In this paper, we present a link between preference-based and multiobjective sequential decision-making. While transforming a multiobjective problem to a preference-based one is quite natural, the other direction is a bit less obvious. We present how this transformation (from preferencebased to multiobjective) can be done under the classic condition that preferences over histories can be repres...

Edge Computing is a new computing paradigm that aims to enhance the Quality of Service (QoS) applications running close end users. However, edge nodes can only host subset all available services and collected data due their limited storage processing capacity. As result, management faces multiple challenges. One significant challenge present at especially when demand for them may change over ti...

This survey is focused on certain sequential decision-making problems that involve optimizing over probability functions. We discuss the relevance of these for learning and control. The organized around a framework combines problem formulation set resolution methods. consists an infinite-dimensional optimization problem. methods come from approaches to search optimal solutions in space Through ...

Decision-making often requires retrieval from memory. Drawing on the neural ACT-R theory [Anderson, J. R., Fincham, J. M., Qin, Y., & Stocco, A. A central circuit of the mind. Trends in Cognitive Sciences, 12, 136-143, 2008] and other neural models of memory, we delineated the neural signatures of two fundamental retrieval aspects during decision-making: automatic and controlled activation of m...

Determine a non-myopic solution to the sequential decision making problem of monitoring and optimising a space and time dependent function using a moving sensor. Contributions: Sequential Bayesian Optimisation (SBO) Formulate SBO as a Partially Observed Markov Decision Process (POMDP). Find non-mypic solution for the POMDP analog of SBO using MonteCarlo Tree Search (MCTS) and Upper Confidence B...

(Computer Science—Machine Learning) EFFICIENT APPROXIMATE POLICY ITERATION METHODS FOR SEQUENTIAL DECISION MAKING IN REINFORCEMENT LEARNING

In this paper, we present the use of sequential decision-making process simulations for base agents in our multi-agent based economic landscape (MABEL) model. The sequential decision-making process described here is a data-driven Markov-Decision Problem (MDP) integrated with stochastic properties. Utility acquisition attributes in our model are generated for each time step of the simulation. We...