Ivan Bratko

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Ivan Bratko [1]

Ivan Bratko,
a Slovenian computer scientist and researcher in artificial intelligence and computer chess, professor at the Faculty of Computer and Information Science University of Ljubljana. Until 2002, professor Bratko also directed the AI group at Jožef Stefan Institute in Ljubljana. In 1982, as a visiting scientist at the University of Edinburgh, Ivan Bratko and Danny Kopec designed the Bratko-Kopec Test [2] [3]

Quotes

Research Interests

Quote from Ivan Bratko's Homepage [4]:

Professor Bratko has conducted research in machine learning, knowledge-based systems, qualitative modeling, intelligent robotics, heuristic programming and computer chess. His main interests in machine learning have been in learning from noisy data, combining learning and qualitative reasoning, constructive induction, Inductive Logic Programming and various applications of machine learning, including medicine and control of dynamic systems. 

Chess Endgames

Quote by Maarten van Emden in I remember Donald Michie [5]:

In 1980 I spent another summer in Edinburgh as a guest of Donald Michie. Since the low point of 1975, thanks to assiduous and inventive joint pursuit of funding possibilities by Donald and Jean, the Machine Intelligence Research Unit was alive with work focused on chess endgames. There were students, including Tim Niblett and Alen Shapiro. Danny Kopec was there, perhaps formally as a student, but de facto as the resident chess consultant. Ivan Bratko visited frequently. Alen was the administrator of the dream computing environment of that time: a small PDP-11 running Unix. 

CLESS

In 1979/80, as visiting researcher at University of Edinburgh, Ivan Bratko worked with Zdenek Zdrahal and Alen Shapiro on Pattern Recognition applied to Chess. In fact they used Bitboards, called cellular 8x8 arrays, to implement their Cellular logic processing emulator for chess (CLESS) [6] . CLESS used three kinds of instructions to recognize simple and more complex chess patterns:

  1. bitwise boolean operations without any interactions between squares
  2. shifts as expand instructions
  3. fill-like propagation instructions, internally using the first two kinds of instructions and conditions in loops

Selected Publications

[7] [8]

1978 ...

1980 ...

1990 ...

Miroslav Kubat, Ivan Bratko, Ryszard Michalski (1998). A Review of Machine Learning Methods. pdf

2000 ...

2005 ...

2010 ...

2015 ...

External Links

References

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