Abstract— This paper proposes a simulation-based active policy learning algorithm for finite-horizon, partially-observed sequential decision processes. The algorithm is tested i...
Ruben Martinez-Cantin, Nando de Freitas, Arnaud Do...
Abstract. Task-based planning problems for multi-agent systems require multiple agents to find a joint plan for a constrained set of tasks. Typically, each agent receives a subset...
J. Renze Steenhuisen, Cees Witteveen, Yingqian Zha...
We develop a hierarchical approach to planning for partially observable Markov decision processes (POMDPs) in which a policy is represented as a hierarchical finite-state control...
The aim of this paper is to describe a generic interface through which a planning tool or design aid can be connected to a telecommunications network. In today's complex and ...
Abstract. In this paper we deal with the problem of finding an optimal query execution plan in database systems. We improve the analysis of a polynomial-time approximation algorit...