The duality of utility and information in optimally learning systems

Author

Belavkin, Roman V.

Author_Institution

Sch. of Comput. Sci., Middlesex Univ., London

fYear

2008

fDate

9-10 Sept. 2008

Firstpage

1

Lastpage

6

Abstract

The paper considers learning systems as optimisation systems with dynamical information constraints, and general optimality conditions are derived using the duality between the space of utility functions and probability measures. The increasing dynamics of the constraints is used to parametrise the optimal solutions which form a trajectory in the space of probability measures. Stochastic processes following such trajectories describe systems achieving the maximum possible utility gain with respect to a given information. The theory is discussed on examples for finite and uncountable sets and in relation to existing applications and cognitive models of learning.

Keywords

learning systems; optimisation; stochastic processes; dynamical information constraints; optimally learning systems; optimisation systems; stochastic process; Artificial intelligence; Bayesian methods; Constraint optimization; Dynamic programming; Information theory; Learning systems; Optimization methods; Signal processing algorithms; Stochastic processes; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Cybernetic Intelligent Systems, 2008. CIS 2008. 7th IEEE International Conference on

Conference_Location

London

Print_ISBN

978-1-4244-2914-1

Electronic_ISBN

978-1-4244-2915-8

Type

conf

DOI

10.1109/UKRICIS.2008.4798960

Filename

4798960