Modelling, safety verification and design of discrete/continuous processing systems.

Imperial Users onl

LIPIcs, Volume 258, SoCG 2023, Complete Volume

LIPIcs, Volume 258, SoCG 2023, Complete Volum

Modeling with renormalization group and randomization.

Ph.DDOCTOR OF PHILOSOPH

Proceedings of the XIII Global Optimization Workshop: GOW'16

[Excerpt] Preface: Past Global Optimization Workshop shave been held in Sopron (1985 and 1990), Szeged (WGO, 1995), Florence (GO’99, 1999), Hanmer Springs (Let’s GO, 2001), Santorini (Frontiers in GO, 2003), San José (Go’05, 2005), Mykonos (AGO’07, 2007), Skukuza (SAGO’08, 2008), Toulouse (TOGO’10, 2010), Natal (NAGO’12, 2012) and Málaga (MAGO’14, 2014) with the aim of stimulating discussion between senior and junior researchers on the topic of Global Optimization. In 2016, the XIII Global Optimization Workshop (GOW’16) takes place in Braga and is organized by three researchers from the University of Minho. Two of them belong to the Systems Engineering and Operational Research Group from the Algoritmi Research Centre and the other to the Statistics, Applied Probability and Operational Research Group from the Centre of Mathematics. The event received more than 50 submissions from 15 countries from Europe, South America and North America. We want to express our gratitude to the invited speaker Panos Pardalos for accepting the invitation and sharing his expertise, helping us to meet the workshop objectives. GOW’16 would not have been possible without the valuable contribution from the authors and the International Scientific Committee members. We thank you all. This proceedings book intends to present an overview of the topics that will be addressed in the workshop with the goal of contributing to interesting and fruitful discussions between the authors and participants. After the event, high quality papers can be submitted to a special issue of the Journal of Global Optimization dedicated to the workshop. [...

Formal verification of deep reinforcement learning agents

Deep reinforcement learning has been successfully applied to many control tasks, but the application of such controllers in safety-critical scenarios has been limited due to safety concerns. Rigorous testing of these controllers is challenging, particularly when they operate in uncertain environments. In this thesis we develop novel verification techniques to give the user stronger guarantees over the performance of the trained agents that they would be able to obtain by testing, under different degrees and sources of uncertainty. In particular, we tackle three different sources of uncertainty to the agent and offer different algorithms to provide strong guarantees to the user. The first one is input noise: sensors in the real world always provide imperfect data. The second source of uncertainty comes from the actuators: once an agent decides to take a specific action, faulty actuators and or hardware problems could still prevent the agent from acting upon the decisions given by the controller. The last source of uncertainty is the policy: the set of decisions the controller takes when operating in the environment. Agents may act probabilistically for a number of reasons, such as dealing with adversaries in a competitive environment or addressing partial observability of the environment. In this thesis, we develop formal models of controllers executing under uncertainty, and propose new verification techniques based on abstract interpretation for their analysis. We cover different horizon lengths, i.e., the number of steps into the future that we analyse, and present methods for both finite-horizon and infinite-horizon verification. We perform both probabilistic and non-probabilistic analysis of the models constructed, depending on the methodology adopted. We implement and evaluate our methods on controllers trained for several benchmark control problems

LIPIcs, Volume 261, ICALP 2023, Complete Volume

LIPIcs, Volume 261, ICALP 2023, Complete Volum