A Bayesian rule for adaptive control based on causal interventions

Artificial General Intelligence - Proceedings of the Third Conference on Artificial General Intelligence, AGI 2010

Volumn , Issue , 2010, Pages 121-126

  Ortega, Pedro A ^a   Braun, Daniel A ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Adaptive behavior; Bayesian control; Intervention calculus; Kullback leibler divergence

Indexed keywords

ADAPTIVE CONTROL SYSTEMS; MIXTURES;

ADAPTIVE BEHAVIOR; ARTIFICIAL INTELLIGENCE RESEARCH; BAYESIAN CONTROL; BAYESIAN MIXTURE; CAUSAL INTERVENTION; INTERVENTION CALCULUS; KULLBACK LEIBLER DIVERGENCE; MIXTURE DISTRIBUTIONS;

CALCULATIONS;

EID: 77954107456     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.2991/agi.2010.39     Document Type: Conference Paper

References (12)

1. R. Beer. Intelligence as Adaptive Behavior. Academic Press, Inc., 1990.
2. M. Haruno, D.M. Wolpert, and M. Kawato. Mosaic model for sensorimotor learning and control. Neural Computation, 13:2201-2220, 2001.
3. D. Haussler and M. Opper. Mutual information, metric entropy and cumulative relative entropy risk. The Annals of Statistics, 25:2451-2492, 1997.
4. M. Hutter. Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability. Springer, Berlin, 2004.
5. M. Littman, R. Sutton, and S. Singh. Predictive representations of state. In Neural Information Processing Systerns (NIPS), number 14, pages 1555-1561, 2002.
6. D.J.C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.
7. K. Narendra and M.A.L. Thathachar. Learning automata- a survey. IEEE Transactions on Systems, Man, and Cybernetics, SMC-4(4):323-334, July 1974.
8. M. Opper. A bayesian approach to online learning. Online Learning in Neural Networks, pages 363-378, 1998.
9. J. Pearl. Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge, UK, 2000.
10. D.G. Stork, R.O. Duda, P.E. Hart. Pattern Classification. Wiley & Sons, Inc., second edition, 2001.
11. J. Schmidhuber. Simple algorithmic theory of subjective beauty, novelty, surprise, interestingness, attention, curiosity, creativity, art, science, music, jokes. Journal of SICE, 48(1):21-32, 2009.
12. J. Wyatt. Exploration and Inference in Learning from Reinforcement. PhD thesis, Department of Artificial Intelligence, University of Edinburgh, 1997.