Work place: University of Split, Faculty of science, 21000 Split Croatia
E-mail: goran.zaharija@pmfst.hr
Website:
Research Interests: Computer systems and computational processes, Artificial Intelligence, Neural Networks, Computer Networks, Data Structures and Algorithms
Biography
Goran Zaharija was born in Split, Croatia on August 13, 1985. He received the B.Sc. and M.Sc. degrees from University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, in 2008 and 2010, respectively.
He is a Junior Researcher at the Department of Informatics at the Faculty of Science, University of Split. He teaches several undergraduate courses in programming and computer architecture. He is currently working on his PhD research in the field of Artificial Intelligence. His other scientific interests include machine learning, artificial neural networks and multi-agent systems.
By Goran Zaharija Sasa Mladenovic Stefan Dunic
DOI: https://doi.org/10.5815/ijisa.2017.03.01, Pub. Date: 8 Mar. 2017
Goals, Operators, Methods, and Selection rules (GOMS) model is a widely recognised concept in Human-Computer Interaction (HCI). Since the initial idea, several GOMS techniques were developed that were used for analysis, differing in their form defined by the logical structure and prediction power. Through defined operators and methods and following the certain rules, the user can reach a specific goal. This work represents an effort to apply GOMS method in the field of artificial intelligence, specifically on a state-space search problems. Card, Morgan, Newman GOMS (CMN-GOMS) model has been chosen, since it represents ground-floor of the GOMS idea that solves the given task through a sequence of operators. Compared with the informed search algorithms for solving the given task, CMN-GOMS model gave better results. Moreover, it was shown that this model could be used in any other space motion problem in the natural environment. LEGO® MINDSTORMS® EV3 robot was used to demonstrate the application of GOMS model in real world pathfinding problems and as a test-bed for comparing proposed model with well-known search algorithms.
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