Knowledge Discovery in Databases: PKDD 2003

This paper describes the Robosail project. It started in 1997 with the aim to build a self-learning auto pilot for a single handed sailing yacht. The goal was to make an adaptive system that would help a single handed sailor to go faster on average in a race. Presently, after five years of development and a number of sea trials, we have a commercial system available (www.robosail.com). It is a hybrid system using agent technology, machine learning, data mining and rule-based reasoning. Apart from describing the system we try to generalize our findings, and argue that sailing is an interesting paradigm for a class of hybrid systems that one could call Skill-based Systems.

[1]  W. G. Cochran,et al.  Controlling Bias in Observational Studies: A Review. , 1974 .

[2]  D. Rubin,et al.  Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies , 1978 .

[3]  D. Rubin,et al.  The Bias Due to Incomplete Matching , 1985 .

[4]  D. Rubin,et al.  Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score , 1985 .

[5]  R. Cramer,et al.  Recent advances in comparative molecular field analysis (CoMFA). , 1989, Progress in clinical and biological research.

[6]  Richard A. Lewis,et al.  Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Henry D. Shapiro,et al.  An Exact Characterization of Greedy Structures , 1993, IPCO.

[8]  A. N. Jain,et al.  Compass: predicting biological activities from molecular surface properties. Performance comparisons on a steroid benchmark. , 1994, Journal of medicinal chemistry.

[9]  Jonathan D. Hirst,et al.  Quantitative structure-activity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by triazines , 1994, J. Comput. Aided Mol. Des..

[10]  L. Johnson,et al.  POTENT INHIBITION OF GLYCOGEN PHOSPHORYLASE BY A SPIROHYDANTOIN OF GLUCOPYRANOSE : FIRST PYRANOSE ANALOGUES OF HYDANTOCIDIN , 1995 .

[11]  D. Rubin,et al.  In utero exposure to phenobarbital and intelligence deficits in adult men. , 1995, JAMA.

[12]  M J Sternberg,et al.  Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Thomas Lengauer,et al.  A fast flexible docking method using an incremental construction algorithm. , 1996, Journal of molecular biology.

[14]  G. V. Paolini,et al.  Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes , 1997, J. Comput. Aided Mol. Des..

[15]  M. Pastor,et al.  A strategy for the incorporation of water molecules present in a ligand binding site into a three-dimensional quantitative structure--activity relationship analysis. , 1997, Journal of medicinal chemistry.

[16]  R. Babine,et al.  MOLECULAR RECOGNITION OF PROTEIN-LIGAND COMPLEXES : APPLICATIONS TO DRUG DESIGN , 1997 .

[17]  Samir Khuller,et al.  Greedy strikes back: improved facility location algorithms , 1998, SODA '98.

[18]  Giorgio Gambosi,et al.  Complexity and approximation: combinatorial optimization problems and their approximability properties , 1999 .

[19]  D. Joseph-McCarthy.  Computational approaches to structure-based ligand design. , 1999, Pharmacology & therapeutics.

[20]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[21]  Ingo Muegge,et al.  Evaluation of docking/scoring approaches: A comparative study based on MMP3 inhibitors , 2000, J. Comput. Aided Mol. Des..

[22]  D. Rognan,et al.  Customized versus universal scoring functions: application to class I MHC-peptide binding free energy predictions. , 2001, Bioorganic & medicinal chemistry letters.

[23]  Nada Lavrač,et al.  An Introduction to Inductive Logic Programming , 2001 .

[24]  Thomas Lengauer,et al.  Flexible docking under pharmacophore type constraints , 2002, J. Comput. Aided Mol. Des..

[25]  R. King,et al.  New approach to pharmacophore mapping and QSAR analysis using inductive logic programming. Application to thermolysin inhibitors and glycogen phosphorylase B inhibitors. , 2002, Journal of medicinal chemistry.

[26]  Ruth Nussinov,et al.  Principles of docking: An overview of search algorithms and a guide to scoring functions , 2002, Proteins.

[27]  H. Kubinyi,et al.  3D QSAR in drug design. , 2002 .

[28]  Ashwin Srinivasan,et al.  Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL , 1998, Machine Learning.