we also look, and we and action as signal processing.

The AAAI-05 workshops were held on Saturday and Sunday, July 9-10, in Pittsburgh, Pennsylvania. The thirteen workshops were Contexts and Ontologies: Theory, Practice and Applications, Educational Data Mining, Exploring Planning and Scheduling for Web Services, Grid and Autonomic Computing, Human Comprehensible Machine Learning, Inference for Textual Question Answering, Integrating Planning into Scheduling, Learning in Computer Vision, Link Analysis, Mobile Robot Workshop, Modular Construction of Humanlike Intelligence, Multiagent Learning, Question Answering in Restricted Domains, and Spoken Language Understanding.

The emerging Semantic Web focuses on bringing knowledge representationlike capabilities to Web applications in a Web-friendly way. The ability to put knowledge on the Web, share it, and reuse it through standard Web mechanisms provides new and interesting challenges to artificial intelligence. In this paper, I explore the similarities and differences between the Semantic Web and traditional AI knowledge representation systems, and see if I can validate the analogy "The Semantic Web is to KR as the Web is to hypertext."