In this work, we introduce a new and efficient solution approach for the problem of decision making under uncertainty, which can be formulated as in belief space, over possibly high-dimensional state space. Typically, to solve problem, one should identify optimal action from set candidates, according some objective. We claim that often generate an analogous yet simplified solved more efficientl...

Abstract A novel idea for an optimal time delay state space reconstruction from uni- and multivariate series is presented. The entire embedding process considered as a game, in which each move corresponds to cycle subject evaluation through objective function. This way the procedure can be modeled tree, leaf holds specific value of By using Monte Carlo ansatz, proposed algorithm populates tree ...

The first question answered in this paper is whether or not learning attention control in the decision space is feasible and how to develop an online as well as interactive learning approach for such control in this space, in case of feasibility. Here, decision space is formed by the decision vector of the agents each has allowed to dynamically observe just a subset of all available sensors. At...

For more than three decades healthcare decentralization has been promoted in developing countries as a way of improving the financing and delivery of public healthcare. Decision autonomy under healthcare decentralization would determine the role and scope of responsibility of local authorities. Jalal Mohammed, Nicola North, and Toni Ashton analyze decision autonomy within decentralized services...



 To enhance the decision support of emergency responders, we are examining the ability of decision space visualization tools to enhance option awareness and support more robust decision making. The current study extends prior work to detail the impact of the decision space information provided to users, relating the correctness of decisions to the levels of complexity represented in the event...

This paper presents a challenge problem for decision-theoretic planners. State-space planners reason globally, building a map of the parts of the world relevant to the planning problem, and then attempt to distill a plan out of the map. A planning problem is constructed that humans find trivial, but no state-space planner can solve. Existing POCL planners cannot solve the problem either, but fo...

We introduce an eecient decision procedure for the theory of equality based on nite instantiations. When using the nite instantiations method, it is a common practice to take a range of 1::n] (where n is the number of input non-Boolean variables) as the range for all non-Boolean variables, resulting in a state-space of n n. Although various attempts to minimize this range were made, typically t...