Learning Search Algorithms: An Educational View

• Autorzy: Janota M. , Šimák V. , Hrbček J.

Identyfikatory

DOI

10.12716/1001.08.04.11

• Języki publikacji: EN

Abstrakty

EN

Artificial intelligence methods find their practical usage in many applications including maritime industry. The paper concentrates on the methods of uninformed and informed search, potentially usable in solving of complex problems based on the state space representation. The problem of introducing the search algorithms to newcomers has its technical and psychological dimensions. The authors show how it is possible to cope with both of them through design and use of specialized authoring systems. A typical example of searching a path through the maze is used to demonstrate how to test, observe and compare properties of various search strategies. Performance of search methods is evaluated based on the common criteria.

Słowa kluczowe

EN

Search Algorithm; Learning Search Algorithms; educational view; artificial intelligence method; Search Method; Labyrinth; Dijkstra algorithm

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2014
• Tom: Vol. 8 no. 4
• Strony: 565--570
• Opis fizyczny: Bibliogr. 15 poz., rys., tab.

Twórcy

autor

Janota M.

• Faculty of Electrical Engineering, University of Žilina, Slovakia

autor

Šimák V.

• Faculty of Electrical Engineering, University of Žilina, Slovakia

autor

Hrbček J.

• Faculty of Electrical Engineering, University of Žilina, Slovakia

Bibliografia

• [1] R. A. Akerkar, and P. S. Sajja. Knowledge‐Based Systems. Jones & Bartlett Learning, 2010.
• [2] Artificial Intelligence Applications to Critical Transportation Issues. TRC E‐C168. November 2012. http://onlinepubs.trb.org/onlinepubs/circulars/ec168.pdf
• [3] D. O. Marlow and J. E. Murphy. Testing various backtracking algorithms in airborne maritime surveillance modelling. In 20th International Congress on Modelling and Simulation, Adelaide, Australia, 1–6 December 2013. http://www.mssanz.org.au/modsim2013/D1/marlow.pdf
• [4] Kilby, P., Tobin, P., Luscombe, R., Barry, S. and Hickson, R. The maritime surveillance problem. In T.R. Marchant, M. Edwards and G.N. Mercer (eds.), Proceedings of the 2007 Mathematics‐in‐Industry Study Group, 32‐56, 2008
• [5] S. Russell and P. Norvig. Solving problems by searching in Artificial Intelligence. A Modern Approach, 3rd ed. New Jersey: Prentice Hall, 2010, ch. 3, pp. 64–119.
• [6] D. S. Touretzky. Preparing computer science students for the robotics revolution. Communications of the ACM. 53(8): 27‐29, 2010.
• [7] T. W. Neller, C. G. Presser, I. Russell, and Z. Markov. Pedagogical possibilities for the dice game pig. J. Comput. Small Coll. 21(6), 149‐161, 2006.
• [8] D. Wong, R. Zink, and S. Koenig. Teaching Artificial Intelligence and Robotics via Games (Abstract). In AAAI Symposium on EAAI 2010, http://www.cs.huji.ac.il/~jeff/aaai10/02/AAAI10‐342.pdf
• [9] M. Zyda and S. Koenig. Teaching artificial intelligence playfully. In Proc. AAAI‐08 Education Colloquium, pages 90‐95, 2008.
• [10] P. J. Muñoz‐Merino, M. F. Molina, M. Muñoz‐Organero and C. D. Kloos. Motivation and Emotions in Competition Systems for Education: An Empirical Study. IEEE Transactions on education. 57(3): 182‐187, 2014.
• [11] B. Chandrasekaran, J. R. Josephson, and V. R. Benjamins. What are ontologies, and why do we need them? IEEE Intelligent Systems. 1(4): 20–26, 1999.
• [12] W. Zhang. State Space Search for Problem Solving. State Space Search. Algorithms, Complexity, Extensions, and Applications. 1st ed. New York: Springer‐Verlag, 1999.
• [13] R. Varela and E. Soto. Scheduling as heuristic search with state space revolution. Lecture Notes in Computer Science. 2527: 815‐824, 2002.
• [14] G. Kovásznai and G. Kusper. Artificial Intelligence and its teaching. 1st ed., 1990 http://aries.ektf.hu/~gkusper/ArtificialIntelligence_LectureNotes.v.1.0.4.pdf
• [15] S. M. Lord, R. A. Layton, and M. W. Ohland. Trajectories of Electrical Engineering and Computer Engineering Students by Race and Gender. IEEE Transactions on education. 54(4): 610‐618, 2011.