The Effectiveness of Using a Modified “Beat Frequent Pick” Algorithm in the First International RoShamBo Tournament

In this study, a bot is developed to compete in the first International RoShamBo Tournament test suite. The basic “Beat Frequent Pick (BFP)” algorithm was taken from the supplied test suite and was improved by adding a random choice tailored fit against the opponent\u27s distribution of picks. A training program was also developed that finds the best performing bot variant by changing the bot\u27s behavior in terms of the timing of the recomputation of the pick distribution. Simulation results demonstrate the significantly improved performance of the proposed variant over the original BFP. This indicates the potential of using the core technique (of the proposed variant) as an Artificial Intelligence bot to similarly applicable computer games

Volume 53 - Issue 18 - Monday, February 12, 2018

The Rose Thorn, Rose-Hulman\u27s independent student newspaper.https://scholar.rose-hulman.edu/rosethorn/2185/thumbnail.jp

Quality is not strategy : Nash equilibrium and international market entry

Version of RecordA recent Harvard Business Review article by Suarez and Lanzolla (2001) entitled the Half-truth of First Mover Advantage argued that this is a business concept which has so much intuitive appeal that its validity is almost taken for granted. In the following paper, we illustrate how typical applications of game theory to describe first mover advantage in the context of international markets are generally set up use an improper theoretical framework and compare incommensurable qualities and quantities. We then review the work of Porter (1996) and others with respect to sustainable competitive advantage and suggest that the Nash equilibrium may provide some guidance as to the kinds of circumstances in which a profitable first mover advantage may or may not be obtainable when entering international markets.Vos Fellman, P., Nugent N., Vos Post, J., & Doyon, D. (2007, October). Quality is not strategy : Nash equilibrium and international market entry. Presented at the Academy of International Business U.S. Northeast Chapter Regional Meeting, Portsmouth, New Hampshire. Retrieved from http://academicarchive.snhu.ed

Enhancing Artificial Intelligence on a Real Mobile Game

Mobile games represent a killer application that is attracting millions of subscribers worldwide. One of the aspects crucial to the commercial success of a game is ensuring an appropriately challenging artificial intelligence (AI) algorithm against which to play. However, creating this component is particularly complex as classic search AI algorithms cannot be employed by limited devices such as mobile phones or, even on more powerful computers, when considering imperfect information games (i.e., games in which participants do not a complete knowledge of the game state at any moment). In this paper, we propose to solve this issue by resorting to a machine learning algorithm which uses profiling functionalities in order to infer the missing information, thus making the AI able to efficiently adapt its strategies to the human opponent. We studied a simple and computationally light machine learning method that can be employed with success, enabling AI improvements for imperfect information games even on mobile phones. We created a mobile phone-based version of a game calledGhostsand present results which clearly show the ability of our algorithm to quickly improve its own predictive performance as far as the number of games against the same human opponent increases

Cedars, May 27, 1994

https://digitalcommons.cedarville.edu/cedars/1678/thumbnail.jp

Agent Modeling in Stochastic Repeated Games

There are many situations in which two or more agents (e.g., human or computer decision makers) interact with each other repeatedly in settings that can be modeled as repeated games. In such situations, there is evidence that agents sometimes deviate greatly from what conventional game theory would predict. There are several reasons why this might happen, one of which is the focus of this dissertation: sometimes an agent's preferences may involve not only its own payoff (as specified in the payoff matrix), but also the payoffs of the other agent(s). In such situations, it is important to be able to understand what an agent's preferences really are, and how those preferences may affect the agent's behavior. This dissertation studies how the notion of Social Value Orientation (SVO), a construct in social psychology to model and measure a person's social preference, can be used to improve our understanding of the behavior of computer agents. Most of the work involves the life game, a repeated game in which the stage game is chosen stochastically at each iteration. The work includes the following results: * Using a combination of the SVO theory and evolutionary game theory, we studied how an agent's SVO affects its behavior in Iterated Prisoner's Dilemma (IPD). Our analysis provides a way to predict outcomes of agents playing IPD given their SVO values. * In the life game, we developed a way to build a model of agent's SVO based on observations of its behavior. Experimental results demonstrate that the modeling technique works well. * We performed experiments showing that the measured social preferences of computer agents have significant correlation with that of their human designers. The experimental results also show that knowing the SVO of an agent's human designer can be used to improve the performance of other agents that interact with the given agent. * A limitation of the SVO model is that it only looks at agents' preferences in one-shot games. This is inadequate for repeated games, in which an agent's actions may depend on both its SVO and whatever predictions it makes of the other agent's behavior. We have developed an extension of the SVO construct called the behavioral signature, a model of how an agent's behavior over time will be affected by both its own SVO and the other agent's SVO. The experimental results show that the behavioral signature is an effective way to generalize SVO to situations where agents interact repeatedly

Bayesian opponent modeling in adversarial game environments.

This thesis investigates the use of Bayesian analysis upon an opponent¿s behaviour in order to determine the desired goals or strategy used by a given adversary. A terrain analysis approach utilising the A* algorithm is investigated, where a probability distribution between discrete behaviours of an opponent relative to a set of possible goals is generated. The Bayesian analysis of agent behaviour accurately determines the intended goal of an opponent agent, even when the opponent¿s actions are altered randomly. The environment of Poker is introduced and abstracted for ease of analysis. Bayes¿ theorem is used to generate an effective opponent model, categorizing behaviour according to its similarity with known styles of opponent. The accuracy of Bayes¿ rule yields a notable improvement in the performance of an agent once an opponent¿s style is understood. A hybrid of the Bayesian style predictor and a neuroevolutionary approach is shown to lead to effective dynamic play, in comparison to agents that do not use an opponent model. The use of recurrence in evolved networks is also shown to improve the performance and generalizability of an agent in a multiplayer environment. These strategies are then employed in the full-scale environment of Texas Hold¿em, where a betting round-based approach proves useful in determining and counteracting an opponent¿s play. It is shown that the use of opponent models, with the adaptive benefits of neuroevolution aid the performance of an agent, even when the behaviour of an opponent does not necessarily fit within the strict definitions of opponent ¿style¿.Engineering and Physical Sciences Research Council (EPSRC

Synthesis of Strategies for Non-Zero-Sum Repeated Games

There are numerous applications that involve two or more self-interested autonomous agents that repeatedly interact with each other in order to achieve a goal or maximize their utilities. This dissertation focuses on the problem of how to identify and exploit useful structures in agents' behavior for the construction of good strategies for agents in multi-agent environments, particularly non-zero-sum repeated games. This dissertation makes four contributions to the study of this problem. First, this thesis describes a way to take a set of interaction traces produced by different pairs of players in a two-player repeated game, and then find the best way to combine them into a strategy. The strategy can then be incorporated into an existing agent, as an enhancement of the agent's original strategy. In cross-validated experiments involving 126 agents for the Iterated Prisoner's Dilemma, Iterated Chicken Game, and Iterated Battle of the Sexes, my technique was able to make improvement to the performance of nearly all of the agents. Second, this thesis investigates the issue of uncertainty about goals when a goal-based agent situated in a nondeterministic environment. The results of this investigation include the necessary and sufficiency conditions for such guarantee, and an algorithm for synthesizing a strategy from interaction traces that maximizes the probability of success of an agent even when no strategy can assure the success of the agent. Third, this thesis introduces a technique, Symbolic Noise Detection (SND), for detecting noise (i.e., mistakes or miscommunications) among agents in repeated games. The idea is that if we can build a model of the other agent's behavior, we can use this model to detect and correct actions that have been affected by noise. In the 20th Anniversary Iterated Prisoner's Dilemma competition, the SND agent placed third in the "noise" category, and was the best performer among programs that had no "slave" programs feeding points to them. Fourth, the thesis presents a generalization of SND that can be wrapped around any existing strategy. Finally, the thesis includes a general framework for synthesizing strategies from experience for repeated games in both noisy and noisy-free environments