Interactive Small-Step Algorithms I: Axiomatization

In earlier work, the Abstract State Machine Thesis -- that arbitrary algorithms are behaviorally equivalent to abstract state machines -- was established for several classes of algorithms, including ordinary, interactive, small-step algorithms. This was accomplished on the basis of axiomatizations of these classes of algorithms. Here we extend the axiomatization and, in a companion paper, the proof, to cover interactive small-step algorithms that are not necessarily ordinary. This means that the algorithms (1) can complete a step without necessarily waiting for replies to all queries from that step and (2) can use not only the environment's replies but also the order in which the replies were received

Leveraging deep reinforcement learning in the smart grid environment

L’apprentissage statistique moderne démontre des résultats impressionnants, où les or- dinateurs viennent à atteindre ou même à excéder les standards humains dans certaines applications telles que la vision par ordinateur ou les jeux de stratégie. Pourtant, malgré ces avancées, force est de constater que les applications fiables en déploiement en sont encore à leur état embryonnaire en comparaison aux opportunités qu’elles pourraient apporter. C’est dans cette perspective, avec une emphase mise sur la théorie de décision séquentielle et sur les recherches récentes en apprentissage automatique, que nous démontrons l’applica- tion efficace de ces méthodes sur des cas liés au réseau électrique et à l’optimisation de ses acteurs. Nous considérons ainsi des instances impliquant des unités d’emmagasinement éner- gétique ou des voitures électriques, jusqu’aux contrôles thermiques des bâtiments intelligents. Nous concluons finalement en introduisant une nouvelle approche hybride qui combine les performances modernes de l’apprentissage profond et de l’apprentissage par renforcement au cadre d’application éprouvé de la recherche opérationnelle classique, dans le but de faciliter l’intégration de nouvelles méthodes d’apprentissage statistique sur différentes applications concrètes.While modern statistical learning is achieving impressive results, as computers start exceeding human baselines in some applications like computer vision, or even beating pro- fessional human players at strategy games without any prior knowledge, reliable deployed applications are still in their infancy compared to what these new opportunities could fathom. In this perspective, with a keen focus on sequential decision theory and recent statistical learning research, we demonstrate efficient application of such methods on instances involving the energy grid and the optimization of its actors, from energy storage and electric cars to smart buildings and thermal controls. We conclude by introducing a new hybrid approach combining the modern performance of deep learning and reinforcement learning with the proven application framework of operations research, in the objective of facilitating seamlessly the integration of new statistical learning-oriented methodologies in concrete applications

Automated decision making and problem solving. Volume 2: Conference presentations

Related topics in artificial intelligence, operations research, and control theory are explored. Existing techniques are assessed and trends of development are determined

Equilibrium Bounded Bargaining

We study one-sided offers bargaining game g where players cannot commit Þnalizing the trade. The game never ends but implements terms of trade iff there is stage k from which on players agree on these terms. Otherwise, no-trade results. We show that equilibria induced by g are equivalent to equilibrium bounded bargaining schemes that are robust against bound extensions. Game g has many sequential equilibria. We give the seller a leading role in equilibrium selection. The seller is allowed to swift from one equilibrium to another iff this is dynamically consistent. We show that the set of admissible equilibria, or stable set, is unique. Under typical conditions, equilibrium in a stable set allocates the good to the buyer with price equal to the buyer’s least valuation in the belief closed set of valuations. Thus higher order uncertainty does not counterbalance the situation in favor of the seller

The Second NASA Formal Methods Workshop 1992

The primary goal of the workshop was to bring together formal methods researchers and aerospace industry engineers to investigate new opportunities for applying formal methods to aerospace problems. The first part of the workshop was tutorial in nature. The second part of the workshop explored the potential of formal methods to address current aerospace design and verification problems. The third part of the workshop involved on-line demonstrations of state-of-the-art formal verification tools. Also, a detailed survey was filled in by the attendees; the results of the survey are compiled

Estimating Production Functions with Robustness Against Errors in the Proxy Variables

This paper proposes a new semi-nonparametric maximum likelihood estimation method for estimating production functions. The method extends the literature on structural estimation of production functions, started by the seminal work of Olley and Pakes (1996), by relaxing the scalar-unobservable assumption about the proxy variables. The key additional assumption needed in the identification argument is the existence of two conditionally independent proxy variables. The assumption seems reasonable in many important cases. The new method is straightforward to apply, and a consistent estimate of the asymptotic covariance matrix of the structural parameters can be easily computed.

Global homotopy theory

This book introduces a new context for global homotopy theory, i.e., equivariant homotopy theory with universal symmetries. Many important equivariant theories naturally exist not just for a particular group, but in a uniform way for all groups in a specific class. Prominent examples are equivariant stable homotopy, equivariant $K$-theory or equivariant bordism. Global equivariant homotopy theory studies such uniform phenomena, i.e., the adjective `global' refers to simultaneous and compatible actions of all compact Lie groups. We give a self-contained treatment of unstable and stable global homotopy theory, modeled by orthogonal spaces respectively orthogonal spectra under global equivalences. Specific topics include the global stable homotopy category, operations on equivariant homotopy groups, global model structures, and ultra-commutative multiplications. The book includes many explicit examples and detailed calculations