226 research outputs found
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 membersâasymmetric information makes both equilibrium computation APX-hard and teamâs 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 teamâs 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
Ultra-thin clay layers facilitate seismic slip in carbonate faults
Many earthquakes propagate up to the Earth's surface producing surface ruptures. Seismic slip propagation is facilitated by along-fault low dynamic frictional resistance, which is controlled by a number of physico-chemical lubrication mechanisms. In particular, rotary shear experiments conducted at seismic slip rates (1âms(-1)) show that phyllosilicates can facilitate co-seismic slip along faults during earthquakes. This evidence is crucial for hazard assessment along oceanic subduction zones, where pelagic clays participate in seismic slip propagation. Conversely, the reason why, in continental domains, co-seismic slip along faults can propagate up to the Earth's surface is still poorly understood. We document the occurrence of micrometer-thick phyllosilicate-bearing layers along a carbonate-hosted seismogenic extensional fault in the central Apennines, Italy. Using friction experiments, we demonstrate that, at seismic slip rates (1âms(-1)), similar calcite gouges with pre-existing phyllosilicate-bearing (clay content â€3âwt.%) micro-layers weaken faster than calcite gouges or mixed calcite-phyllosilicate gouges. We thus propose that, within calcite gouge, ultra-low clay content (â€3âwt.%) localized along micrometer-thick layers can facilitate seismic slip propagation during earthquakes in continental domains, possibly enhancing surface displacement
A square-root speedup for finding the smallest eigenvalue
We describe a quantum algorithm for finding the smallest eigenvalue of a
Hermitian matrix. This algorithm combines Quantum Phase Estimation and Quantum
Amplitude Estimation to achieve a quadratic speedup with respect to the best
classical algorithm in terms of matrix dimensionality, i.e.,
black-box queries to an oracle
encoding the matrix, where is the matrix dimension and is the
desired precision. In contrast, the best classical algorithm for the same task
requires queries. In addition, this
algorithm allows the user to select any constant success probability. We also
provide a similar algorithm with the same runtime that allows us to prepare a
quantum state lying mostly in the matrix's low-energy subspace. We implement
simulations of both algorithms and demonstrate their application to problems in
quantum chemistry and materials science.Comment: 17 pages, 6 figures, all comments are welcome, additional references
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Status of the Space Radiation Monte Carlos Simulation Based on FLUKA and ROOT
The NASA-funded project reported on at the first IWSSRR in Arona to develop a Monte-Carlo simulation program for use in simulating the space radiation environment based on the FLUKA and ROOT codes is well into its second year of development, and considerable progress has been made. The general tasks required to achieve the final goals include the addition of heavy-ion interactions into the FLUKA code and the provision of a ROOT-based interface to FLUKA. The most significant progress to date includes the incorporation of the DPMJET event generator code within FLUKA to handle heavy-ion interactions for incident projectile energies greater than 3GeV/A. The ongoing effort intends to extend the treatment of these interactions down to 10 MeV, and at present two alternative approaches are being explored. The ROOT interface is being pursued in conjunction with the CERN LHC ALICE software team through an adaptation of their existing AliROOT software. As a check on the validity of the code, a simulation of the recent data taken by the ATIC experiment is underway
GEANT: detector description and simulation tool
As the scale and complexity of High Energy Physics experiments increase, simulation studies require more and more care and become essential to design and optimise the detectors, develop and test the reconstruction and analysis programs, and interpret the experimental data. GEANT is a system of detector description and simulation tools that help physicists in such studies
BankSealer: An Online Banking Fraud Analysis and Decision Support System
Part 9: Malicious Behavior and FraudInternational audienceWe propose a semi-supervised online banking fraud analysis and decision support approach. During a training phase, it builds a profile for each customer based on past transactions. At runtime, it supports the analyst by ranking unforeseen transactions that deviate from the learned profiles. It uses methods whose output has a immediate statistical meaning that provide the analyst with an easy-to-understand model of each customerâs spending habits. First, we quantify the anomaly of each transaction with respect to the customer historical profile. Second, we find global clusters of customers with similar spending habits. Third, we use a temporal threshold system that measures the anomaly of the current spending pattern of each customer, with respect to his or her past spending behavior. As a result, we mitigate the undertraining due to the lack of historical data for building of well-trained profiles (of fresh users), and the users that change their (spending) habits over time. Our evaluation on real-world data shows that our approach correctly ranks complex frauds as âtop priorityâ
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