doc. dr. Matej Guid
Asistent
T: +386 1 479 8278
E:Pošljite sporočilo
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Prostor: R3.69 - Kabinet
Opis
Docent Matej Guid je raziskovalec na področju umetne inteligence v Laboratoriju za algoritmiko. Ukvarja se z razvojem metod umetne inteligence na presečišču strojnega učenja, kognitivne znanosti in računalniškega igranja iger. V okviru strojnega učenja razvija metode, ki omogočajo učinkovito interakcijo z domenskim strokovnjakom. Njegovo osrednje raziskovalno področje je preučevanje kognitivnih procesov pri človeškem reševanju problemov s pomočjo algoritmičnih pristopov – zlasti modeliranje težavnosti nalog za človeka, odkrivanje implicitnih kriterijev pri ekspertnem presojanju podobnosti ter razložljivo napovedovanje na osnovi preiskovalnih dreves. Njegovo delo sega tudi na področja konstrukcije značilk z razložljivimi napovedmi, računalniške ustvarjalnosti in računalniškega igranja iger.
Nagrade
  • Slovenski start:up leta 2020 s podjetjem InstaText, 2020.
  • Prešernova nagrada študentom za delo Računalniška analiza svetovnih šahovskih prvakov (pod mentorstvom prof. dr. Ivana Bratka), 2005.
Predmeti
Laboratorij
Projekti
Zaključeni projekti
Publikacije

JOURNAL PAPERS

  • B. Vouk, M. Guid, M. Robnik Šikonja. Feature construction using explanations of individual predictions. Engineering applications of artificial intelligence, 2023, vol. 120, str. 1-27, ilustr. ISSN 0952-1976.
  • P. Backus, M. Cubel, M. Guid, S. Sánchez-Pagés, E. López Manas. Gender, competition, and performance: Evidence from chess players. Quantitative economics., 2023, vol. 14, iss. 1, str. 349-380, graf. prikazi. ISSN 1759-7323.
  • K. Reba, M. Guid, K. Rozman, D. Janežič, J. Konc. Exact maximum clique algorithm for different graph types using machine learning. Mathematics, 2022, vol. 10, iss. 1, str. 1-14, ilustr. ISSN 2227-7390.
  • J. Krivec, I. Bratko, M. Guid. Identification and conceptualization of procedural chunks in chess. Cognitive systems research. vol. 69, pp. 22-40, 2021.
  • J. Krivec, M. Guid. The influence of context on information processing. Cognitive Processing, vol. 21, pp. 1-18, 2020.
  • V. Janko, M. Guid. A Program for Progressive Chess. Theoretical Computer Science (2016), doi:10.1016/j.tcs.2016.06.028.
  • D. Hristova, M. Guid, I. Bratko. Assessing the difficulty of chess tactical problems. International journal on advances in intelligent systems, Vol. 7, No. 3/4, pp. 728-738, 2014.
  • A. Iqbal, H. van der Heijden, M. Guid, A. Makhmali. Evaluating the aesthetics of endgame studies: a computational model of human aesthetic perception. IEEE Transactions on Computational Intelligence and AI in Games, Vol. 4, No. 3, pp. 178-191, 2012.
  • M. Guid, I. Bratko. Detecting Fortresses in Chess. Elektrotehniški vestnik: Journal of Electrical Engineering and Computer Science, Vol. 79, No. 1/2, pp. 35-40, 2012.
  • V. Groznik, M. Guid, A. Sadikov, M. Možina, D. Georgiev, V. Kragelj, S. Ribarič, Z. Pirtošek, and I. Bratko. Elicitation of neurological knowledge with argument-based machine learning. Artificial Intelligence in Medicine, Vol. 57, No. 2, pp. 133–144, 2013.
  • M. Guid, I. Bratko. Using Heuristic-Search Based Engines for Estimating Human Skill at Chess. ICGA Journal, Vol. 34, No. 2, pp. 71-81, 2011.
  • M. Guid, A. Pérez, I. Bratko. How Trustworthy is Crafty's Analysis of World Chess Champions? ICGA Journal, Vol. 31, No. 3, pp. 131-144, 2008.
  • M. Guid, I. Bratko. Factors affecting diminishing returns for searching deeper. ICGA Journal, Vol. 30, No. 2, pp. 75-84, 2007.
  • M. Guid, I. Bratko. Computer analysis of world chess champions. ICGA Journal, Vol. 29, No. 2, pp. 65-73, 2006.

 

BOOKS

  • M. Guid. Learn and Master Progressive Chess. Založba UL FRI, 2017.
  • A. Iqbal, M. Guid, S. Colton, J. Krivec, S. Azman, B. Haghighi. The Digital Synaptic Neural Substrate: A New Approach to Computational Creativity. SpringerBriefs in Cognitive Computation, Springer International Publishing, 2016.

 

BOOK CHAPTERS

  • I. Bratko, D. Hristova, M. Guid. Search Versus Knowledge in Human Problem Solving: A Case Study in Chess. In Model-Based Reasoning in Science and Technology, pp. 569–583. Springer International Publishing, 2016.

 

CONFERENCE PAPERS

  • M. Bizjak, M. Guid. Automatic recognition of similar chess motifs. Advances in Computers and Games (ACG 2021).
  • M. Guid, M. Možina, M. Pavlič, K. Turšič. Learning by Arguing in Argument-Based Machine Learning Framework. The 15th International Conference on Intelligent Tutoring Systems (ITS 2019). Kingston, Jamaica, June 3-7, 2019.
  • M. Guid, M. Pavlič, M. Možina. Automated Feedback Generation for Argument-Based Intelligent Tutoring Systems. The 11th International Conference on Computer Supported Education (CSEDU 2019). Heraklion, Crete, Greece, May 2-4, 2019.
  • M. Guid, I. Bratko. Influence of Search Depth on Position Evaluation. The 15th International Conference on Advances in Computer Games (ACG 2017). Leiden, Netherlands, July 3-5, 2017.
  • S. Stoiljkovikj, I. Bratko, M. Guid. A Computational Model for Estimating the Difficulty of Chess Problems. The Annual Third Conference on Advances in Cognitive Systems (ACS 2015), Atlanta, Georgia (USA); 28-31 May, 2015.    
  • V. Janko, M. Guid. Development of a Program for Playing Progressive Chess. The 14th International Conference on Advances in Computer Games (ACG 2015). Leiden, Netherlands, July 1-3, 2015.
  • M. Zapušek, M. Možina, I. Bratko, J. Rugelj, M. Guid: Designing an Interactive Teaching Tool with ABML Knowledge Refinement Loop. Intelligent Tutoring Systems (LNCS 8474): 575-582, 2014.
  • D. Hristova, M. Guid, I. Bratko. Toward modeling task difficulty: the case of chess. COGNITIVE 2014, Venice, Italy, May 25-29, 2014. The Sixth International Conference on Advanced Cognitive Technologies and Applications, pp. 211-214, 2014. IARIA.
  • M. Guid, M. Možina, C. Bohak, A. Sadikov, I. Bratko: Building an Intelligent Tutoring System for Chess Endgames. The 5th International Conference on Computer Supported Education: 263-266, 2013.
  • M. Guid, I. Bratko: Search-Based Estimation of Problem Difficulty for Humans. Artificial Intelligence in Education (LNCS 7926): 860-863, 2013.
  • M. Guid, M. Možina, V. Groznik, A. Sadikov, D. Georgiev, Z. Pirtošek, I. Bratko. ABML Knowledge Refinement Loop: A Case Study. 20th International Symposium, ISMIS 2012, Macau, China, December 4-7, 2012, Proceedings. Lecture Notes in Computer Science, Vol. 7661, pp. 41-50, 2012. Springer.
  • M. Mozina, M. Guid, A. Sadikov, V. Groznik, I. Bratko: Goal-Oriented Conceptualization of Procedural Knowledge. Pp. 286-291, ITS 2012.
  • M. Guid, J. Krivec, and I. Bratko. An experiment in students' acquisition of problem solving skill from goal-oriented instructions. COGNITIVE 2012.
  • M. Možina, M. Guid, A. Sadikov, V. Groznik, J. Krivec and I. Bratko. Conceptualizing Procedural Knowledge Targeted at Students of Different Skill Levels. The 3rd International Conference on Educational Data Mining - EDM, Pittsburg, USA, 2010.
  • M. Možina, M. Guid, J. Krivec, A. Sadikov, and I. Bratko. Learning to Explain with ABML. ExaCt'2010: The 5th International Workshop on Explanation-aware Computing, Lisbon, Portugal, August 16, 2010.
  • M. Guid, M. Možina, A. Sadikov, and I. Bratko. Deriving Concepts and Strategies from Chess Tablebases. Advances in Computers and Games conference - ACG 12, Pamplona, Spain, 2009.
  • J. Krivec, M. Guid, and I. Bratko. Identification and Characteristic Descriptions of Procedural Chunks. COGNITIVE 2009.
  • M. Možina, M. Guid, J. Krivec, A. Sadikov, and I. Bratko. Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning. 18th European Conference on Artificial Intelligence – ECAI, 2008.
  • M. Guid, M. Možina, J. Krivec, A. Sadikov, and I. Bratko. Learning Positional Features for Annotating Chess Games: A Case Study. Computers and Games, 6th International Conference, CG 2008, Beijing, China, September/ October, 2008. Proceedings. Computers and Games, Lecture Notes in Computer Science, Vol. 5131, pp. 192-204, 2008. Springer.
  • A. Sadikov, M. Možina, M. Guid, J. Krivec, and I. Bratko. Automated chess tutor. Computers and Games, 5th International Conference, CG 2006, Turin, Italy, May 29-31, 2006. Revised Papers. Computers and Games, Lecture Notes in Computer Science, Vol. 4630, pp. 13-25, 2007. Springer.
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