A marriage between adversarial team games and 2-player games: enabling abstractions, no-regret learning, and subgame solving

Ex ante correlation is becoming the mainstream approach for sequential adversarial team games,where a team of players faces another team in a zero-sum game. It is known that team members’asymmetric information makes both equilibrium computation APX-hard and team’s strategies not directly representable on the game tree. This latter issue prevents the adoption of successful tools for huge 2-player zero-sum games such as, e.g., abstractions, no-regret learning, and sub game solving. This work shows that we can re cover from this weakness by bridging the gap be tween sequential adversarial team games and 2-player games. In particular, we propose a new,suitable game representation that we call team public-information, in which a team is repre sented as a single coordinator who only knows information common to the whole team and pre scribes to each member an action for any pos sible private state. The resulting representation is highly explainable, being a 2-player tree in which the team’s strategies are behavioral with a direct interpretation and more expressive than he original extensive form when designing ab stractions. Furthermore, we prove payoff equiva lence of our representation, and we provide tech niques that, starting directly from the extensive form, generate dramatically more compact repre sentations without information loss. Finally, we experimentally evaluate our techniques when ap plied to a standard testbed, comparing their per formance with the current state of the art

Public Information Representation for Adversarial Team Games

The peculiarity of adversarial team games resides in the asymmetric information available to the team members during the play, which makes the equilibrium computation problem hard even with zero-sum payoffs. The algorithms available in the literature work with implicit representations of the strategy space and mainly resort to Linear Programming and column generation techniques to enlarge incrementally the strategy space. Such representations prevent the adoption of standard tools such as abstraction generation, game solving, and subgame solving, which demonstrated to be crucial when solving huge, real-world two-player zero-sum games. Differently from these works, we answer the question of whether there is any suitable game representation enabling the adoption of those tools. In particular, our algorithms convert a sequential team game with adversaries to a classical two-player zero-sum game. In this converted game, the team is transformed into a single coordinator player who only knows information common to the whole team and prescribes to the players an action for any possible private state. Interestingly, we show that our game is more expressive than the original extensive-form game as any state/action abstraction of the extensive-form game can be captured by our representation, while the reverse does not hold. Due to the NP-hard nature of the problem, the resulting Public Team game may be exponentially larger than the original one. To limit this explosion, we provide three algorithms, each returning an information-lossless abstraction that dramatically reduces the size of the tree. These abstractions can be produced without generating the original game tree. Finally, we show the effectiveness of the proposed approach by presenting experimental results on Kuhn and Leduc Poker games, obtained by applying state-of-art algorithms for two-player zero-sum games on the converted gamesComment: 19 pages, 7 figures, Best Paper Award in Cooperative AI Workshop at NeurIPS 202

The Update Equivalence Framework for Decision-Time Planning

The process of revising (or constructing) a policy immediately prior to execution -- known as decision-time planning -- is key to achieving superhuman performance in perfect-information settings like chess and Go. A recent line of work has extended decision-time planning to more general imperfect-information settings, leading to superhuman performance in poker. However, these methods requires considering subgames whose sizes grow quickly in the amount of non-public information, making them unhelpful when the amount of non-public information is large. Motivated by this issue, we introduce an alternative framework for decision-time planning that is not based on subgames but rather on the notion of update equivalence. In this framework, decision-time planning algorithms simulate updates of synchronous learning algorithms. This framework enables us to introduce a new family of principled decision-time planning algorithms that do not rely on public information, opening the door to sound and effective decision-time planning in settings with large amounts of non-public information. In experiments, members of this family produce comparable or superior results compared to state-of-the-art approaches in Hanabi and improve performance in 3x3 Abrupt Dark Hex and Phantom Tic-Tac-Toe

eSports Switzerland 2021 : a study by the Institute of Marketing Management under the direction of Marcel Hüttermann

The eSports market has been growing for several years, with revenues likely to top USD 1 billion this year. This great potential has been recognized by a variety of companies investing in that market, and their interest has also caused eSports to become more professionalized. This is the second of our representative studies on eSports in Switzerland. Like the first, which was published in 2019, it investigates the status quo in e-sports in Switzerland and deals with issues that are relevant to market participants, such as the perceptions of companies in an eSports context, the professionalization of eSports in Switzerland, and the gaming, purchasing, and information-gathering behavior of eSports enthusiasts. In general, the awareness level, popularity, and professional standing of eSports, as well as its acceptance as a sport by the Swiss, have increased considerably: – 43.5 percent of Swiss residents know exactly what eSports is. – 41 percent have stated that, in their opinion, eSports should be considered as a sport. – 79.5 percent recognize that eSports in Switzerland is becoming more professional. – 565,620 Swiss residents are eAthletes (+ 248,530 people compared to 2019). – More than 110,000 succeed in gaining money with esports (+ 67,500 people compared to 2019). – The growth of the eSports phenomenon is sustainable and seems unaffected by the COVID-19 pandemic. Esports is mainly played or watched on PCs and laptops. Having fun is given as the main reason for playing eSports and gaming. According to eSports enthusiasts, eSports and gaming mainly train players’ reaction time. The most popular type of eSports in Switzerland are MOBA (multiplayer online battle arena) games. 49.1 percent of eSports enthusiasts stated that they regularly watch eSports games and tournaments, with the use of streaming platforms increasing significantly compared to 2019. 68.5 percent of respondents watch eSports via streaming platforms such as Twitch, YouTube, or similar providers, which means an increase of 36.3 percent; this is more than twice as much as back in 2019. 18.6 percent of eSports enthusiasts follow individual streamers. Companies are mainly perceived as being involved in streams or as sponsors of live events. Esports players and gamers spend almost CHF 523 a year on their passion, mainly on hardware, games, and fan merchandise. In the future, it will be exciting to see how eSports will develop in Switzerland and what new products and services will be launched. 45.5 percent of all 16- to 29-year-olds are in favor of promoting eSports in clubs, and 32.1 percent of Swiss residents imagine that it will be possible in the long term to find paid work within the eSports industry

Modeling of random magnetization dynamics in nanosystems

Nonlinear magnetization dynamic process in nano-scale magnetic systems is of great scientific interests for its application to magnetic recording technology and spintronic devices. In the dynamic process, thermal fluctuation effects are of critical importance since they are directly related to long term reliability of magnetic devices. Recently, a novel approach to modeling stochastic magnetization dynamics has been proposed[1,2,3]. In this approach, thermal bath effects are accounted for by introducing a jump-noise torque term into the precessional magnetization dynamics equation. In this dissertation, we develop a Monte Carlo type numerical technique for implementation of the approach. There are two central elements of our numerical technique: a ``midpoint'' finite-difference scheme for integration of deterministic precessions and a ``self-scattering'' scheme which results in time-homogenization of a jump-noise process. The numerical technique unconditionally preserves the micromagnetic constraint and appreciably simplifies the random component of Monte Carlo simulations. We perform and illustrate numerous Monte Carlo simulations in the dissertation using numerical examples. The Monte Carlo simulations are ideally suited for implementation on GPUs since they are intrinsically parallelizable in the sense that different realizations of stochastic magnetization dynamics can be computed concurrently. Therefore we develop a parallel algorithm and implement it using an Nvidia GPU card. A speed-up factor of more than 200 is achieved using this GPU implementation in comparison with the tranditional CPU single threaded implementation. Furthermore, we apply the jump-noise process driven magnetization dynamic equation to study random magnetization switching induced by thermal fluctuations. Numerical results demonstrate that the magnetization switching rate has a very different temperature dependence at relatively high and very low temperatures. The high temperature switching conforms to the Arrhenius law of thermal activation, whereas the low temperature switching has many features traditionally attributed to the phenomenon of macroscopic magnetization tunneling. The two temperature dependent regimes emerge directly from the properties of a jump-noise process while no quantum considerations are involved in our approach. Finally, we study the magnetization dynamics at elevated temperatures. We extend the jump-noise process driven magnetization dynamics approach and derive a generalization of the classical Landau-Lifshitz equation to describe magnetization dynamics around Curie temperature where the traditional micromagnetic constraint is not valid. The longitudinal and transverse damping terms in the generalized equation emerge directly from the mathematical structure of a jump-noise process which accounts for interactions with thermal bath

Voices and views : English language issues from different perspectives

The articles in this collection were produced within the framework of the MA program “Maestría en Inglés”, Facultad de Lenguas, Universidad Nacional de Córdoba, Argentina. They are based on selected MA theses and dissertations on Applied Linguistics and Literary Studies. The collection focuses on EFL teacher training, pronunciation assessment, academic writing, Critical Discourse Analysis and Literature