Preference Elicitation in Fully Probabilistic Design of Decision Strategies

0353209 - ÚTIA 2011 RIV US eng C - Conference Paper (international conference)
Kárný, Miroslav - Guy, Tatiana Valentine
Preference Elicitation in Fully Probabilistic Design of Decision Strategies.
Proceedings of the 49th IEEE Conference on Decision and Control. Atlanta: IEEE, 2010, s. 5327-5332. ISBN 978-1-4244-7745-6. ISSN 0743-1546.
[49th IEEE Conference on Decision and Control. Atlanta (US), 14.12.2010-18.12.2010]
R&D Projects: GA ČR GA102/08/0567
Institutional research plan: CEZ:AV0Z10750506
Keywords : knowledge elicitation * Bayesian decision making * fullz probabilistic design
Subject RIV: BB - Applied Statistics, Operational Research
http://library.utia.cas.cz/separaty/2010/AS/karny-preference elicitation in fully probabilistic design of decision strategies.pdf

Any systematic decision-making design selects a decision strategy that makes the resulting closed-loop behaviour close to the desired one. Fully Probabilistic Design (FPD) describes modelled and desired closed-loop behaviours via their distributions. The designed strategy is a minimiser of Kullback-Leibler divergence of these distributions. FPD: i) unifies modelling and aim-expressing languages; ii) directly describes multiple aims and constraints; iii) simplifies an (inevitable) approximate design as it has an explicit minimiser. The paper enriches the theory of FPD, in particular, it: i) improves its axiomatic basis; ii) quantitatively relates FPD to standard Bayesian decision making showing that the set of FPD tasks is a dense extension of Bayesian problem formulations; iii) opens a way to a systematic data-based preference elicitation, i.e., quantitative expression of decision-making aims.
Permanent Link: http://hdl.handle.net/11104/0006221