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This book explores the combination of Reinforcement Learning and Quantum Computing in the light of complex attacker-defender scenarios. Reinforcement Learning has proven its capabilities in different challenging optimization problems and is now an established method in Operations Research. However, complex attacker-defender scenarios have several characteristics that challenge Reinforcement Learning algorithms, requiring enormous computational power to obtain the optimal solution. The upcoming field of Quantum Computing is a promising path for solving computationally complex problems. Therefore, this work explores a hybrid quantum approach to policy gradient methods in Reinforcement Learning...
This book constitutes the refereed proceedings of the 17th International Conference on Critical Information Infrastructures Security, CRITIS 2022, which took place in Munich, Germany, during September 14–16, 2022. The 16 full papers and 4 short papers included in this volume were carefully reviewed and selected from 26 submissions. They are organized in topical sections as follows: protection of cyber-physical systems and industrial control systems (ICS); C(I)IP organization, (strategic) management and legal aspects; human factor, security awareness and crisis management for C(I)IP and critical services; and future, TechWatch and forecast for C(I)IP and critical services.
The volume LNCS 12296 constitutes the papers of the 17th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research which will be held online in September 2020. The 32 regular papers presented together with 4 abstracts of fast-track papers were carefully reviewed and selected from a total of 72 submissions. Additionally, this volume includes the 4 abstracts and 2 invited papers by plenary speakers. The conference program also included a Master Class on the topic “Recent Advances in Optimization Paradigms and Solving Technology"
This book presents recent findings and results concerning the solutions of especially finite state-space Markov decision problems and determining Nash equilibria for related stochastic games with average and total expected discounted reward payoffs. In addition, it focuses on a new class of stochastic games: stochastic positional games that extend and generalize the classic deterministic positional games. It presents new algorithmic results on the suitable implementation of quasi-monotonic programming techniques. Moreover, the book presents applications of positional games within a class of multi-objective discrete control problems and hierarchical control problems on networks. Given its scope, the book will benefit all researchers and graduate students who are interested in Markov theory, control theory, optimization and games.
Quantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices.
Contains research project reports arranged by subject with descriptors from the EUDISED Multilingual Thesaurus.
A multidisciplinary index covering the journal literature of the arts and humanities. It fully covers 1,144 of the world's leading arts and humanities journals, and it indexes individually selected, relevant items from over 6,800 major science and social science journals.