A probabilistic approach to decision-making is developed in which the states of the underlying stochastic process, assumed to be of the Markov type, represent the competing options. The principal parameters determining the dominance of a particular option versus the others are identified and the transduction of information associated to the transitions between states is quantified using a set of entropy-like quantities.SCOPUS: ar.jinfo:eu-repo/semantics/publishe
Abstract. Markov Decision Processes (MDPs) are a well known math-ematical formalism that combines pr...
Abstract. Sensitivity analysis is a general technique for investigating the robust-ness of the outpu...
International audienceThis paper presents an extension to a partially observable Markov decision pro...
(A) Example application of probabilistic decision-making to drive the actions of a synthetic cell in...
In this work, we propose two high-level formalisms, Markov Decision Petri Nets (MDPNs) and Markov De...
In this work, we propose two high-level formalisms, Markov Decision Petri Nets (MDPNs) and Markov De...
In this work, we propose two high-level formalisms, Markov Decision Petri Nets (MDPNs) and Markov De...
AbstractThis paper is about how to represent and solve decision problems in Bayesian decision theory...
The Markov decision process (MDP) (M.L. Puterman, 2005) formalism is widely used for modeling system...
This thesis is about how to represent and solve decision problems in Bayesian decision theory (e.g. ...
The Markov decision process (MDP) (M.L. Puterman, 2005) formalism is widely used for modeling system...
Conventional decision theory suggests that under risk, people choose option(s) by maximizing the exp...
The Markov Decision Process (MDP) formalism is a well-known mathematical formalism to study systems ...
This thesis is about how to represent and solve decision problems in Bayesian decision theory (e.g. ...
This thesis is about how to represent and solve decision problems in Bayesian decision the ory (e.g...
Abstract. Markov Decision Processes (MDPs) are a well known math-ematical formalism that combines pr...
Abstract. Sensitivity analysis is a general technique for investigating the robust-ness of the outpu...
International audienceThis paper presents an extension to a partially observable Markov decision pro...
(A) Example application of probabilistic decision-making to drive the actions of a synthetic cell in...
In this work, we propose two high-level formalisms, Markov Decision Petri Nets (MDPNs) and Markov De...
In this work, we propose two high-level formalisms, Markov Decision Petri Nets (MDPNs) and Markov De...
In this work, we propose two high-level formalisms, Markov Decision Petri Nets (MDPNs) and Markov De...
AbstractThis paper is about how to represent and solve decision problems in Bayesian decision theory...
The Markov decision process (MDP) (M.L. Puterman, 2005) formalism is widely used for modeling system...
This thesis is about how to represent and solve decision problems in Bayesian decision theory (e.g. ...
The Markov decision process (MDP) (M.L. Puterman, 2005) formalism is widely used for modeling system...
Conventional decision theory suggests that under risk, people choose option(s) by maximizing the exp...
The Markov Decision Process (MDP) formalism is a well-known mathematical formalism to study systems ...
This thesis is about how to represent and solve decision problems in Bayesian decision theory (e.g. ...
This thesis is about how to represent and solve decision problems in Bayesian decision the ory (e.g...
Abstract. Markov Decision Processes (MDPs) are a well known math-ematical formalism that combines pr...
Abstract. Sensitivity analysis is a general technique for investigating the robust-ness of the outpu...
International audienceThis paper presents an extension to a partially observable Markov decision pro...