LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Burke, Edmund; MacCarthy, Bart L.; Petrovic, Sanja; Qu, Rong (2000)
Languages: English
Types: Article
Subjects:
In this paper, we present a case-based reasoning (CBR) approach solving educational time-tabling problems. Following the basic idea behind CBR, the solutions of previously solved problems are employed to aid finding the solutions for new problems. A list of feature-value pairs is insufficient to represent all the necessary information. We show that attribute graphs can represent more information and thus can help to retrieve re-usable cases that have similar structures to the new problems. The case base is organised as a decision tree to store the attribute graphs of solved problems hierarchically. An example is given to illustrate the retrieval, re-use and adaptation of structured cases. The results from our experiments show the effectiveness of the retrieval and adaptation in the proposed method.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] Waston I and Marir F, Case-based reasoning: a review, The Knowledge Engineering Review 9 (1994) 327-354.
    • [2] Smyth B and Keane ML, Adaptation-guided retrieval: questioning the similarity assumption in reasoning, Artificial Intelligence 102 (1998) 249-293.
    • [3] Börner K, Structural similarity as guidance in case-based design, in: Wess S, Althoff KD and Richter M, eds., Topics in Case-based Reasoning, (Springer-Verlag, Kaiserslautern, 1993) 197-208 (EWCBR-93).
    • [4] Börner K, Coulon CH, Pippig E and Tammer EC, Structural similarity and adaptation, in: Smith I and Faltings B, eds., Advances in Case-based Reasoning (Springer-Verlag, Switzerland, 1996) 58-75 (EWCBR-96).
    • [5] Ricci F and Senter L, Structured cases, trees and efficient retrieval, to appear in Proceedings of the Fourth European Workshop on Case-based Reasoning, (Springer-Verlag, Dublin, 1998).
    • [6] Sanders KE, Kettler BP and Hendler JA, The case for graph-structured representations, to appear in Proceedings of the Second International Conference on Case-based Reasoning, (Springer-Verlag, Berlin, 1997).
    • [7] Andersen WA, Evett MP, Kettler B and Hendler J, Massively parallel support for case-based planning, IEEE Expert 7 (1994) 8-14.
    • [8] Jantke KP, Nonstandard concepts of similarity in case-based reasoning, Proceedings of the 17th Annual Conference of the “Gesellllschaft fűr klassifikation e.V.”, (Springer-Verlag, Kaiderslautern, 1993).
    • [9] Gebhardt F, Methods and systems for case retrieval exploiting the case structure, FABEL-Report 39, GMD, Sankt Augustin, 1995.
    • [10] MacCarthy B and Jou P, Case-based reasoning in scheduling, in: Khan MK and Wright CS, eds., Proceedings of the Symposium on Advanced Manufacturing Processes, Systems and Techniques (AMPST96), (MEP Publications Ltd, 1996) 211-218.
    • [11] Koton P, SMARTlan: A case-based resource allocation and scheduling system, in: Proceedings: Workshop on Case-based Reasoning (DARPA) 1989 285-289.
    • [12] Bezirgan A, A case-based approach to scheduling constraints, in: Dorn J and Froeschl KA ed., Scheduling of Production Processes, (Ellis Horwood Limited, 1993) 48-60.
    • [13] Miyashita K and Sycara K, Adaptive case-based control of scheduling revision, in: Zweben M and Fox MS, eds., Intelligent Scheduling, (Morgan Kaufmann, 1994) 291-308.
    • [14] Hennessy D and Hinkle D, Applying case-based reasoning to autoclave loading, IEEE Expert, 7 (1992) 21-26.
    • [15] Cunningham P and Smyth B, Case-based reasoning in scheduling: reusing solution components, The International Journal of Production Research 35 (1997) 2947-2961.
    • [16] Schmidt G, Case-based reasoning for production scheduling, International Journal of Production Economics 56-57 (1998) 537-546.
    • [17] MacCarthy B and Jou P, A case-based expert system for scheduling problems with sequence dependent set up times, in: Adey RA and Rzevski G, eds., Applications of Artificial Intelligence in Engineering X, (Computational Machines Publications, Southampton, 1995) 89-96.
    • [18] Wren A, Scheduling timetabling and rostering - a special relationship, in: [33] 46-76.
    • [19] Carter MW and Laporte G, Recent developments in practical examination timetabling, in: [33] 3- 21.
    • [20] Carter MW and Laporte G, Recent developments in practical course timetabling, in: [34] 3-19.
    • [21] Boufflet JP and Negre S, Three methods to solve an examination timetable problem, in: [33] 327- 344.
    • [22] Dowsland KA, Off-the-peg or made to measure? Timetabling and scheduling with SA and TS, in: [34] 37-52.
    • [23] Thomson JM and Dowsland KA, General cooling schedules for a simulate annealing based timetabling system, in: [33] 345-364.
    • [24] Elmohamed MAS, Coddington P and Fox G, A comparison of annealing techniques for academic course timetabling, in: [34] 92-112.
    • [25] Rich DC, A smart genetic algorithm for university timetabling, in: [33] 181-197.
    • [26] Erben W and Keppler J, A genetic algorithm solving a weekly course-timeatbling problem, in: [33] 198-211.
    • [27] Ross P, Hart E and Corne D, Some observations about GA-based exam timetabling, in: [34] 115- 129.
    • [28] Burke EK, Newell JP and Weare RF, A memetic algorithm for university exam timetabling, in: [33] 241-250.
    • [29] Burke EK and Newall JP, A multi-stage evolutionary algorithm for the timetable problem, IEEE Transactions on Evolutionary Computation 3 (1999) 63-74.
    • [30] Burke EK, Newell JP and Weare RF, Initialisation strategies and diversity in evolutionary timetabling, Evolutionary Computation Journal (special issue on scheduling) 6 (1998) 81-103.
    • [31] Paechter B, Cumming A and Luchian H, The use of local search suggestion lists for improving the solution of timetable problems with evolutionary algorithms, in: Goos G, Hartmanis J and Leeuwen J, eds., AISB workshop, (Springer-Verlag, Sheffield, 1995) 86-93 (Lecture Notes in Computer Science 993).
    • [32] Paechter B, Cumming A, Norman MG and Luchian H, Extensions to a memetic timetabing system, in: [33] 251-265.
    • [33] Burke E and Ross P, eds., The Practice and Theory of Automated Timetabling: Selected Papers from the First International Conference, (Springer-Verlag, Berlin, 1996) (Lecture Notes in Computer Science 1153).
    • [34] Burke E and Carter M eds., The Practice and Theory of Automated Timetabling: Selected Papers from the Second International Conference, (Springer-Verlag, Berlin, 1997) (Lecture notes in computer science 1408).
    • [35] Garey MR and Johnson DS, Computers and Intractability: A Guide to the Theory of NPCompleteness, (Freeman and Company. New York, 1979).
    • [36] Messmer BT, Efficient graph matching algorithms for preprocessed model graph, PhD thesis, University of Bern, Switzerland, 1995.
    • [37] Burke EK, Elliman DG and Weare RF, A university timetabling system based on graph colouring and constraint manipulation, Journal of Research on Computing in Education 27 (1994) 1-18.
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

    Title Year Similarity

    The range of the heat operator

    200471
    71%

Share - Bookmark

Cite this article