— Reinforcement learning (RL) algorithms have long been promising methods for enabling an autonomous robot to improve its behavior on sequential decision-making tasks. The obviou...
Several features such as reconfiguration, voltage and frequency scaling, low-power operating states, duty-cycling, etc. are exploited for latency and energy efficient application ...
A general and expressive model of sequential decision making under uncertainty is provided by the Markov decision processes (MDPs) framework. Complex applications with very large ...
Transactional Coherence and Consistency (TCC) is a novel coherence scheme for shared memory multiprocessors that uses programmer-defined transactions as the fundamental unit of p...
We present a compositional method for the verification of component-based systems described in a subset of the BIP language encompassing multi-party interaction without data transf...
Saddek Bensalem, Marius Bozga, Joseph Sifakis, Tha...