FACTORS AFFECTING CAN THO RESIDENTS’ INTENTION TO VISIT DALAT IN THE COVID-19 ERA

The paper examines the factors affecting the intentions of Can Tho residents to travel to Dalat as the COVID-19 pandemic is gradually brought under control. The data were collected from a survey of 213 Can Tho residents and analyzed with Cronbach’s alpha, exploratory factor analysis, confirmatory factor analysis, and structural equation modeling. The results show that the intentions of Can Tho residents to travel to Dalat depend on four factors: (1) attitude toward Dalat tourism, (2) subjective norms, (3) perceived behavioral controls, and (4) perceived health risk. In particular, the attitudinal factor significantly influences Dalat travel intentions. Based on the research results, the article proposes four recommendations for managers, corresponding to the four influencing factors, to increase the intentions of Can Tho residents to travel to Dalat in the COVID-19 era

Exploiting No-Regret Algorithms in System Design

We investigate a repeated two-player zero-sum game setting where the column player is also a designer of the system, and has full control on the design of the payoff matrix. In addition, the row player uses a no-regret algorithm to efficiently learn how to adapt their strategy to the column player's behaviour over time in order to achieve good total payoff. The goal of the column player is to guide her opponent to pick a mixed strategy which is favourable for the system designer. Therefore, she needs to: (i) design an appropriate payoff matrix $A$ whose unique minimax solution contains the desired mixed strategy of the row player; and (ii) strategically interact with the row player during a sequence of plays in order to guide her opponent to converge to that desired behaviour. To design such a payoff matrix, we propose a novel solution that provably has a unique minimax solution with the desired behaviour. We also investigate a relaxation of this problem where uniqueness is not required, but all the minimax solutions have the same mixed strategy for the row player. Finally, we propose a new game playing algorithm for the system designer and prove that it can guide the row player, who may play a \emph{stable} no-regret algorithm, to converge to a minimax solution

Regret-Minimizing Double Oracle for Extensive-Form Games

By incorporating regret minimization, double oracle methods have demonstrated rapid convergence to Nash Equilibrium (NE) in normal-form games and extensive-form games, through algorithms such as online double oracle (ODO) and extensive-form double oracle (XDO), respectively. In this study, we further examine the theoretical convergence rate and sample complexity of such regret minimization-based double oracle methods, utilizing a unified framework called Regret-Minimizing Double Oracle. Based on this framework, we extend ODO to extensive-form games and determine its sample complexity. Moreover, we demonstrate that the sample complexity of XDO can be exponential in the number of information sets $|S|$, owing to the exponentially decaying stopping threshold of restricted games. To solve this problem, we propose the Periodic Double Oracle (PDO) method, which has the lowest sample complexity among regret minimization-based double oracle methods, being only polynomial in $|S|$. Empirical evaluations on multiple poker and board games show that PDO achieves significantly faster convergence than previous double oracle algorithms and reaches a competitive level with state-of-the-art regret minimization methods.Comment: Accepted at ICML, 202

Achieving Better Regret against Strategic Adversaries

We study online learning problems in which the learner has extra knowledge about the adversary's behaviour, i.e., in game-theoretic settings where opponents typically follow some no-external regret learning algorithms. Under this assumption, we propose two new online learning algorithms, Accurate Follow the Regularized Leader (AFTRL) and Prod-Best Response (Prod-BR), that intensively exploit this extra knowledge while maintaining the no-regret property in the worst-case scenario of having inaccurate extra information. Specifically, AFTRL achieves $O(1)$ external regret or $O(1)$ \emph{forward regret} against no-external regret adversary in comparison with $O(\sqrt{T})$ \emph{dynamic regret} of Prod-BR. To the best of our knowledge, our algorithm is the first to consider forward regret that achieves $O(1)$ regret against strategic adversaries. When playing zero-sum games with Accurate Multiplicative Weights Update (AMWU), a special case of AFTRL, we achieve \emph{last round convergence} to the Nash Equilibrium. We also provide numerical experiments to further support our theoretical results. In particular, we demonstrate that our methods achieve significantly better regret bounds and rate of last round convergence, compared to the state of the art (e.g., Multiplicative Weights Update (MWU) and its optimistic counterpart, OMWU)

Last Round Convergence and No-Instant Regret in Repeated Games with Asymmetric Information

This paper considers repeated games in which one player has more information about the game than the other players. In particular, we investigate repeated two-player zero-sum games where only the column player knows the payoff matrix A of the game. Suppose that while repeatedly playing this game, the row player chooses her strategy at each round by using a no-regret algorithm to minimize her (pseudo) regret. We develop a no-instant-regret algorithm for the column player to exhibit last round convergence to a minimax equilibrium. We show that our algorithm is efficient against a large set of popular no-regret algorithms of the row player, including the multiplicative weight update algorithm, the online mirror descent method/follow-the-regularized-leader, the linear multiplicative weight update algorithm, and the optimistic multiplicative weight update

Investigate the Structural Response of Ultra High Performance Concrete Column under the High Explosion

Most of the structures that are damaged by an explosion are not initially designed to resist this kind of load. In the overall structure of any building, columns play an important role to prevent the collapse of frame structure under blast impact. Hence, the main concept in the blast resistance design of the structure is to improve the blast load capacity of the column. In this study, dynamic analysis and numerical model of Ultra High Performance Concrete (UHPC) column under high explosive load, is presented. Based on the Johnson Holmquist 2 damage model and the subroutine in the ABAQUS platform, a total of twenty UHPC model of the column were calculated. The objective of the article is to investigate the structural response of the UHPC column and locate the most vulnerable scenarios to propose necessary recommendations for the UHPC column in the blast loading resistance design. The input parameters, including the effect of various shapes of cross-section, scaled distance, steel reinforcement ratio, and cross-section area, are analyzed to clarify the dynamic behavior of the UHPC column subjected to blast loading. Details of the numerical data, and the discussion on the important obtained results, are also provided in this paper

How to guide a non-cooperative learner to cooperate : exploiting no-regret algorithms in system design

We investigate a repeated two-player game setting where the column player is also a designer of the system, and has full control over payoff matrices. In addition, we assume that the row player uses a no-regret algorithm to efficiently learn how to adapt their strategy to the column player's behaviour over time. The goal of the column player is to guide her opponent into picking a mixed strategy which is preferred by the system designer. Therefore, she needs to: (i) design appropriate payoffs for both players; and (ii) strategically interact with the row player during a sequence of plays in order to guide her opponent to converge to the desired mixed strategy. To design appropriate payoffs, we propose a novel zero-sum game construction whose unique minimax solution contains the desired behaviour. We also propose another construction in which only the minimax strategy of the row player is unique. Finally, we propose a new game playing algorithm for the system designer and show that it can guide the row player to its minimax strategy, under the assumption that the row player adopts a stable no-regret algorithm

DISTRIBUTION OF USEFUL AND HARMFUL MICROORGANISMS IN SHRIMP AQUACULTURE WATER IN TIEN HAI COASTAL OF THAI BINH PROVINCE

Joint Research on Environmental Science and Technology for the Eart