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Property-Driven Design for Robot Swarms: A Design Method Based on Prescriptive Modeling and Model Checking
Manuele Brambilla, Arne Brutschy, Marco Dorigo, Mauro Birattari
Article No.: 17
In this article, we present property-driven design, a novel top-down design method for robot swarms based on prescriptive modeling and model checking. Traditionally, robot swarms have been developed using a code-and-fix approach: in a bottom-up...
Reinforcement Learning of Informed Initial Policies for Decentralized Planning
Landon Kraemer, Bikramjit Banerjee
Article No.: 18
Decentralized partially observable Markov decision processes (Dec-POMDPs) offer a formal model for planning in cooperative multiagent systems where agents operate with noisy sensors and actuators, as well as local information. Prevalent solution...
João Paiva, Pedro Ruivo, Paolo Romano, Luís Rodrigues
Article No.: 19
This article addresses the problem of self-tuning the data placement in replicated key-value stores. The goal is to automatically optimize replica placement in a way that leverages locality patterns in data accesses, such that internode...
Multiagent Reinforcement Social Learning toward Coordination in Cooperative Multiagent Systems
Jianye Hao, Ho-Fung Leung, Zhong Ming
Article No.: 20
Most previous works on coordination in cooperative multiagent systems study the problem of how two (or more) players can coordinate on Pareto-optimal Nash equilibrium(s) through fixed and repeated interactions in the context of cooperative games....
Distributed Data-Centric Adaptive Sampling for Cyber-Physical Systems
Eun Kyung Lee, Hariharasudhan Viswanathan, Dario Pompili
Article No.: 21
A data-centric joint adaptive sampling and sleep scheduling solution, SILENCE, for autonomic sensor-based systems that monitor and reconstruct physical or environmental phenomena is proposed. Adaptive sampling and sleep scheduling can help realize...