Abstract
We propose that it is appropriate to more seriously consider the nature of systems that are capable of learning over a lifetime. There are three reasons for taking this position. First, there exists a body of related work for this research under names such as constructive induction, continual learning, sequential task learning and most recently learning with deep architectures. Second, the computational and data storage power of modern computers are capable of implementing and testing machine lifelong learning systems. Third, there are significant challenges and benefits to pursuing programs of research in the area to AGI and brain sciences. This paper discusses each of the above in the context of a general framework for machine lifelong learning.
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References
Bengio, Y.: Learning deep architectures for ai. Foundations and Trends in Machine Learning 2(1), 1–127 (2009)
Michalski, R.S.: Learning = inferencing + memorizing. In: Foundations of Knowledge Acquistion: Machine Learning, pp. 1–41 (1993)
Mitchell, T.M.: The need for biases in learning generalizations. In: Shavlik, J.W., Dietterich, T.G. (eds.) Readings in Machine Learning, pp. 184–191 (1980)
Ring, M.B.: Child: A first step towards continual learning. In: Machine Learning, pp. 77–104 (1997)
Shultz, T.R., Rivest, F.: Knowledge-based cascade-correlation: using knowledge to speed learning. Connect. Sci. 13(1), 43–72 (2001)
Silver, D.L., Alisch, R.: A measure of relatedness for selecting consolidated task knowledge. In: Proceedings of the 18th Florida Artificial Intelligence Research Society Conference (FLAIRS 2005), pp. 399–404 (2005)
Silver, D.L., Mercer, R.E.: The parallel transfer of task knowledge using dynamic learning rates based on a measure of relatedness. Connection Science Special Issue: Transfer in Inductive Systems 8(2), 277–294 (1996)
Silver, D.L., Mercer, R.E.: The task rehearsal method of life-long learning: Overcoming impoverished data. In: Advances in Artificial Intelligence, 15th Conference of the Canadian Society for Computational Studies of Intelligence (AI 2002), pp. 90–101 (2002)
Silver, D.L., Poirier, R.: Sequential consolidation of learned task knowledge. In: Canadian AI 2004. Lecture Notes in AI, pp. 217–232 (2004)
Silver, D.L., Poirier, R., Currie, D.: Inductive tranfser with context-sensitive neural networks. Machine Learning 73(3), 313–336 (2008)
Solomonoff, R.J.: A system for incremental learning based on algorithmic probability. In: Probability, Proceedings of the Sixth Israeli Conference on Artificial Intelligence, Computer Vision and Pattern Recognition, pp. 515–527 (1989)
Thrun, S.: Explanation-Based Neural Network Learning: A Lifelong Learning Approach. Kluwer Academic Publishers, Boston (1996)
Thrun, S.: Lifelong learning algorithms. In: Learning to Learn, pp. 181–209. Kluwer Academic Publishers, Dordrecht (1997)
Utgoff, P.E.: Adjusting bias in concept learning. In: Proceedings of IJCAI-1983, pp. 447–449 (1983)
Utgoff, P.E.: Machine Learning of Inductive Bias. Kluwer Academc Publisher, Boston (1986)
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Silver, D.L. (2011). Machine Lifelong Learning: Challenges and Benefits for Artificial General Intelligence. In: Schmidhuber, J., Thórisson, K.R., Looks, M. (eds) Artificial General Intelligence. AGI 2011. Lecture Notes in Computer Science(), vol 6830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22887-2_45
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DOI: https://doi.org/10.1007/978-3-642-22887-2_45
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