Learning What to Value
I. J. Good’s intelligence explosion theory predicts that ultraintelligent agents will undergo a process of repeated self-improvement; in the wake of such an event, how well our values are fulfilled would depend on the goals of these ultraintelligent agents. With this motivation, we examine ultraintelligent reinforcement learning agents. Reinforcement learning can only be used in the real world to define agents whose goal is to maximize expected rewards, and since this goal does not match with human goals, AGIs based on reinforcement learning will often work at cross-purposes to us. To solve this problem, we define value learners, agents that can be designed to learn and maximize any initially unknown utility function so long as we provide them with an idea of what constitutes evidence about that utility function.
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- 1.Good, I.J.: Speculations Concerning the First Ultraintelligent Machine. In: Alt, F.L., Rubinoff, M. (eds.) Advances in Computers, vol. 6, pp. 31–88 (1965)Google Scholar
- 2.Hay, N.: Optimal Agents (2007), http://www.cs.auckland.ac.nz/~nickjhay/honours.revamped.pdf
- 4.Hutter, M.: http://www.hutter1.net/ai/uaibook.htm#oneline
- 5.Omohundro: The Nature of Self-Improving Artificial Intelligence, http://omohundro.files.wordpress.com/2009/12/nature_of_self_improving_ai.pdf
- 6.Omohundro, S.: The basic AI drives. In: Wang, P., Goertzel, B., Franklin, S. (eds.) Proceedings of the First AGI Conference on Frontiers in Artificial Intelligence and Applications, vol. 171. IOS Press, Amsterdam (2008)Google Scholar
- 7.Russell, S., Norvig, P.: AI A Modern Approach. Prentice-Hall, Englewood Cliffs (1995)Google Scholar
- 8.Yudkowsky, E.: Artificial intelligence as a positive and negative factor in global risk. In: Bostrom, N. (ed.) Global Catastrophic Risks. Oxford University Press, Oxford (2008)Google Scholar