文章基本信息

标题：Preference Elicitation within Framework of Fully Probabilistic Design of Decision Strategies ⁎
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作者：Miroslav Kárný ; Tatiana V. Guy
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：29
页码：239-244
DOI：10.1016/j.ifacol.2019.12.656
语种：English
出版社：Elsevier
摘要：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 closed-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 Kullback-Leibler 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.The 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.
关键词：Keywordsdynamic decision makingpreference elicitationfully probabilistic designdecision strategyKullback Leibler Divergence