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Conference Paper (international conference)

Preference Elicitation within Framework of Fully Probabilistic Design of Decision Strategies

Kárný Miroslav, Guy Tatiana Valentine

: IFAC-PapersOnLine. Volume 52, Issue 29 - Proceedings of the 13th IFAC Workshop on Adaptive and Learning Control Systems 2019, p. 239-244

: IFAC Workshop on Adaptive and Learning Control Systems 2019 /13./, (Winchester, GB, 20191204)

: LTC18075, GA MŠk, CA16228, EU-COST

: dynamic decision making, Kullback Leibler Divergence, decision strategy, fully probabilistic design, preference elicitation

: 10.1016/j.ifacol.2019.12.656

: http://library.utia.cas.cz/separaty/2019/AS/karny-0519795.pdf

(eng): The paper proposes the preference-elicitation support within the framework of fully probabilistic design (FPD) of decision strategies. Agent employing FPD uses probability densities to model the\nclosed-loop behaviour, i.e. a collection of all observed, opted and considered random variables. Opted actions are generated by a randomised strategy. The optimal decision strategy minimises KullbackLeibler divergence of the closed-loop model to its ideal counterpart describing the agent’s preferences. Thus, selecting the ideal closed-loop model comprises preference elicitation.\nThe paper provides a general choice of the best ideal closed-loop model reflecting agent’s preferences. The foreseen application potential of such a preference elicitation is high as FPD is a non-trivial dense extension of Bayesian decision making that dominates prescriptive decision theories. The general solution is illustrated on the regulation task with a linear Gaussian model describing the agent’s environment.

: BD

: 10103