Alamino, R., and Nestor, C. 2006. Online learning in discrete hidden Markov models. In: Djafari, A. M. (ed) Proc. AIP Conf, vol. 872(1), pp. 187-194.
Baum, L. E., Petrie, T., Soules, G., and Weiss, N. 1970. A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann. Math. Statist., 41(1), pp. 164-171.
Bishop, C. 2006. Pattern Recognition and Machine Learning. Springer, Berlin.
Bostrom, N. 2003. Ethical issues in advanced artificial intelligence. In: Smit, I. et al (eds) Cognitive, Emotive and Ethical Aspects of Decision Making in Humans and in Artificial Intelligence, Vol. 2, pp. 12-17. Int. Institute of Advanced Studies in Systems Research and Cybernetics.
Dewey, D. 2011. Learning what to value. In: Schmidhuber, J., Thórisson, K. R., and Looks, M. (eds) AGI 2011. LNCS (LNAI), vol. 6830, pp. 309-314. Springer, Heidelberg.
Ghahramani, Z. 1997. Learning dynamic Bayesian networks. In: Giles, C., and Gori, M. (eds), Adaptive Processing of Temporal Information. LNCS, vol. 1387, pp. 168-197. Springer, Heidelberg.
Gisslén, L., Luciw, M., Graziano, V., and Schmidhuber, J. 2011. Sequential constant size compressors for reinforcement learning. In: Schmidhuber, J., Thórisson, K. R., and Looks, M. (eds) AGI 2011. LNCS (LNAI), vol. 6830, pp. 31-40. Springer, Heidelberg.
Goertzel, B. 2004. Universal ethics: the foundations of compassion in pattern dynamics. http://www.goertzel.org/papers/UniversalEthics.htm
Hibbard, B. 2008. The technology of mind and a new social contract. J. Evolution and Technology 17(1), pp. 13-22.
Hutter, M. 2005. Universal artificial intelligence: sequential decisions based on algorithmic probability. Springer, Heidelberg.
Hutter, M. 2009a. Feature reinforcement learning: Part I. Unstructured MDPs. J. Artificial General Intelligence 1, pp. 3-24.
Hutter, M. 2009b. Feature dynamic Bayesian networks. In: Goertzel, B., Hitzler, P., and Hutter, M. (eds) AGI 2009. Proc. Second Conf. on AGI, pp. 67-72. Atlantis Press, Amsterdam.
Koutroumbas, K., and Theodoris, S. 2008. Pattern recognition (4th ed.). Academic Press, Boston.
Li, M., and Vitanyi, P. 1997. An introduction to Kolmogorov complexity and its applications. Springer, Heidleberg.
Lloyd, S. Computational Capacity of the Universe. Phys. Rev. Lett. 88 (2002) 237901.
Olds, J., and P. Milner, P. 1954. Positive reinforcement produced by electrical stimulation of septal area and other regions of rat brain. J. Comp. Physiol. Psychol. 47, pp. 419-427.
Omohundro, S. 2008. The basic AI drive. In Wang, P., Goertzel, B., and Franklin, S. (eds) AGI 2008. Proc. First Conf. on AGI, pp. 483-492. IOS Press, Amsterdam.
Orseau, L., and Ring, M. 2011a. Self-modification and mortality in artificial agents. In: Schmidhuber, J., Thórisson, K. R., and Looks, M. (eds) AGI 2011. LNCS (LNAI), vol. 6830, pp. 1-10. Springer, Heidelberg.
Puterman, M. L. 1994. Markov Decision Processes - Discrete Stochastic Dynamic Programming. Wiley, New York.
Ring, M., and Orseau, L. 2011b. Delusion, survival, and intelligent agents. In: Schmidhuber, J., Thórisson, K. R., and Looks, M. (eds) AGI 2011. LNCS (LNAI), vol. 6830, pp. 11-20. Springer, Heidelberg.
Russell, S., and Norvig, P. 2010. Artificial intelligence: a modern approach (3rd ed.). Prentice Hall, New York.
Schmidhuber, J. 2002. The speed prior: a new simplicity measure yielding near-optimal computable predictions. In: Kiven, J., and Sloan, R. H. (eds) COLT 2002. LNCS (LNAI), vol. 2375, pp. 216-228. Springer, Heidelberg.
Schmidhuber, J. 2009. Ultimate cognition à la Gödel. Cognitive Computation 1(2), pp. 177-193.
Sutton, R. S., and Barto, A. G. 1998. Reinforcement learning: an introduction. MIT Press.
Wang, P. 1995. Non-Axiomatic Reasoning System — Exploring the essence of intelligence. PhD Dissertation, Indiana University Comp. Sci. Dept. and the Cog. Sci. Program.
Wasser, M. 2011. Rational universal benevolence: simpler, safer, and wiser than "friendly AI." In: Schmidhuber, J., Thórisson, K. R., and Looks, M. (eds) AGI 2011. LNCS (LNAI), vol. 6830, pp. 153-162. Springer, Heidelberg.
Yudkowsky, E. 2004. CoherentExtrapolatedVolition. http://www.sl4.org/wiki/CollectiveVolition
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Model-based Utility Functions
1Space Science and Engineering Center, University of Wisconsin - Madison, 1225 W. Dayton Street Madison, WI 53706, USA
Citation Information: Journal of Artificial General Intelligence. Volume 3, Issue 1, Pages 1–24, ISSN (Online) 1946-0163, DOI: 10.2478/v10229-011-0013-5, May 2012
- Published Online:
Orseau and Ring, as well as Dewey, have recently described problems, including self-delusion, with the behavior of agents using various definitions of utility functions. An agent's utility function is defined in terms of the agent's history of interactions with its environment. This paper argues, via two examples, that the behavior problems can be avoided by formulating the utility function in two steps: 1) inferring a model of the environment from interactions, and 2) computing utility as a function of the environment model. Basing a utility function on a model that the agent must learn implies that the utility function must initially be expressed in terms of specifications to be matched to structures in the learned model. These specifications constitute prior assumptions about the environment so this approach will not work with arbitrary environments. But the approach should work for agents designed by humans to act in the physical world. The paper also addresses the issue of self-modifying agents and shows that if provided with the possibility to modify their utility functions agents will not choose to do so, under some usual assumptions.