### Table 2: Classification of online game cheating

"... In PAGE 5: ... Our classification is intentionally reminiscent of the dependability taxonomy provided in [12, 1] and the concep- tual model described in [20]. Table2 shows the details of the taxonomy by vulnerabil- ity, possible failure and exploiter (i.e.... In PAGE 8: ... Furthermore, it is worthwhile asking what implications online cheating has for security in such a representative In- ternet application as online games, since it is well known that security can mean different things in a different con- text. Re-examining the classification by possible failures in Table2 tells us that no matter whether a cheating form results in either information theft, service denial, improper modification of game characteristics, or masquerade, a fair- ness violation in fact will be caused, gaining a cheater some advantages over his peer players in the game. Thus, fairness and its enforcement appear to be a proper perspective for understanding the role of security in applications like online games.... ..."

### Table 5: Algorithm: Fictitious play for two-player, zero-sum stochastic games using a model.

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"... In PAGE 9: ...1 Fictitious Play Fictitious play [16, 25] assumes opponents play stationary strategies. The basic game theory algorithm is shown in Table5 .... ..."

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### Table 5: Algorithm: Fictitious play for two-player, zero-sum stochastic games using a model.

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"... In PAGE 9: ...1 Fictitious Play Fictitious play [16, 25] assumes opponents play stationary strategies. The basic game theory algorithm is shown in Table5 .... ..."

Cited by 17

### Table 5: Algorithm: Fictitious play for two-player, zero-sum stochastic games using a model.

2000

"... In PAGE 9: ...1 Fictitious Play Fictitious play [16, 25] assumes opponents play stationary strategies. The basic game theory algorithm is shown in Table5 .... ..."

Cited by 17

### Table 5: Algorithm: Fictitious play for two-player, zero-sum stochastic games using a model.

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Cited by 17

### Table 5. Algorithm: Fictitious play for two-player, zero-sum stochastic games using a model.

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"... In PAGE 6: ...1 Fictitious Play Fictitious play (Robinson, 1951; Vrieze, 1987) assumes opponents play stationary strategies. The basic game the- ory algorithm is shown in Table5 . The algorithm main- tains information about the average value of each action (i.... ..."

Cited by 17

### Table 5. Algorithm: Fictitious play for two-player, zero-sum stochastic games using a model.

2000

"... In PAGE 6: ...1 Fictitious Play Fictitious play (Robinson, 1951; Vrieze, 1987) assumes opponents play stationary strategies. The basic game the- ory algorithm is shown in Table5 . The algorithm main- tains information about the average value of each action (i.... ..."

Cited by 17

### Table 3. Abalearn 1.1 playing online managed to win intermediate players.

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"... In PAGE 15: ...able 4. Abalearn v.1.2 won a player with ELO 1501. Table3 shows the results of some games played by Abalearn 1.1 online against players of different ELOs.... ..."

Cited by 1

### Table 2: Movements identified during game play

"... In PAGE 5: ... We identified an initial set of movements; these were further checked and performed with the games to ensure that they would drive interaction, eventually settling into a set of seven movements. See Table2 for the seven movements, a description and illustrative examples. Table 2: Movements identified during game play... ..."

### Table 4: Percent of games played with weather information

"... In PAGE 7: ... Table4 summarizes the percent of trades that took place indicating a strong preference to use information despite its seeming lack of usefulness for the bottom line profit. The data reveal a stronger tendency to purchase than to sell information.... In PAGE 7: ... 1998) who found that preference for information does not imply expected utility. The strong preference to purchase information is also reflected in the percent of trades which actually took place as seen in Table4 . We assume a 50% expected percentage for trades.... ..."