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Volumn 11, Issue 1-5, 1997, Pages 343-370

A Teaching Strategy for Memory-Based Control

Author keywords

Differential games; Genetic algorithms; Lazy learning; Nearest neighbor; Pursuit games; Reinforcement learning; Teaching

Indexed keywords

DATA STORAGE EQUIPMENT; GAME THEORY; GENETIC ALGORITHMS; INTELLIGENT CONTROL; LEARNING ALGORITHMS; PROBLEM SOLVING;

EID: 0031071704     PISSN: 02692821     EISSN: None     Source Type: Journal    
DOI: 10.1007/978-94-017-2053-3_13     Document Type: Article
Times cited : (25)

References (52)
  • 2
    • 0000217085 scopus 로고
    • Tolerating noisy, irrelevant, and novel attributes in instance-based learning algorithms
    • Aha, D. W. (1992). Tolerating noisy, irrelevant, and novel attributes in instance-based learning algorithms. International Journal of Man-Machine Studies 16: 267-287.
    • (1992) International Journal of Man-Machine Studies , vol.16 , pp. 267-287
    • Aha, D.W.1
  • 3
    • 2342560362 scopus 로고
    • Using local models to control movement
    • Touretzky, D. S. (ed.). San Mateo, CA: Morgan Kaufman
    • Atkeson, C. (1990). Using local models to control movement. In Touretzky, D. S. (ed.), Advances in Neural Information Processing Systems 2, 316-323. San Mateo, CA: Morgan Kaufman.
    • (1990) Advances in Neural Information Processing Systems , vol.2 , pp. 316-323
    • Atkeson, C.1
  • 4
    • 0005760958 scopus 로고
    • Memory-based approaches to approximating continuous functions
    • Casdagli, M. & Eubanks, S. (eds.). Addison Wesley
    • Atkeson, C. G. (1992). Memory-based approaches to approximating continuous functions. In Casdagli, M. & Eubanks, S. (eds.), Nonlinear Modeling and Forecasting, pp. 503-521. Addison Wesley.
    • (1992) Nonlinear Modeling and Forecasting , pp. 503-521
    • Atkeson, C.G.1
  • 6
    • 0002201501 scopus 로고
    • Learning and sequential decision making
    • Gabriel & Moore (eds.). Cambridge: MIT Press
    • Barto, A., Sutton, R. & & Watkins, C. (1990). Learning and sequential decision making. In Gabriel & Moore (eds.), Learning and Computational Neuroscience, pp. 539-602. Cambridge: MIT Press.
    • (1990) Learning and Computational Neuroscience , pp. 539-602
    • Barto, A.1    Sutton, R.2    Watkins, C.3
  • 9
    • 0023381915 scopus 로고
    • Planning for conjunctive goals
    • Chapman, D. (1987). Planning for conjunctive goals. Artificial Intelligence 32: 333-377.
    • (1987) Artificial Intelligence , vol.32 , pp. 333-377
    • Chapman, D.1
  • 11
    • 84976922517 scopus 로고
    • Training agents to perform sequential behavior
    • Colombetti, M. & Dorigo, M. (1994). Training agents to perform sequential behavior. Adaptive Behavior 2(3): 247-275.
    • (1994) Adaptive Behavior , vol.2 , Issue.3 , pp. 247-275
    • Colombetti, M.1    Dorigo, M.2
  • 13
    • 38249042848 scopus 로고
    • On the editing rate of the multiedit algorithm
    • Devijver, P. A. (1986). On the editing rate of the multiedit algorithm. Pattern Recognition Letters 4: 9-12.
    • (1986) Pattern Recognition Letters , vol.4 , pp. 9-12
    • Devijver, P.A.1
  • 15
    • 0028739953 scopus 로고
    • Robot shaping: Developing autonomous agents through learning
    • Dorigo, M. & Colombetti, M. (1994). Robot shaping: Developing autonomous agents through learning. Artificial Intelligence 71(2): 321-370.
    • (1994) Artificial Intelligence , vol.71 , Issue.2 , pp. 321-370
    • Dorigo, M.1    Colombetti, M.2
  • 18
    • 0003182781 scopus 로고
    • A multistrategy learning scheme for agent knowledge acquisition
    • Gordon, D. & Subramanian, D. (1993a). A multistrategy learning scheme for agent knowledge acquisition. Informatica 17: 331-346.
    • (1993) Informatica , vol.17 , pp. 331-346
    • Gordon, D.1    Subramanian, D.2
  • 20
    • 0000146518 scopus 로고
    • Credit assignment in rule discovery systems based on genetic algorithms
    • Grefenstette, J. (1988). Credit assignment in rule discovery systems based on genetic algorithms. Machine Learning 3, 225-245.
    • (1988) Machine Learning , vol.3 , pp. 225-245
    • Grefenstette, J.1
  • 22
    • 0000488536 scopus 로고
    • Learning sequential decision rules using simulation models and competition
    • Grefenstette, J., Ramsey, C. & Schultz, A. (1990). Learning sequential decision rules using simulation models and competition. Machine Learning 5: 355-381.
    • (1990) Machine Learning , vol.5 , pp. 355-381
    • Grefenstette, J.1    Ramsey, C.2    Schultz, A.3
  • 23
    • 84931162639 scopus 로고
    • The condensed nearest neighbor rule
    • Hart, P. (1968). The condensed nearest neighbor rule. IEEE Transactions on Information Theory 14(3): 515-516.
    • (1968) IEEE Transactions on Information Theory , vol.14 , Issue.3 , pp. 515-516
    • Hart, P.1
  • 25
    • 38249001658 scopus 로고
    • Pursuit-evasion geometry analysis between two missiles and an aircraft
    • Imado, F. & Ishihara, T. (1993). Pursuit-evasion geometry analysis between two missiles and an aircraft. Computers and Mathematics wth Applications 26(3): 125-139.
    • (1993) Computers and Mathematics wth Applications , vol.26 , Issue.3 , pp. 125-139
    • Imado, F.1    Ishihara, T.2
  • 28
    • 85149834820 scopus 로고
    • Markov games as a framework for multi-agent reinforcement learning
    • New Brunswick, NJ: Morgan Kaufmann
    • Littman, M. (1994). Markov games as a framework for multi-agent reinforcement learning. In Proceedings of the Eleventh International Machine Conference, pp. 157-163. New Brunswick, NJ: Morgan Kaufmann.
    • (1994) Proceedings of the Eleventh International Machine Conference , pp. 157-163
    • Littman, M.1
  • 29
    • 85153937136 scopus 로고
    • Instance-based state identification for reinforcement learning
    • McCallum, R. A. (1995). Instance-based state identification for reinforcement learning. In Advances in Neural Information Processing Systems 7, pp. 377-384.
    • (1995) Advances in Neural Information Processing Systems , vol.7 , pp. 377-384
    • McCallum, R.A.1
  • 30
    • 0000714373 scopus 로고
    • A reinforcement connectionist approach to robot path finding in non-maze-like environments
    • Millan, J. & Torras, C. (1992). A reinforcement connectionist approach to robot path finding in non-maze-like environments. Machine Learning 8: 363-395.
    • (1992) Machine Learning , vol.8 , pp. 363-395
    • Millan, J.1    Torras, C.2
  • 31
    • 79952785777 scopus 로고
    • An empirical comparison of pruning methods for decision tree induction
    • Mingers, J. (1989). An empirical comparison of pruning methods for decision tree induction. Machine Learning 4(2): 227-243.
    • (1989) Machine Learning , vol.4 , Issue.2 , pp. 227-243
    • Mingers, J.1
  • 33
    • 0027684215 scopus 로고
    • Prioritized sweeping: Reinforcement learning with less data and less time
    • Moore, A. & Atkeson, C. (1993). Prioritized sweeping: Reinforcement learning with less data and less time. Machine Learning 13: 103-130.
    • (1993) Machine Learning , vol.13 , pp. 103-130
    • Moore, A.1    Atkeson, C.2
  • 40
    • 2342639406 scopus 로고
    • Tech. Rep. JHU-93/94-02, Department of Computer Science, Johns Hopkins University, Baltimore, Maryland. Revised May
    • Sheppard, J. W. & Salzberg, S. L. (1993). Memory-based learning of pursuit games. Tech. Rep. JHU-93/94-02, Department of Computer Science, Johns Hopkins University, Baltimore, Maryland. Revised May, 1995.
    • (1993) Memory-based Learning of Pursuit Games
    • Sheppard, J.W.1    Salzberg, S.L.2
  • 41
    • 0012657799 scopus 로고
    • Prototype and feature selection by sampling and random mutation hill climbing algorithms
    • New Brunswick, NJ: Morgan Kaufman
    • Skalak, D. (1994). Prototype and feature selection by sampling and random mutation hill climbing algorithms. In Proceedings of the Eleventh International Machine Learning Conference, pp. 293-301. New Brunswick, NJ: Morgan Kaufman.
    • (1994) Proceedings of the Eleventh International Machine Learning Conference , pp. 293-301
    • Skalak, D.1
  • 43
    • 33847202724 scopus 로고
    • Learning to predict my methods of temporal differences
    • Sutton, R. (1988). Learning to predict my methods of temporal differences. Machine Learning 3: 9-44.
    • (1988) Machine Learning , vol.3 , pp. 9-44
    • Sutton, R.1
  • 44
    • 0001046225 scopus 로고
    • Practical issues in temporal difference learning
    • Tesauro, G. (1992). Practical issues in temporal difference learning. Machine Learning 8: 257-277.
    • (1992) Machine Learning , vol.8 , pp. 257-277
    • Tesauro, G.1
  • 45
    • 0024702037 scopus 로고
    • A parallel network that learns to play backgammon
    • Tesauro, G. & Sejnowski, T. J. (1989). A parallel network that learns to play backgammon. Artificial Intelligence 39: 357-390.
    • (1989) Artificial Intelligence , vol.39 , pp. 357-390
    • Tesauro, G.1    Sejnowski, T.J.2
  • 48
    • 0004049895 scopus 로고
    • Ph.D. thesis, Cambridge University, Department of Computer Science, Cambridge, England
    • Watkins, C. (1989). Learning with Delayed Rewards. Ph.D. thesis, Cambridge University, Department of Computer Science, Cambridge, England.
    • (1989) Learning with Delayed Rewards
    • Watkins, C.1
  • 51
    • 0015361129 scopus 로고
    • Asymptotic properties of nearest neighbor rules using edited data
    • Wilson, D. (1972). Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man, and Cybernetics 2(3): 408-421.
    • (1972) IEEE Transactions on Systems, Man, and Cybernetics , vol.2 , Issue.3 , pp. 408-421
    • Wilson, D.1
  • 52
    • 85107848924 scopus 로고
    • Selecting typical instances in instance-based learning
    • Aberdeen, Scotland: Morgan Kaufman
    • Zhang, J. (1992). Selecting typical instances in instance-based learning. In Proceedings of the Ninth International Machine Learning Conference, pp. 470-479. Aberdeen, Scotland: Morgan Kaufman.
    • (1992) Proceedings of the Ninth International Machine Learning Conference , pp. 470-479
    • Zhang, J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.