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PROGRESSIVE CHESS
The program for playing Progressive Chess. In Progressive Chess, rather than just making one move per turn, players play progressively longer series of moves.We are designing a program for playing this game. This is just a very short demo. In the following diagram, White to move checkmates in 7 moves. USING HEURISTIC-SEARCH BASED ENGINES FOR ESTIMATING HUMAN 72 ICGA Journal June 2011 Program Evaluation CHESSMASTER 10 0.15 CRAFTY 19.19 0.20 CRAFTY 20.14 0.08 DEEP SHREDDER 10 -0.35 DEEP SHREDDER 11 0.00 FRITZ 6 -0.19 FRITZ 11 0.07 RYBKA 2.2n2 -0.01 RYBKA 3 -0.26 ZAPPA 1.1 0.13 Figure 1: Lasker-Capablanca, St. Petersburg 1914, position after White’s 12th move. The table on the right DETECTING FORTRESSES IN CHESS DETECTING FORTRESSES IN CHESS 37 Figure 2. GM Dvoretsky: “This is an elementary fortress. White cannot overcome the barrier. If the blackking returns to
LEARNING GOAL-ORIENTED STRATEGIES IN PROBLEM SOLVING Learning goal-oriented strategies in problem solving Martin Mo zina, Timotej Lazar, Ivan Bratko Faculty of Computer and Information Science University of Ljubljana, Ljubljana, Slovenia FIGHTING KNOWLEDGE ACQUISITION BOTTLENECK WITH ARGUMENT Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning Martin Mozinaˇ and Matej Guid and Jana Krivec and Aleksander Sadikov and Ivan Bratko 1 Abstract. DERIVING CONCEPTS AND STRATEGIES FROM CHESS TABLEBASES 5 Fig.4. In the position on the left, white pieces lure the defending king out of the wrong corner: 1.Ne5-f7+ Kh8-g8 2.Bf5-g6 Kf8 (note that this is the only available square, since THEORETICAL COMPUTER SCIENCE V. Janko, M. Guid / Theoretical Computer Science 644 (2016) 76–91 77 From a game-theoretic perspective, Progressive chess shares many properties with chess. 1 THE BISHOP AND KNIGHT CHECKMATE 1 The Bishop and Knight Checkmate The bishop and knight checkmate in chess is the checkmate of a lone king which can be forced by a bishop,knight, and king.
GOAL-ORIENTED CONCEPTUALIZATION OF PROCEDURAL KNOWLEDGE 3 Algorithm 1 Pseudo code of the goal-oriented rule learning method. GOAL-ORIENTED RULE LEARNING (examples ES, depth) let allRules be an empty list while ES is not empty do let seedExample be FindBestSeed(ES, ruleList) let goals be DiscoverGoals(ES, seedExample, ruleList, depth) if goals is empty then remove seedExample from ES and return to the beginning of while sentence MATEJ GUID - RESEARCH PAGE - AILAB.SI Matej Guid Asst. Professor. University of Ljubljana Faculty of computer and information science Vecna pot 113, SI-1000 LjubljanaSlovenia
PROGRESSIVE CHESS
The program for playing Progressive Chess. In Progressive Chess, rather than just making one move per turn, players play progressively longer series of moves.We are designing a program for playing this game. This is just a very short demo. In the following diagram, White to move checkmates in 7 moves. USING HEURISTIC-SEARCH BASED ENGINES FOR ESTIMATING HUMAN 72 ICGA Journal June 2011 Program Evaluation CHESSMASTER 10 0.15 CRAFTY 19.19 0.20 CRAFTY 20.14 0.08 DEEP SHREDDER 10 -0.35 DEEP SHREDDER 11 0.00 FRITZ 6 -0.19 FRITZ 11 0.07 RYBKA 2.2n2 -0.01 RYBKA 3 -0.26 ZAPPA 1.1 0.13 Figure 1: Lasker-Capablanca, St. Petersburg 1914, position after White’s 12th move. The table on the right DETECTING FORTRESSES IN CHESS DETECTING FORTRESSES IN CHESS 37 Figure 2. GM Dvoretsky: “This is an elementary fortress. White cannot overcome the barrier. If the blackking returns to
LEARNING GOAL-ORIENTED STRATEGIES IN PROBLEM SOLVING Learning goal-oriented strategies in problem solving Martin Mo zina, Timotej Lazar, Ivan Bratko Faculty of Computer and Information Science University of Ljubljana, Ljubljana, Slovenia FIGHTING KNOWLEDGE ACQUISITION BOTTLENECK WITH ARGUMENT Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning Martin Mozinaˇ and Matej Guid and Jana Krivec and Aleksander Sadikov and Ivan Bratko 1 Abstract. DERIVING CONCEPTS AND STRATEGIES FROM CHESS TABLEBASES 5 Fig.4. In the position on the left, white pieces lure the defending king out of the wrong corner: 1.Ne5-f7+ Kh8-g8 2.Bf5-g6 Kf8 (note that this is the only available square, since THEORETICAL COMPUTER SCIENCE V. Janko, M. Guid / Theoretical Computer Science 644 (2016) 76–91 77 From a game-theoretic perspective, Progressive chess shares many properties with chess. 1 THE BISHOP AND KNIGHT CHECKMATE 1 The Bishop and Knight Checkmate The bishop and knight checkmate in chess is the checkmate of a lone king which can be forced by a bishop,knight, and king.
GOAL-ORIENTED CONCEPTUALIZATION OF PROCEDURAL KNOWLEDGE 3 Algorithm 1 Pseudo code of the goal-oriented rule learning method. GOAL-ORIENTED RULE LEARNING (examples ES, depth) let allRules be an empty list while ES is not empty do let seedExample be FindBestSeed(ES, ruleList) let goals be DiscoverGoals(ES, seedExample, ruleList, depth) if goals is empty then remove seedExample from ES and return to the beginning of while sentencePROGRESSIVE CHESS
The program for playing Progressive Chess. In Progressive Chess, rather than just making one move per turn, players play progressively longer series of moves.We are designing a program for playing this game. This is just a very short demo. In the following diagram, White to move checkmates in 7 moves.PROGRESSIVE CHESS
The program for playing Progressive Chess. In Progressive Chess, rather than just making one move per turn, players play progressively longer series of moves.We are designing a program for playing this game. This is just a very short demo. In the following diagram, White to move checkmates in 7 moves. A QUALITATIVE MODEL OF THE SALMON LIFE CYCLE IN THE A qualitative model of the salmon life cycle in the context of river rehabilitation R. A. A. Noble a, *, B. Bredeweg b, F. Linnebank b, P. Salles c & I. G. Cowx a a The University of Hull International Fisheries Institute, Hull, HU6 7RX UK (r.a.noble@hull.ac.uk and i.g.cowx@hull.ac.uk) b Human Computer Studies, University of Amsterdam, Kruislaan 419 (matrix I), 1098 VA Amsterdam, TheNetherlands
THEORETICAL COMPUTER SCIENCE V. Janko, M. Guid / Theoretical Computer Science 644 (2016) 76–91 77 From a game-theoretic perspective, Progressive chess shares many properties with chess. GOAL-ORIENTED CONCEPTUALIZATION OF PROCEDURAL KNOWLEDGE 3 Algorithm 1 Pseudo code of the goal-oriented rule learning method. GOAL-ORIENTED RULE LEARNING (examples ES, depth) let allRules be an empty list while ES is not empty do let seedExample be FindBestSeed(ES, ruleList) let goals be DiscoverGoals(ES, seedExample, ruleList, depth) if goals is empty then remove seedExample from ES and return to the beginning of while sentence BUILDING AN INTELLIGENT TUTORING SYSTEM FOR CHESS ENDGAMES Building an Intelligent Tutoring System for Chess Endgames Matej Guid, Martin Mozina, Ciril Bohak, Aleksander Sadikov, and Ivan Bratkoˇ Faculty of Computer and Information Science, University of Ljubljana, Trˇza ska cesta 25, Ljubljana, Sloveniaˇ ABML KNOWLEDGE REFINEMENT LOOP: A CASE STUDY ABML Knowledge Refinement Loop: A Case Study Matej Guid1, Martin Moˇzina 1, Vida Groznik1, Dejan Georgiev2, Aleksander Sadikov1, Zvezdan Pirtoˇsek 2, and Ivan Bratko1 1 Faculty of Computer and Information Science, University of Ljubljana, Slovenia 2 Department of Neurology, University Medical Centre Ljubljana, Slovenia Abstract. Argument Based Machine Learning (ABML) was recently demon- HOW TRUSTWORTHY IS CRAFTY’S ANALYSIS OF WORLD CHESS CHAMPIONS? How Trustworthy is CRAFTY’s Analysis of World Chess Champions? 133 from computer-preferred moves) is as a criterion for comparing chess players’ ability in general. Therefore any possible interpretations of the results and rankings that appear in this paper should be made carefully keeping this LEARNING TO EXPLAIN WITH ABML Learning to Explain with ABML Martin Moˇzina, Matej Guid, Jana Krivec, Aleksander Sadikov, and Ivan Bratko Faculty of Computer and Information Science, University of Ljubljana, Slovenia, APPLICATION OF QUALITATIVE REASONING MODELS IN THE Application of qualitative reasoning models in the scientific education of deaf students Paulo Sallesa, Gisele M. Feltrinib, Isabella G. de Sáa, Mônica M.P. Resendeb and Heloisa Lima-Sallesc a Institute of Biological Sciences (psalles@unb.br; isabellagontijo@gmail.com) b Graduate program in Science Teaching (gisele_morisson@yahoo.com.br; monicamresende@terra.com.br) MATEJ GUID - RESEARCH PAGE - AILAB.SI Matej Guid Asst. Professor. University of Ljubljana Faculty of computer and information science Vecna pot 113, SI-1000 LjubljanaSlovenia
PROGRESSIVE CHESS
The program for playing Progressive Chess. In Progressive Chess, rather than just making one move per turn, players play progressively longer series of moves.We are designing a program for playing this game. This is just a very short demo. In the following diagram, White to move checkmates in 7 moves.PROGRESSIVE CHESS
The program for playing Progressive Chess. In Progressive Chess, rather than just making one move per turn, players play progressively longer series of moves.We are designing a program for playing this game. This is just a very short demo. In the following diagram, White to move checkmates in 7 moves. USING HEURISTIC-SEARCH BASED ENGINES FOR ESTIMATING HUMAN 72 ICGA Journal June 2011 Program Evaluation CHESSMASTER 10 0.15 CRAFTY 19.19 0.20 CRAFTY 20.14 0.08 DEEP SHREDDER 10 -0.35 DEEP SHREDDER 11 0.00 FRITZ 6 -0.19 FRITZ 11 0.07 RYBKA 2.2n2 -0.01 RYBKA 3 -0.26 ZAPPA 1.1 0.13 Figure 1: Lasker-Capablanca, St. Petersburg 1914, position after White’s 12th move. The table on the right DETECTING FORTRESSES IN CHESS DETECTING FORTRESSES IN CHESS 37 Figure 2. GM Dvoretsky: “This is an elementary fortress. White cannot overcome the barrier. If the blackking returns to
LEARNING GOAL-ORIENTED STRATEGIES IN PROBLEM SOLVING Learning goal-oriented strategies in problem solving Martin Mo zina, Timotej Lazar, Ivan Bratko Faculty of Computer and Information Science University of Ljubljana, Ljubljana, Slovenia FIGHTING KNOWLEDGE ACQUISITION BOTTLENECK WITH ARGUMENT Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning Martin Mozinaˇ and Matej Guid and Jana Krivec and Aleksander Sadikov and Ivan Bratko 1 Abstract. 1 THE BISHOP AND KNIGHT CHECKMATE 1 The Bishop and Knight Checkmate The bishop and knight checkmate in chess is the checkmate of a lone king which can be forced by a bishop,knight, and king.
HOW TRUSTWORTHY IS CRAFTY’S ANALYSIS OF WORLD CHESS … How Trustworthy is CRAFTY’s Analysis of World Chess Champions? 133 from computer-preferred moves) is as a criterion for comparing chess players’ ability in general. Therefore any possible interpretations of the results and rankings that appear in this paper should be made carefully keeping this GDV IMAGES: CURRENT RESEARCH AND RESULTS GDV images: Current research and results Igor Kononenko, Tatjana Zrimec, Aleksander Sadikov, Danijel Skoˇcaj University of Ljubljana, Faculty of Computer and Information Science MATEJ GUID - RESEARCH PAGE - AILAB.SI Matej Guid Asst. Professor. University of Ljubljana Faculty of computer and information science Vecna pot 113, SI-1000 LjubljanaSlovenia
PROGRESSIVE CHESS
The program for playing Progressive Chess. In Progressive Chess, rather than just making one move per turn, players play progressively longer series of moves.We are designing a program for playing this game. This is just a very short demo. In the following diagram, White to move checkmates in 7 moves.PROGRESSIVE CHESS
The program for playing Progressive Chess. In Progressive Chess, rather than just making one move per turn, players play progressively longer series of moves.We are designing a program for playing this game. This is just a very short demo. In the following diagram, White to move checkmates in 7 moves. USING HEURISTIC-SEARCH BASED ENGINES FOR ESTIMATING HUMAN 72 ICGA Journal June 2011 Program Evaluation CHESSMASTER 10 0.15 CRAFTY 19.19 0.20 CRAFTY 20.14 0.08 DEEP SHREDDER 10 -0.35 DEEP SHREDDER 11 0.00 FRITZ 6 -0.19 FRITZ 11 0.07 RYBKA 2.2n2 -0.01 RYBKA 3 -0.26 ZAPPA 1.1 0.13 Figure 1: Lasker-Capablanca, St. Petersburg 1914, position after White’s 12th move. The table on the right DETECTING FORTRESSES IN CHESS DETECTING FORTRESSES IN CHESS 37 Figure 2. GM Dvoretsky: “This is an elementary fortress. White cannot overcome the barrier. If the blackking returns to
LEARNING GOAL-ORIENTED STRATEGIES IN PROBLEM SOLVING Learning goal-oriented strategies in problem solving Martin Mo zina, Timotej Lazar, Ivan Bratko Faculty of Computer and Information Science University of Ljubljana, Ljubljana, Slovenia FIGHTING KNOWLEDGE ACQUISITION BOTTLENECK WITH ARGUMENT Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning Martin Mozinaˇ and Matej Guid and Jana Krivec and Aleksander Sadikov and Ivan Bratko 1 Abstract. 1 THE BISHOP AND KNIGHT CHECKMATE 1 The Bishop and Knight Checkmate The bishop and knight checkmate in chess is the checkmate of a lone king which can be forced by a bishop,knight, and king.
HOW TRUSTWORTHY IS CRAFTY’S ANALYSIS OF WORLD CHESS … How Trustworthy is CRAFTY’s Analysis of World Chess Champions? 133 from computer-preferred moves) is as a criterion for comparing chess players’ ability in general. Therefore any possible interpretations of the results and rankings that appear in this paper should be made carefully keeping this GDV IMAGES: CURRENT RESEARCH AND RESULTS GDV images: Current research and results Igor Kononenko, Tatjana Zrimec, Aleksander Sadikov, Danijel Skoˇcaj University of Ljubljana, Faculty of Computer and Information Science FIGHTING KNOWLEDGE ACQUISITION BOTTLENECK WITH ARGUMENT Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning Martin Mozinaˇ and Matej Guid and Jana Krivec and Aleksander Sadikov and Ivan Bratko 1 Abstract. DERIVING CONCEPTS AND STRATEGIES FROM CHESS TABLEBASES 5 Fig.4. In the position on the left, white pieces lure the defending king out of the wrong corner: 1.Ne5-f7+ Kh8-g8 2.Bf5-g6 Kf8 (note that this is the only available square, since A QUALITATIVE MODEL OF THE SALMON LIFE CYCLE IN THE A qualitative model of the salmon life cycle in the context of river rehabilitation R. A. A. Noble a, *, B. Bredeweg b, F. Linnebank b, P. Salles c & I. G. Cowx a a The University of Hull International Fisheries Institute, Hull, HU6 7RX UK (r.a.noble@hull.ac.uk and i.g.cowx@hull.ac.uk) b Human Computer Studies, University of Amsterdam, Kruislaan 419 (matrix I), 1098 VA Amsterdam, TheNetherlands
GOAL-ORIENTED CONCEPTUALIZATION OF PROCEDURAL KNOWLEDGE 3 Algorithm 1 Pseudo code of the goal-oriented rule learning method. GOAL-ORIENTED RULE LEARNING (examples ES, depth) let allRules be an empty list while ES is not empty do let seedExample be FindBestSeed(ES, ruleList) let goals be DiscoverGoals(ES, seedExample, ruleList, depth) if goals is empty then remove seedExample from ES and return to the beginning of while sentence HOW TRUSTWORTHY IS CRAFTY’S ANALYSIS OF WORLD CHESS CHAMPIONS? How Trustworthy is CRAFTY’s Analysis of World Chess Champions? 133 from computer-preferred moves) is as a criterion for comparing chess players’ ability in general. Therefore any possible interpretations of the results and rankings that appear in this paper should be made carefully keeping this IMAGE CATEGORIZATION USING LOCAL PROBABILISTIC DESCRIPTORS Image Categorization Using Local Probabilistic Descriptors Extended version of the paper published in Proceedings of the 18th International Conf. on Pattern Recognition, ICPR 2006 HOW CAN HUMANS LEARN FROM COMPUTERS? 218 ICGA Journal December 2010 he studies factors that influence the behavior of diminished returns with increased search effort. His ‘go-deep’ experiments on more than 40,000 positions comprise the most extensive research into this phenomenon to date. DEVELOPMENT OF A PROGRAM FOR PLAYING PROGRESSIVE CHESS Development of a Program for Playing Progressive Chess Vito Janko1 and Matej Guid2 1 Jo zef Stefan Institute, Ljubljana, Slovenia 2 Faculty of Computer and Information Science, University of Ljubljana, Slovenia Abstract. We present the design of a computer program for playingProgressive Chess.
AUTOMATED CHESS TUTOR Automated Chess Tutor 3 a great deal of experience in this respect. It has a good selection of (1) when to comment, (2) which subvariations to give (not easy problems at all) and (3)NASLOVNA STRAN
A Curious Phenomenon. RYBKA 2.1c 32-bit (one of the strongest engines in 2006) assigned the same heuristic. evaluations to all winningpositions in this
MATEJ GUID - RESEARCH PAGE - AILAB.SI Matej Guid Asst. Professor. University of Ljubljana Faculty of computer and information science Vecna pot 113, SI-1000 LjubljanaSlovenia
PROGRESSIVE CHESS
The program for playing Progressive Chess. In Progressive Chess, rather than just making one move per turn, players play progressively longer series of moves.We are designing a program for playing this game. This is just a very short demo. In the following diagram, White to move checkmates in 7 moves.PROGRESSIVE CHESS
The program for playing Progressive Chess. In Progressive Chess, rather than just making one move per turn, players play progressively longer series of moves.We are designing a program for playing this game. This is just a very short demo. In the following diagram, White to move checkmates in 7 moves. USING HEURISTIC-SEARCH BASED ENGINES FOR ESTIMATING HUMAN 72 ICGA Journal June 2011 Program Evaluation CHESSMASTER 10 0.15 CRAFTY 19.19 0.20 CRAFTY 20.14 0.08 DEEP SHREDDER 10 -0.35 DEEP SHREDDER 11 0.00 FRITZ 6 -0.19 FRITZ 11 0.07 RYBKA 2.2n2 -0.01 RYBKA 3 -0.26 ZAPPA 1.1 0.13 Figure 1: Lasker-Capablanca, St. Petersburg 1914, position after White’s 12th move. The table on the right DETECTING FORTRESSES IN CHESS DETECTING FORTRESSES IN CHESS 37 Figure 2. GM Dvoretsky: “This is an elementary fortress. White cannot overcome the barrier. If the blackking returns to
LEARNING GOAL-ORIENTED STRATEGIES IN PROBLEM SOLVING Learning goal-oriented strategies in problem solving Martin Mo zina, Timotej Lazar, Ivan Bratko Faculty of Computer and Information Science University of Ljubljana, Ljubljana, Slovenia FIGHTING KNOWLEDGE ACQUISITION BOTTLENECK WITH ARGUMENT Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning Martin Mozinaˇ and Matej Guid and Jana Krivec and Aleksander Sadikov and Ivan Bratko 1 Abstract. 1 THE BISHOP AND KNIGHT CHECKMATE 1 The Bishop and Knight Checkmate The bishop and knight checkmate in chess is the checkmate of a lone king which can be forced by a bishop,knight, and king.
HOW TRUSTWORTHY IS CRAFTY’S ANALYSIS OF WORLD CHESS … How Trustworthy is CRAFTY’s Analysis of World Chess Champions? 133 from computer-preferred moves) is as a criterion for comparing chess players’ ability in general. Therefore any possible interpretations of the results and rankings that appear in this paper should be made carefully keeping this GDV IMAGES: CURRENT RESEARCH AND RESULTS GDV images: Current research and results Igor Kononenko, Tatjana Zrimec, Aleksander Sadikov, Danijel Skoˇcaj University of Ljubljana, Faculty of Computer and Information Science MATEJ GUID - RESEARCH PAGE - AILAB.SI Matej Guid Asst. Professor. University of Ljubljana Faculty of computer and information science Vecna pot 113, SI-1000 LjubljanaSlovenia
PROGRESSIVE CHESS
The program for playing Progressive Chess. In Progressive Chess, rather than just making one move per turn, players play progressively longer series of moves.We are designing a program for playing this game. This is just a very short demo. In the following diagram, White to move checkmates in 7 moves.PROGRESSIVE CHESS
The program for playing Progressive Chess. In Progressive Chess, rather than just making one move per turn, players play progressively longer series of moves.We are designing a program for playing this game. This is just a very short demo. In the following diagram, White to move checkmates in 7 moves. USING HEURISTIC-SEARCH BASED ENGINES FOR ESTIMATING HUMAN 72 ICGA Journal June 2011 Program Evaluation CHESSMASTER 10 0.15 CRAFTY 19.19 0.20 CRAFTY 20.14 0.08 DEEP SHREDDER 10 -0.35 DEEP SHREDDER 11 0.00 FRITZ 6 -0.19 FRITZ 11 0.07 RYBKA 2.2n2 -0.01 RYBKA 3 -0.26 ZAPPA 1.1 0.13 Figure 1: Lasker-Capablanca, St. Petersburg 1914, position after White’s 12th move. The table on the right DETECTING FORTRESSES IN CHESS DETECTING FORTRESSES IN CHESS 37 Figure 2. GM Dvoretsky: “This is an elementary fortress. White cannot overcome the barrier. If the blackking returns to
LEARNING GOAL-ORIENTED STRATEGIES IN PROBLEM SOLVING Learning goal-oriented strategies in problem solving Martin Mo zina, Timotej Lazar, Ivan Bratko Faculty of Computer and Information Science University of Ljubljana, Ljubljana, Slovenia FIGHTING KNOWLEDGE ACQUISITION BOTTLENECK WITH ARGUMENT Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning Martin Mozinaˇ and Matej Guid and Jana Krivec and Aleksander Sadikov and Ivan Bratko 1 Abstract. 1 THE BISHOP AND KNIGHT CHECKMATE 1 The Bishop and Knight Checkmate The bishop and knight checkmate in chess is the checkmate of a lone king which can be forced by a bishop,knight, and king.
HOW TRUSTWORTHY IS CRAFTY’S ANALYSIS OF WORLD CHESS … How Trustworthy is CRAFTY’s Analysis of World Chess Champions? 133 from computer-preferred moves) is as a criterion for comparing chess players’ ability in general. Therefore any possible interpretations of the results and rankings that appear in this paper should be made carefully keeping this GDV IMAGES: CURRENT RESEARCH AND RESULTS GDV images: Current research and results Igor Kononenko, Tatjana Zrimec, Aleksander Sadikov, Danijel Skoˇcaj University of Ljubljana, Faculty of Computer and Information SciencePADE EXPERIMENTS
Algorithm Pade by Jure Zabkar: Pade is a supervised machine learning algorithm for calculating partial derivatives from point cloud numerical data. It assumes a set of learning examples described by a set of attributes and numerical class variable. Pade calculates the approximations of numerical partial derivatives which can later be transformed into qualitative derivatives.PARKINSONCHECK
ParkinsonCheck 3 the built-in decision support system which looks for tell-tale signs of PD and ET in the drawn spirals. The construction and the workings of the decision COMPUTER ANALYSIS OF WORLD CHESS CHAMPIONS Computer Analysis of World Chess Champions 65 COMPUTER ANALYSIS OF WORLD CHESS CHAMPIONS1 Matej Guid2 and Ivan Bratko2 Ljubljana, Slovenia ABSTRACT Who is HOW TRUSTWORTHY IS CRAFTY’S ANALYSIS OF WORLD CHESS CHAMPIONS? How Trustworthy is CRAFTY’s Analysis of World Chess Champions? 133 from computer-preferred moves) is as a criterion for comparing chess players’ ability in general. Therefore any possible interpretations of the results and rankings that appear in this paper should be made carefully keeping this A QUALITATIVE MODEL OF THE SALMON LIFE CYCLE IN THE A qualitative model of the salmon life cycle in the context of river rehabilitation R. A. A. Noble a, *, B. Bredeweg b, F. Linnebank b, P. Salles c & I. G. Cowx a a The University of Hull International Fisheries Institute, Hull, HU6 7RX UK (r.a.noble@hull.ac.uk and i.g.cowx@hull.ac.uk) b Human Computer Studies, University of Amsterdam, Kruislaan 419 (matrix I), 1098 VA Amsterdam, TheNetherlands
GOAL-ORIENTED CONCEPTUALIZATION OF PROCEDURAL KNOWLEDGE 3 Algorithm 1 Pseudo code of the goal-oriented rule learning method. GOAL-ORIENTED RULE LEARNING (examples ES, depth) let allRules be an empty list while ES is not empty do let seedExample be FindBestSeed(ES, ruleList) let goals be DiscoverGoals(ES, seedExample, ruleList, depth) if goals is empty then remove seedExample from ES and return to the beginning of while sentence LEARNING POSITIONAL FEATURES FOR ANNOTATING CHESS GAMES: A 5 3 Case Study: The Bad Bishop In this case study, we demonstrate the construction of a static positional fea-ture, BAD BISHOP (with possible values yes or no), which was designed for commenting on bad bishops (possibly combined with some heuristic search). IMAGE CATEGORIZATION USING LOCAL PROBABILISTIC DESCRIPTORS Image Categorization Using Local Probabilistic Descriptors Extended version of the paper published in Proceedings of the 18th International Conf. on Pattern Recognition, ICPR 2006 AUTOMATED CHESS TUTOR Automated Chess Tutor 3 a great deal of experience in this respect. It has a good selection of (1) when to comment, (2) which subvariations to give (not easy problems at all) and (3) DEVELOPMENT OF A PROGRAM FOR PLAYING PROGRESSIVE CHESS Development of a Program for Playing Progressive Chess Vito Janko1 and Matej Guid2 1 Jo zef Stefan Institute, Ljubljana, Slovenia 2 Faculty of Computer and Information Science, University of Ljubljana, Slovenia Abstract. We present the design of a computer program for playingProgressive Chess.
MATEJ GUID - RESEARCH PAGE - AILAB.SI Matej Guid Asst. Professor. University of Ljubljana Faculty of computer and information science Vecna pot 113, SI-1000 LjubljanaSlovenia
PROGRESSIVE CHESS
The program for playing Progressive Chess. In Progressive Chess, rather than just making one move per turn, players play progressively longer series of moves.We are designing a program for playing this game. This is just a very short demo. In the following diagram, White to move checkmates in 7 moves.PROGRESSIVE CHESS
The program for playing Progressive Chess. In Progressive Chess, rather than just making one move per turn, players play progressively longer series of moves.We are designing a program for playing this game. This is just a very short demo. In the following diagram, White to move checkmates in 7 moves. USING HEURISTIC-SEARCH BASED ENGINES FOR ESTIMATING HUMAN 72 ICGA Journal June 2011 Program Evaluation CHESSMASTER 10 0.15 CRAFTY 19.19 0.20 CRAFTY 20.14 0.08 DEEP SHREDDER 10 -0.35 DEEP SHREDDER 11 0.00 FRITZ 6 -0.19 FRITZ 11 0.07 RYBKA 2.2n2 -0.01 RYBKA 3 -0.26 ZAPPA 1.1 0.13 Figure 1: Lasker-Capablanca, St. Petersburg 1914, position after White’s 12th move. The table on the right LEARNING GOAL-ORIENTED STRATEGIES IN PROBLEM SOLVING Learning goal-oriented strategies in problem solving Martin Mo zina, Timotej Lazar, Ivan Bratko Faculty of Computer and Information Science University of Ljubljana, Ljubljana, Slovenia DETECTING FORTRESSES IN CHESS DETECTING FORTRESSES IN CHESS 37 Figure 2. GM Dvoretsky: “This is an elementary fortress. White cannot overcome the barrier. If the blackking returns to
FIGHTING KNOWLEDGE ACQUISITION BOTTLENECK WITH ARGUMENT Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning Martin Mozinaˇ and Matej Guid and Jana Krivec and Aleksander Sadikov and Ivan Bratko 1 Abstract. 1 THE BISHOP AND KNIGHT CHECKMATE 1 The Bishop and Knight Checkmate The bishop and knight checkmate in chess is the checkmate of a lone king which can be forced by a bishop,knight, and king.
GOAL-ORIENTED CONCEPTUALIZATION OF PROCEDURAL KNOWLEDGE 3 Algorithm 1 Pseudo code of the goal-oriented rule learning method. GOAL-ORIENTED RULE LEARNING (examples ES, depth) let allRules be an empty list while ES is not empty do let seedExample be FindBestSeed(ES, ruleList) let goals be DiscoverGoals(ES, seedExample, ruleList, depth) if goals is empty then remove seedExample from ES and return to the beginning of while sentence HOW TRUSTWORTHY IS CRAFTY’S ANALYSIS OF WORLD CHESS … How Trustworthy is CRAFTY’s Analysis of World Chess Champions? 133 from computer-preferred moves) is as a criterion for comparing chess players’ ability in general. Therefore any possible interpretations of the results and rankings that appear in this paper should be made carefully keeping this MATEJ GUID - RESEARCH PAGE - AILAB.SI Matej Guid Asst. Professor. University of Ljubljana Faculty of computer and information science Vecna pot 113, SI-1000 LjubljanaSlovenia
PROGRESSIVE CHESS
The program for playing Progressive Chess. In Progressive Chess, rather than just making one move per turn, players play progressively longer series of moves.We are designing a program for playing this game. This is just a very short demo. In the following diagram, White to move checkmates in 7 moves.PROGRESSIVE CHESS
The program for playing Progressive Chess. In Progressive Chess, rather than just making one move per turn, players play progressively longer series of moves.We are designing a program for playing this game. This is just a very short demo. In the following diagram, White to move checkmates in 7 moves. USING HEURISTIC-SEARCH BASED ENGINES FOR ESTIMATING HUMAN 72 ICGA Journal June 2011 Program Evaluation CHESSMASTER 10 0.15 CRAFTY 19.19 0.20 CRAFTY 20.14 0.08 DEEP SHREDDER 10 -0.35 DEEP SHREDDER 11 0.00 FRITZ 6 -0.19 FRITZ 11 0.07 RYBKA 2.2n2 -0.01 RYBKA 3 -0.26 ZAPPA 1.1 0.13 Figure 1: Lasker-Capablanca, St. Petersburg 1914, position after White’s 12th move. The table on the right LEARNING GOAL-ORIENTED STRATEGIES IN PROBLEM SOLVING Learning goal-oriented strategies in problem solving Martin Mo zina, Timotej Lazar, Ivan Bratko Faculty of Computer and Information Science University of Ljubljana, Ljubljana, Slovenia DETECTING FORTRESSES IN CHESS DETECTING FORTRESSES IN CHESS 37 Figure 2. GM Dvoretsky: “This is an elementary fortress. White cannot overcome the barrier. If the blackking returns to
FIGHTING KNOWLEDGE ACQUISITION BOTTLENECK WITH ARGUMENT Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning Martin Mozinaˇ and Matej Guid and Jana Krivec and Aleksander Sadikov and Ivan Bratko 1 Abstract. 1 THE BISHOP AND KNIGHT CHECKMATE 1 The Bishop and Knight Checkmate The bishop and knight checkmate in chess is the checkmate of a lone king which can be forced by a bishop,knight, and king.
GOAL-ORIENTED CONCEPTUALIZATION OF PROCEDURAL KNOWLEDGE 3 Algorithm 1 Pseudo code of the goal-oriented rule learning method. GOAL-ORIENTED RULE LEARNING (examples ES, depth) let allRules be an empty list while ES is not empty do let seedExample be FindBestSeed(ES, ruleList) let goals be DiscoverGoals(ES, seedExample, ruleList, depth) if goals is empty then remove seedExample from ES and return to the beginning of while sentence HOW TRUSTWORTHY IS CRAFTY’S ANALYSIS OF WORLD CHESS … How Trustworthy is CRAFTY’s Analysis of World Chess Champions? 133 from computer-preferred moves) is as a criterion for comparing chess players’ ability in general. Therefore any possible interpretations of the results and rankings that appear in this paper should be made carefully keeping thisPADE EXPERIMENTS
Algorithm Pade by Jure Zabkar: Pade is a supervised machine learning algorithm for calculating partial derivatives from point cloud numerical data. It assumes a set of learning examples described by a set of attributes and numerical class variable. Pade calculates the approximations of numerical partial derivatives which can later be transformed into qualitative derivatives.PARKINSONCHECK
ParkinsonCheck 3 the built-in decision support system which looks for tell-tale signs of PD and ET in the drawn spirals. The construction and the workings of the decision FIGHTING KNOWLEDGE ACQUISITION BOTTLENECK WITH ARGUMENT Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning Martin Mozinaˇ and Matej Guid and Jana Krivec and Aleksander Sadikov and Ivan Bratko 1 Abstract. COMPUTER ANALYSIS OF WORLD CHESS CHAMPIONS Computer Analysis of World Chess Champions 65 COMPUTER ANALYSIS OF WORLD CHESS CHAMPIONS1 Matej Guid2 and Ivan Bratko2 Ljubljana, Slovenia ABSTRACT Who is HOW TRUSTWORTHY IS CRAFTY’S ANALYSIS OF WORLD CHESS CHAMPIONS? How Trustworthy is CRAFTY’s Analysis of World Chess Champions? 133 from computer-preferred moves) is as a criterion for comparing chess players’ ability in general. Therefore any possible interpretations of the results and rankings that appear in this paper should be made carefully keeping this LEARNING POSITIONAL FEATURES FOR ANNOTATING CHESS GAMES: A 5 3 Case Study: The Bad Bishop In this case study, we demonstrate the construction of a static positional fea-ture, BAD BISHOP (with possible values yes or no), which was designed for commenting on bad bishops (possibly combined with some heuristic search). IMAGE CATEGORIZATION USING LOCAL PROBABILISTIC DESCRIPTORS Image Categorization Using Local Probabilistic Descriptors Extended version of the paper published in Proceedings of the 18th International Conf. on Pattern Recognition, ICPR 2006 GOAL-ORIENTED CONCEPTUALIZATION OF PROCEDURAL KNOWLEDGE 3 Algorithm 1 Pseudo code of the goal-oriented rule learning method. GOAL-ORIENTED RULE LEARNING (examples ES, depth) let allRules be an empty list while ES is not empty do let seedExample be FindBestSeed(ES, ruleList) let goals be DiscoverGoals(ES, seedExample, ruleList, depth) if goals is empty then remove seedExample from ES and return to the beginning of while sentence DEVELOPMENT OF A PROGRAM FOR PLAYING PROGRESSIVE CHESS Development of a Program for Playing Progressive Chess Vito Janko1 and Matej Guid2 1 Jo zef Stefan Institute, Ljubljana, Slovenia 2 Faculty of Computer and Information Science, University of Ljubljana, Slovenia Abstract. We present the design of a computer program for playingProgressive Chess.
AUTOMATED CHESS TUTOR Automated Chess Tutor 3 a great deal of experience in this respect. It has a good selection of (1) when to comment, (2) which subvariations to give (not easy problems at all) and (3) MATEJ GUID - RESEARCH PAGE - AILAB.SI Matej Guid Asst. Professor. University of Ljubljana Faculty of computer and information science Vecna pot 113, SI-1000 LjubljanaSlovenia
PROGRESSIVE CHESS
The program for playing Progressive Chess. In Progressive Chess, rather than just making one move per turn, players play progressively longer series of moves.We are designing a program for playing this game. This is just a very short demo. In the following diagram, White to move checkmates in 7 moves.PROGRESSIVE CHESS
The program for playing Progressive Chess. In Progressive Chess, rather than just making one move per turn, players play progressively longer series of moves.We are designing a program for playing this game. This is just a very short demo. In the following diagram, White to move checkmates in 7 moves. PERSONAL INFORMATION Personal Information Teaching Information Curriculum Vitae Publications Chess Page Invited Lectures at AI lab Send an E-mail : AI Laboratory > People > Aleksander Sadikov USING HEURISTIC-SEARCH BASED ENGINES FOR ESTIMATING HUMAN 72 ICGA Journal June 2011 Program Evaluation CHESSMASTER 10 0.15 CRAFTY 19.19 0.20 CRAFTY 20.14 0.08 DEEP SHREDDER 10 -0.35 DEEP SHREDDER 11 0.00 FRITZ 6 -0.19 FRITZ 11 0.07 RYBKA 2.2n2 -0.01 RYBKA 3 -0.26 ZAPPA 1.1 0.13 Figure 1: Lasker-Capablanca, St. Petersburg 1914, position after White’s 12th move. The table on the right LEARNING GOAL-ORIENTED STRATEGIES IN PROBLEM SOLVING Learning goal-oriented strategies in problem solving Martin Mo zina, Timotej Lazar, Ivan Bratko Faculty of Computer and Information Science University of Ljubljana, Ljubljana, Slovenia DETECTING FORTRESSES IN CHESS DETECTING FORTRESSES IN CHESS 37 Figure 2. GM Dvoretsky: “This is an elementary fortress. White cannot overcome the barrier. If the blackking returns to
ARGUMENT BASED MACHINE LEARNING APPLIED TO LAW Argument Based Machine Learning Applied to Law Martin Mo•zina1, Jure •Zabkar 1, Trevor Bench-Capon2, and Ivan Bratko1 1 Faculty of Computer and Information Science, University of Ljubljana, Slovenia fmartin.mozina,jure.zabkar,ivan.bratkog@fri.uni-lj.si 2 Department of Computer Science, The University of Liverpool, Liverpool, UK T.J.M.Bench-Capon@csc.liv.ac.uk FIGHTING KNOWLEDGE ACQUISITION BOTTLENECK WITH ARGUMENT Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning Martin Mozinaˇ and Matej Guid and Jana Krivec and Aleksander Sadikov and Ivan Bratko 1 Abstract. 1 THE BISHOP AND KNIGHT CHECKMATE 1 The Bishop and Knight Checkmate The bishop and knight checkmate in chess is the checkmate of a lone king which can be forced by a bishop,knight, and king.
MATEJ GUID - RESEARCH PAGE - AILAB.SI Matej Guid Asst. Professor. University of Ljubljana Faculty of computer and information science Vecna pot 113, SI-1000 LjubljanaSlovenia
PROGRESSIVE CHESS
The program for playing Progressive Chess. In Progressive Chess, rather than just making one move per turn, players play progressively longer series of moves.We are designing a program for playing this game. This is just a very short demo. In the following diagram, White to move checkmates in 7 moves.PROGRESSIVE CHESS
The program for playing Progressive Chess. In Progressive Chess, rather than just making one move per turn, players play progressively longer series of moves.We are designing a program for playing this game. This is just a very short demo. In the following diagram, White to move checkmates in 7 moves. PERSONAL INFORMATION Personal Information Teaching Information Curriculum Vitae Publications Chess Page Invited Lectures at AI lab Send an E-mail : AI Laboratory > People > Aleksander Sadikov USING HEURISTIC-SEARCH BASED ENGINES FOR ESTIMATING HUMAN 72 ICGA Journal June 2011 Program Evaluation CHESSMASTER 10 0.15 CRAFTY 19.19 0.20 CRAFTY 20.14 0.08 DEEP SHREDDER 10 -0.35 DEEP SHREDDER 11 0.00 FRITZ 6 -0.19 FRITZ 11 0.07 RYBKA 2.2n2 -0.01 RYBKA 3 -0.26 ZAPPA 1.1 0.13 Figure 1: Lasker-Capablanca, St. Petersburg 1914, position after White’s 12th move. The table on the right LEARNING GOAL-ORIENTED STRATEGIES IN PROBLEM SOLVING Learning goal-oriented strategies in problem solving Martin Mo zina, Timotej Lazar, Ivan Bratko Faculty of Computer and Information Science University of Ljubljana, Ljubljana, Slovenia DETECTING FORTRESSES IN CHESS DETECTING FORTRESSES IN CHESS 37 Figure 2. GM Dvoretsky: “This is an elementary fortress. White cannot overcome the barrier. If the blackking returns to
ARGUMENT BASED MACHINE LEARNING APPLIED TO LAW Argument Based Machine Learning Applied to Law Martin Mo•zina1, Jure •Zabkar 1, Trevor Bench-Capon2, and Ivan Bratko1 1 Faculty of Computer and Information Science, University of Ljubljana, Slovenia fmartin.mozina,jure.zabkar,ivan.bratkog@fri.uni-lj.si 2 Department of Computer Science, The University of Liverpool, Liverpool, UK T.J.M.Bench-Capon@csc.liv.ac.uk FIGHTING KNOWLEDGE ACQUISITION BOTTLENECK WITH ARGUMENT Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning Martin Mozinaˇ and Matej Guid and Jana Krivec and Aleksander Sadikov and Ivan Bratko 1 Abstract. 1 THE BISHOP AND KNIGHT CHECKMATE 1 The Bishop and Knight Checkmate The bishop and knight checkmate in chess is the checkmate of a lone king which can be forced by a bishop,knight, and king.
HOMEPAGE OF DORIAN SUC Šuc, D., Bratko, I. Skill modeling through symbolic reconstruction of operator's trajectories. In: Automated systems based on human skill : joint design of technology and organisation: preprints of the 6th IFAC Symposium, 1997. Aachen: University of Technology, Department of Informatics in Mechanical Engineering; Ljubljana: J. Stefan Institute,1997, pp. 35-38.
PERSONAL INFORMATION Personal Information Teaching Information Curriculum Vitae Publications Chess Page Invited Lectures at AI lab Send an E-mail : AI Laboratory > People > Aleksander Sadikov DERIVING CONCEPTS AND STRATEGIES FROM CHESS TABLEBASES 5 Fig.4. In the position on the left, white pieces lure the defending king out of the wrong corner: 1.Ne5-f7+ Kh8-g8 2.Bf5-g6 Kf8 (note that this is the only available square, since FIGHTING KNOWLEDGE ACQUISITION BOTTLENECK WITH ARGUMENT Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning Martin Mozinaˇ and Matej Guid and Jana Krivec and Aleksander Sadikov and Ivan Bratko 1 Abstract. SEARCH-BASED ESTIMATION OF PROBLEM DIFFICULTY FOR HUMANS Search-Based Estimation of Problem Difficulty for Humans 3 d best E1 second E2 DS 2 Nf3-g5 123 Qd1-c2 80 – 3 Nf3-g5 107 Qd1-c2 103 0 4 Nf3-g5 117 Qd1-c2 103 0 BUILDING AN INTELLIGENT TUTORING SYSTEM FOR CHESS ENDGAMES Building an Intelligent Tutoring System for Chess Endgames Matej Guid, Martin Mozina, Ciril Bohak, Aleksander Sadikov, and Ivan Bratkoˇ Faculty of Computer and Information Science, University of Ljubljana, Trˇza ska cesta 25, Ljubljana, Sloveniaˇ GOAL-ORIENTED CONCEPTUALIZATION OF PROCEDURAL KNOWLEDGE 3 Algorithm 1 Pseudo code of the goal-oriented rule learning method. GOAL-ORIENTED RULE LEARNING (examples ES, depth) let allRules be an empty list while ES is not empty do let seedExample be FindBestSeed(ES, ruleList) let goals be DiscoverGoals(ES, seedExample, ruleList, depth) if goals is empty then remove seedExample from ES and return to the beginning of while sentence HOW TRUSTWORTHY IS CRAFTY’S ANALYSIS OF WORLD CHESS CHAMPIONS? How Trustworthy is CRAFTY’s Analysis of World Chess Champions? 133 from computer-preferred moves) is as a criterion for comparing chess players’ ability in general. Therefore any possible interpretations of the results and rankings that appear in this paper should be made carefully keeping this GDV IMAGES: CURRENT RESEARCH AND RESULTS GDV images: Current research and results Igor Kononenko, Tatjana Zrimec, Aleksander Sadikov, Danijel Skoˇcaj University of Ljubljana, Faculty of Computer and Information Science AUTOMATED CHESS TUTOR Automated Chess Tutor 3 a great deal of experience in this respect. It has a good selection of (1) when to comment, (2) which subvariations to give (not easy problems at all) and (3)Youtube
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Home > Research > Laboratories > Artificial Intelligence Laboratory Artificial Intelligence LaboratoryLocation: R3.54
The laboratory carries out research in machine learning (particularly argument based machine learning, inductive logic programming, robot learning), qualitative reasoning with robotics applications, intelligent robotics (planning, learning for planning), machine learning in medicine, and intelligent tutoring systems (ITS for programming and game playing, automated hint generation and the automatic assessment of the level of difficulty of problems forhumans).
* Lab members
* Aleksander Sadikov, Head of Laboratory* Ivan Bratko
* Martin Možina
* Jure Žabkar
* Dejan Georgiev
* Vida Groznik
* Ana Herzog
* Teodora Matić
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* PKP6 - Computer System for Screening for Dyslexia Projects Structural Funds(PKP6), 01.03.2020 - 31.07.2020 * QUIERO - Quantitative MR-based imaging of physical biomarkers (QUIERO), 01.06.2019 - 31.05.2022 * MATIĆ TEODORA - TEODORA MATIĆ - Young Researcher Research projects ARRS(MATIĆ TEODORA), 01.10.2018 - 30.09.2022 * Artificial intelligence and inteligent systems ARRS research programmes01.01.2015 - 31.12.2020Past:
* Izdelava aplikacije za pametne telefone za pomoč pri izvajanju elektrokardiografije 20.09.2019 - 20.09.2019* NEUS - Neus
(NEUS),01.08.2019 - 31.12.2019 * ŠIPK 4 - Razvoj kulturno-informacijske platforme za obveščanje o kulturnih dogodkih in lokacijah kulturne dediščine Projects Structural Funds(ŠIPK 4),15.03.2019 - 14.07.2019 * ŠIPK 4 - Izdelava računalniškega sistema za prepoznavanje disleksije in pomoč dislektikom Projects Structural Funds(ŠIPK 4),01.03.2019 - 30.06.2019 * Pogodba o sodelovanju 09.04.2018 - 31.05.2018 * EkoSmart - EkoSMART – a smartcity ecosystem Projects Structural Funds(EkoSmart),01.08.2016 - 31.07.2019 * Molecular and other prognosticators of lung cancer andmesothelioma
Research projects ARRS01.01.2016 - 31.12.2018* CODE Q - CODE Q
Projects Structural Funds(CODE Q),03.11.2014 - 30.09.2015 * PKP 1 - Kdo ta hip gleda televizijo? Projects Structural Funds(PKP 1),01.04.2014 - 30.09.2014 * Machine learning for building intelligent tutoring systems: conceptualization of problem-solving domains Bilateral projects01.01.2013 - 31.12.2014 * PARKINSCHECK - Pravočasno odkrivanje in spremljanje Parkinsonovebolezni
Projects Structural Funds(PARKINSCHECK),21.08.2012 - 31.08.2013 * Machine learning for building intelligent tutoring systems Research projects ARRS01.07.2011 - 30.06.2014 * Molecular and other prognosticators of lung cancer andmesothelioma
Research projects ARRS01.07.2011 - 30.06.2014 * Towards Multi-Agent Intelligent Systems with Ability of Incremental Learning Bilateral projects01.01.2011 - 31.12.2012 * Qualitative modeling from data Research projects ARRS01.05.2009 - 30.04.2012 * Artificial intelligence and inteligent systems ARRS research programmes01.01.2009 - 31.12.2014 * XPERO - Learning by Experimentation European projects(XPERO),01.04.2006 - 30.09.2009 * X-MEDIA - Large Scale Knowledge Sharing and Reuse Across Media European projects(X-MEDIA),01.03.2006 - 30.09.2009Research
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