Special Sessions at the

        6th Int'l Symposium on Artificial Intelligence and Mathematics
        Jan. 5-7, 2000 in Ft. Lauderdale, Florida
                http://rutcor.rutgers.edu/~amai

We are pleased to announce the following three special sessions to be held
during the 6th Int'l Symposium on Artificial Intelligence and Mathematics,
Jan. 5-7, 2000 in Ft. Lauderdale, Florida.

Mathematical Aspects of Knowledge Discovery and Data Analysi
        Organized by 
                Prof. Ronen Feldman <[EMAIL PROTECTED]>
                Prof. Martin Golumbic <[EMAIL PROTECTED]>
                Prof. Peter Hammer <[EMAIL PROTECTED]>   
                Prof. Alexander Kogan <[EMAIL PROTECTED]>
                
Satisfiability and Theorem Proving
        Organized by 
                Prof. Endre Boros <[EMAIL PROTECTED]>   

Knowledge exploration for predictive toxicity of chemicals
        Organized by 
                Prof. Giuseppina Gini <[EMAIL PROTECTED]>

Further information on the Symposium, registration, hotel, schedule,
web-proceedings, etc. can be found at http://rutcor.rutgers.edu/~amai


Titles of Lectures from the special sessions

        Mathematical Aspects of Knowledge Discovery and Data Analysis

                SPECIAL SESSION AI & Math 2000

Pareto-Optimal Patterns in Logical Analysis of Data
        Peter L. Hammer         <[EMAIL PROTECTED]>   
        Alexander Kogan         <[EMAIL PROTECTED]>
        Bruno Simeone           <[EMAIL PROTECTED]>

Identification of Frequent Sets and  Association Rules
        Jan Cor Bioch <[EMAIL PROTECTED]>

Average Case Performance of the Apriori Algorithm
        Paul Purdom <[EMAIL PROTECTED]>,

Generating all ``good'' patterns in polynomial expected time
        Endre Boros     <[EMAIL PROTECTED]>
        Lijie Shi       RUTCOR, Rutgers University and
        Mutsunori Yagiura        Applied Mathematics and Physics Department,
                        Graduate School of Engineering, Kyoto University

Algorithms for Massive Data Streams
        Martin Strauss <[EMAIL PROTECTED]>

Multiple Randomized Classifiers: Why boosting and randomized forests really work
        Yali Amit       <[EMAIL PROTECTED]>

- -------------------------------------------------------------------

        SPECIAL SESSION ON SATISFIABLILTY AND THEOREM PROVING


A parallel approach to resolution-refutation proofs.
        Monroe Newborn <[EMAIL PROTECTED]>

Practical Heuristics for Solving Problems for Serializable Subgoals
        Mohammed Almulla  <[EMAIL PROTECTED]
        A. El-Sheikh  <[EMAIL PROTECTED]

Qualitative Theorem Proving in Linear Constraints
        Vijay Chandru   <[EMAIL PROTECTED]>
        Catherine Lassez   <[EMAIL PROTECTED]>
        Jean-Louis Lassez  <[EMAIL PROTECTED]>

Some results about the probabilistic SAT problem
        Daniele Pretolani       <[EMAIL PROTECTED]>
        Kim Allan Andersen

Solving some SAT instances by cutting planes
        Ming Ouyang     <[EMAIL PROTECTED]>

Semi-definite relaxations of 2+p-SAT problems ; another phase-transition?
        Hans van Maaren <[EMAIL PROTECTED]>

The mechanics of upper and lower bound arguments related to the probabilistic 
complexity of resolution-based algorithms on random k-SAT formulas
        John Franco     <[EMAIL PROTECTED]>


- ---------------------------------------------------------------------

                Special Session on

        "Knowledge exploration for predictive toxicity of chemicals"


Prediction of ecotoxicity of pesticides: comparison of multivariate
analysis, neural networks, and classifiers.
        Gini, G., Balestri, M., DEI, Politecnico di Milano, Italy
        Benfenati, E., Pelagatti, S., Istituto Mario Negri, Milano, Italy

Knowledge Exploration for Toxicity Prediction by Using Genetic
Optimized B-Spline Networks,
        Adolf Grauel   <[EMAIL PROTECTED]>
        Ingo Renners
        Lars A. Ludwig

Computational Intelligence Methods Aid the Design of Safe Chemicals
        Les M. Sztandera  <[EMAIL PROTECTED]>
        Charles Bock
        Mendel Trachtman

Structure-Activity Relationship Models: Using the Results of Model Ensembles
        Nancy B. Sussman  <[EMAIL PROTECTED]>

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