• Proceedings of the Korean Society of Computational and Applied Mathematics Conference
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• 2003.09a
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• pp.15-15
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• 2003

We provide some recent results of approximation algorithms for solving Markov Games and discuss their applications to problems that arise in Computer Science. We consider a receding horizon approach as an approximate solution to two-person zero-sum Markov games with an infinite horizon discounted cost criterion. We present error bounds from the optimal equilibrium value of the game when both players take “correlated” receding horizon policies that are based on exact or approximate solutions of receding finite horizon subgames. Motivated by the worst-case optimal control of queueing systems by Altman, we then analyze error bounds when the minimizer plays the (approximate) receding horizon control and the maximizer plays the worst case policy. We give two heuristic examples of the approximate receding horizon control. We extend “parallel rollout” and “hindsight optimization” into the Markov game setting within the framework of the approximate receding horizon approach and analyze their performances. From the parallel rollout approach, the minimizing player seeks to combine dynamically multiple heuristic policies in a set to improve the performances of all of the heuristic policies simultaneously under the guess that the maximizing player has chosen a fixed worst-case policy. Given $\varepsilon$>0, we give the value of the receding horizon which guarantees that the parallel rollout policy with the horizon played by the minimizer “dominates” any heuristic policy in the set by $\varepsilon$, From the hindsight optimization approach, the minimizing player makes a decision based on his expected optimal hindsight performance over a finite horizon. We finally discuss practical implementations of the receding horizon approaches via simulation and applications.

• Journal of the Korea Computer Graphics Society
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• v.15 no.2
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• pp.9-17
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• 2009

Animating multiple characters to compete with each other is an important problem in computer games and animation films. However, it remains difficult to simulate strategic competition among characters because of its inherent complex decision process that should be able to cope with often unpredictable behavior of opponents. We adopt a reinforcement learning method in Markov games to action models built from captured motion data. This enables two characters to perform globally optimal counter-strategies with respect to each other. We also extend this method to simulate competition between two teams, each of which can consist of an arbitrary number of characters. We demonstrate the usefulness of our approach through various competitive scenarios, including playing-tag, keeping-distance, and shooting.

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.543-552
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• 2012

Parrondo paradox is a counter-intuitive phenomenon where two losing games can be combined to win or two winning games can be combined to lose. When we trade stocks with a history-dependent Parrondo game rule (where we buy and sell stocks based on recent investment outcomes) we found Parrondo paradox in stock trading. Using stock data of the KRX from 2008 to 2010, we analyzed the Parrondo paradoxical cases in the Korean stock market.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.745-753
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• 2014

Parrondo paradox is the counter-intuitive situation where individually losing games can combine to win or individually winning games can combine to lose. In this paper, we compare the history-dependent Parrondo games and the space-dependent Parrondo games played cooperatively by the multiple players. We show that there is a probability region where the history-dependent Parrondo game is a losing game whereas the space-dependent Parrondo game is a winning game.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.973-982
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• 2009

Two losing games that can be combined, either by periodic alternation or by random mixture, to form a winning game are known as Parrondo games. We consider a collective version of Parrondo games in which players are allowed to choose the game to be played by the whole ensemble in each turn. In this paper, we analyze the long-range optimization strategy for all choices of the parameters and find the expected average profit in the steady state.

• Journal of Korea Game Society
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• v.16 no.2
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• pp.119-128
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• 2016

In computer games, most of GUI methods are keyboards, mouses and touch screens. The total time of processing the sound commands for games is the sum of input time and recognition time. In this paper, we propose a method for taking only the prefixes of the input signals for sound commands, resulting in the reduced the total processing time, instead of taking the whole input signals. In our method, command sounds are recognized using HMM(Hidden Markov Model), where separate HMM's are built for the whole input signals and their prefix signals. We experiment our proposed method with representative commands of platform games. The experiment shows that the total processing time of input command signals reduces without decreasing recognition rate significantly. The study will contribute to enhance the versatility of GUI for computer games.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.867-880
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• 2012

Parrondo paradox is the counter-intuitive situation where individually losing games can combine to win or individually winning games can combine to lose. In this paper, we derive the expected profit per trade for each portfolio when we trade stocks everyday under the spatially dependent Parrondo game rule. Using stock data of KRX (Korea Exchange) from 2008 to 2010, we show that Parrondo paradox exists in the stock trading.

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.121-130
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• 2016

An ansemble of N players arranged in a circle play a spatially dependent Parrondo game B. One player is randomly selected to play game B, which is based on the toss of a biased coin, with the amount of the bias depending on states of the selected player's two nearest neighbors. The player wins one unit with heads and loses one unit with tails. In game A' the randomly chosen player transfers one unit of capital to another player who is randomly chosen among N - 1 players. Game A' is fair with respect to the ensemble's total profit. The games are said to exhibit the Parrondo effect if game B is losing and the random mixture game C is winning and the reverse-Parrondo effect if game B is winning and the random mixture game C is losing. We compute the exact mean profits for games B and C by applying a state space reduction method with lumped Markov chains and we sketch the Parrondo and reverse-Parrondo regions for $3{\leq}N{\leq}6$.

• Journal of Korea Multimedia Society
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• v.12 no.4
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• pp.521-529
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• 2009

In this paper, we present a vision-based human primitive motion recognition method. It models the reference motion patterns, recognizes a user's motion, and measures the similarity between the reference action and the user's one. In order to recognize a motion, we provide a pattern modeling method based on the Hidden Markov Models. In addition, we provide a similarity measurement method between the reference motion and the user's one using the editing distance algorithm. Experimental results show that the recognition rate of ours is above 93%. Our method can be used in the motion recognizable games, the motion recognizable postures, and the rehabilitation training systems.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.77-87
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• 2015

Parrondo paradox is the counter-intuitive phenomenon where two losing games can be combined to win or two winning games can be combined to lose. In this paper, we consider an ensemble of players, one of whom is chosen randomly to play game A' or game B. In game A', the randomly chosen player transfers one unit of his capital to another randomly selected player. In game B, the player plays the history-dependent Parrondo game in which the winning probability of the present trial depends on the results of the last two trials in the past. We show that Parrondo paradox exists in this redistribution model of the history-dependent Parrondo game.