Planejamento probabilístico sensível a risco com ILAO* e função utilidade exponencial São Paulo 2019Autorizo a reprodução e divulgação total ou parcial deste trabalho, por qualquer meio convencional ou eletrônico, para fins de estudo e pesquisa, desde que citada a fonte.
Processos de decisão de Markov, sensível a risco, averso a risco, planejamento probabilístico, utilidade exponencial ABSTRACT Markov Decision Process (MDP) has been used very e ciently to solve sequential decision-making problems. There are problems in which dealing with the risks of the environment to obtain a reliable result is more important than maximizing the expected average return. MDPs that deal with this type of problem are called risk-sensitive Markov decision processes (RSMDP). This systematic review of the literature aims to identify the theoretical results and proposed algorithms to solve RSMDP problems that have an exponential utility function, evaluating their main characteristics, similarities, particularities and di↵erences in order Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. SBSI 2017 June 5 th -8 th , 2017, Lavras, Minas Gerais, Brazil Copyright SBC 2017.to allow the reader the knowledge of this tool of decision making for risk sensitive problems.
Markov Decision Process (MDP) has been used very efficiently to solve sequential decision-making problems. However, there are problems in which dealing with the risks of the environment to obtain a reliable result is more important than minimizing the total expected cost. MDPs that deal with this type of problem are called risk-sensitive Markov decision processes (RSMDP). In this paper we propose an efficient heuristic search algorithm that allows to obtain a solution by evaluating only the relevant states to reach the goal states starting from an initial state.
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