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WO2006036100A1 - Evaluation d'un eventuel comportement frauduleux dans le cadre de jeux en ligne - Google Patents

Evaluation d'un eventuel comportement frauduleux dans le cadre de jeux en ligne Download PDF

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Publication number
WO2006036100A1
WO2006036100A1 PCT/SE2005/001351 SE2005001351W WO2006036100A1 WO 2006036100 A1 WO2006036100 A1 WO 2006036100A1 SE 2005001351 W SE2005001351 W SE 2005001351W WO 2006036100 A1 WO2006036100 A1 WO 2006036100A1
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WIPO (PCT)
Prior art keywords
array
player
play
agent
game
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PCT/SE2005/001351
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English (en)
Inventor
Björn Sperber
Christian Erlandsson
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Ongame e Solutions AB
Bwin Games AB
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Ongame e Solutions AB
Bwin Games AB
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Publication of WO2006036100A1 publication Critical patent/WO2006036100A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07F—COIN-FREED OR LIKE APPARATUS
    • G07F17/00—Coin-freed apparatus for hiring articles; Coin-freed facilities or services
    • G07F17/32—Coin-freed apparatus for hiring articles; Coin-freed facilities or services for games, toys, sports, or amusements
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07F—COIN-FREED OR LIKE APPARATUS
    • G07F17/00—Coin-freed apparatus for hiring articles; Coin-freed facilities or services
    • G07F17/32—Coin-freed apparatus for hiring articles; Coin-freed facilities or services for games, toys, sports, or amusements
    • G07F17/3225—Data transfer within a gaming system, e.g. data sent between gaming machines and users
    • G07F17/3232—Data transfer within a gaming system, e.g. data sent between gaming machines and users wherein the operator is informed
    • G07F17/3237—Data transfer within a gaming system, e.g. data sent between gaming machines and users wherein the operator is informed about the players, e.g. profiling, responsible gaming, strategy/behavior of players, location of players
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07F—COIN-FREED OR LIKE APPARATUS
    • G07F17/00—Coin-freed apparatus for hiring articles; Coin-freed facilities or services
    • G07F17/32—Coin-freed apparatus for hiring articles; Coin-freed facilities or services for games, toys, sports, or amusements
    • G07F17/3241—Security aspects of a gaming system, e.g. detecting cheating, device integrity, surveillance

Definitions

  • the present invention per ⁇ ins to a system and method for evaluation of fraud behaviour in games with bets including valuables online between at least two parties through comparing player actions during a game state with the corresponding actions of a norm player agent for said state said agent being trained in norm playing tactics for a specific game type by analyzing earlier stored games of said type through at least one neural network
  • Dumping in Internet poker is also sometimes used by a . player as a method for managing multiple own accounts and it can also be utilized as a tool for settling debts between associates.
  • Collusion is another type of fraudulent behaviour and involves that gamblers use the greater opportunities for undetected co-operation, which is enabled through Internet casinos in comparison to real casinos.
  • Two or more players at a poker table are able to exchange information about their hands of cards without their honest opponents being aware of this. Such information could be used for determining the worth of a hand of cards and to help raising the bets to high levels.
  • a problem with this system is that it is built on observations on a number of fraudster attributes, which in their design are both partial and static.
  • a system built on a momentary image of the dumping character involves the risk that future fraudsters will act different to a degree not fitting within the profile of the system.
  • the present invention relates to a system and a method for evaluation of fraud behaviour in games with bets including valuables between at least two parties online an Internet casino.
  • a virtual norm player model or agent is established for a certain type of game by analyzing the actions or moves performed by players at given game states in a multitude of previously played games of the same type, and Jhe agent is utilized in the system for comparing player actions at given states during a game with the corresponding actions according to the norm player agent as derived empirically for these states.
  • the resulting difference for each action is added and normalised over all actions in the game to form a basis for evaluating and classifying the behaviour of the players graphically as normal or conventional or fraudulent depending on the degree of divergence from preset threshold values for norm piay according to the agent, after the game has been completed.
  • One object of the invention is to provide such a system and method, enabling an automatic evaluation and visualization of fraud behaviour in online gambling including bets, which is easily comprehensible by an observer.
  • the present invention provides a system for evaluation of possible fraud behavior in games with bets including valuables.
  • the system comprises: a polling means, for fetching a plurality of input attributes for a specific state in a game between at least two players, the state demanding a decision from the players, and storing the attributes and decisions taken in a play array for each player; a predetermined agent array having attributes closest to represent the specific state for each player and a conventional decision taken for the specific state for each player; a map array, the map array having a set of possible agent arrays for specific state arrays as attributes, subdividing the set in at least two subsets; each subset determining a class for the play arrays at least one subset classifying a play array as conventional behavior and at least one subset classifying a play array as fraud behavior; a comparator, for determining a difference between the play array and the agent array for each player, the difference being mapped on the map array and being , positioned within one subset; and each player decision being classified in a sub
  • the game is an online poker card game.
  • the input attributes comprise at least one of card data, betting round, position from dealer and earlier player moves.
  • the map array is a ⁇ pre-trained SOFM (Self Organized Feature Map) prepared with regions for classifying the behavior of said player, wherein at least one region corresponds to said agent array.
  • the agent array is periodically updated, through the polling means, with the most recently finished games fitting within, the frame of normal behavior for an iterative training of the agent array in norm playing tactics.
  • a further embodiment of the invention defines that the map array is displayed on a digital screen with said regions visualized discrete.
  • the agent array comprises at least one neural network trained on multiple previously played games through error back propagation.
  • An additional embodiment of the invention defines that a first, a second and a third mutually separate and structurally identical neural network of the kind fully connected, feed forward, multilayer perceptron is used for said agent array, each outputting a respective signal response corresponding to a degree of probability for fold, check/call and bet/raise respectively for a given state in the game, wherein the strongest signal of the three determines the move of the array for said state.
  • the difference for each game action is added and normalised over all actions in the game to form a basis for evaluating and classifying the behaviour of the players as normal or fraudulent depending on the degree of divergence from preset threshold values for norm play according to said agent array.
  • a still further embodiment of the invention defines that at least one of a number of further game attributes including at least economic net, number of played hands and number of highly divergent hands are considered in the evaluation.
  • the present invention sets forth a method for evaluation of possible fraud behavior in network games with bets including valuables. It comprises the steps of: fetching a plurality of input attributes for a specific state in a game between at least two players, said state demanding a decision from said players, and storing said attributes and decisions taken in a play array for each player; providing a predetermined agent array having attributes closest to represent said specific state for each player and a conventional decision taken for said specific state for each player; providing a map array, said map array having a set of possible agent arrays for specific state arrays as attributes, subdividing said set in at least to subsets; each subset determining a class for said play arrays at least one subset classifying a play array as conventional behavior and at least one subset classifying a play array as fraud behavior; determining a difference between said play array and said agent array for each player, the difference being mapped on the map array and being positioned within one subset; and each player decision being classified in a subset during play, determining an
  • the method of the present invention is able to perform method steps of the above network embodiments in accordance with attached method sub-claims.
  • FIG. 1 schematically depicts the distribution and names of the cards in a game of
  • Fig. 2 schematically illustrates a player agent or model with separated state information, according to a preferred embodiment of the present invention.
  • Fig. 3 schematically illustrates a player agent or model with amalgamated state information.
  • Fig. 4 schematically illustrates a Self Organized Feature Map (SOFM) having predefined regions corresponding to normal and abnormal behaviour for player hands in a game of poker;
  • SOFM Self Organized Feature Map
  • Fig. 5 illustrates a deployment of a system for evaluation of fraud behaviour in online games with bets between at least two parties.
  • Fig. 6 depicts a flowchart of system events for evaluation of fraud behaviour in online games with bets between at least two parties, according to one embodiment of the invention.
  • a neura! network is a modeling technique based on the observed behavior of biological neurons and is used to mimic the performance of a system. In the context of this application it consists of a set of neurons, whose weights start out initialized with random values, and which, based upon operational feedback, are iteratively adjusted towards the values required to generate the required results.
  • Multi Layer Perceptrons and Self Organizing Feature Maps are two different types of neural networks used in the present invention.
  • SOFM Self Organized Feature Map
  • a SOFM is usually utilized for dividing a set of data into .separate classes and to describe how these classes relate to each other.
  • a SOFM is instead utilized to force new data into an existing class and to describe graphically how well the new data vector fits in the class.
  • a perceptro ⁇ model is built on an amount of in signals which, together with a weight for each in signal, are added together and transformed to an out signal through a transition function, for example through a formula such as:
  • x is a vector with in signals X 0 ... x n
  • w is a vector with weights W 0 ... W n
  • y is the out signal from a perceptron computed through the function f(x).
  • Table 1 schematically illustrates a card state representation for a deck of 52 cards having 104 values (1 or 0) in a game of Texas Hold ' em.
  • Table 2 shows a card state vector for two closed cards in a poker game of Texas Hold ' em.
  • Table 3 shows a card state vector for 5 open cards in a Texas Hold ' em game, according to one embodiment of the invention.
  • Table 4 schematically illustrates a player state representation for a game of Texas Hold ' em according to the invention.
  • Table 5 shows a first betting round in a game of poker between two people, also called heads up.
  • the present invention sets forth a system and a method for evaluation of possible fraud behavior in online games, such as internet poker games and the like card games, between at least two parties with bets including valuables.
  • a means for assessing the probability or normality of the actions or moves performed by a player during a game is needed.
  • a machine such as a computer or computerized system
  • a model for probable or normal poker play is required, preferably in a piece of software, hardware components or a combination thereof.
  • the model should assess the probability of each possible move.
  • a neural network model based on a single layer of perceptrons is only able to approximate simple functions and to classify linearly separable sets of data. Higher complexity approximation requires that the out signals from a row of perceptrons are forwarded as in signals to further rows of perceptrons.
  • Such a neural network model called a Multi Layer Perceptron model, can be extended to comprise an arbitrary number of layers with the capacity to approximate arbitrary functions. Classification of, problems which are not linearly separable also becomes possible with this model type.
  • a player agent or agent array for probable poker play and specifically for a certain type of poker game called Texas Hold ' em, is built based on database stored data of a multitude, perhaps as much as millions, of previously played hands of that game type. This enables building a player agent or agent array describing a norm player.
  • the States and corresponding poker moves of the stored games are extracted or polled from the database, for empirical neural network training of a set of multilayer perceptrons. Each state is trained versus a known target value for each individual set of multilayer perceptrons, according to the well known supervised learning technique.
  • the player agent or agent, array can alternatively be directed at describing the optimal player, which theoretically is accomplished through neural network training based on rewards instead of on previously played games, for example by utilizing the well known TD ( ⁇ )-algorithm for reinforcement learning.
  • the objective of the player agent trained through reinforcement learning can be directed towards maximizing the profit amount in a game, since most players are ⁇ usually interested in winning large sums of money.
  • the training could be directed at maximizing the frequency of wins.
  • the training is preferably performed utilizing a variant of an algorithm, well known for this purpose, called error .backpropagation, but other the like algorithms for training multilayer perceptrons can be used just as well.
  • Backpropagation employs the just as well known delta rule connected in series from the last layer to the first. The delta rule enables training a perceptron to produce a certain out signal responding to a set of in signals and thus the perceptron functions as a classifier.
  • a non-linear transition function such as the logistic sigmoid function
  • the logistic sigmoid function which is continuous, has an out domain which is limited between 0 and 1 and is well suited for use in both hidden, i.e. in-between layers, and out signal layers.
  • Texas Hold ' em Since the type of poker game called Texas Hold ' em currently is one of the most popular poker variants, also on the internet casinos. This specific online card game type, which is played between at least two parties with bets including valuables, is the one referred to below in the evaluation of possible fraud behavior. However, other types of online card games, for example such as "seven card stud", are just as well suited for evaluation of possible fraud behavior with the solution according to the invention.
  • the poker game of Texas Hold'em is played with a regular deck of 52 cards. One player is appointed dealer, whose job it is to deal closed cards to the other players, and to place open cards on the table. The cards are dealt clockwise beginning with the player directly to the left of the dealer.
  • the task of being dealer is also shifted to the player sitting directly to the left of the current dealer when the current hand has been played.
  • the game commences by the dealer dealing two cards each to every participant player. These two cards are called “the pocket cards” and shall be kept secret for the opponents. From these cards each player, in turn beginning with the player to the left of the dealer, can bet according to defined betting rules. After the first round of betting is completed, three open .cards are dealt to the table. These three cards are called “the flop” and can be used in combination with each of the players pocket cards to form a hand of poker. A second betting round then follows, where the players have the opportunity to use the information that became available through "the flop".
  • the distribution and names of the cards are schematically shown in Fig. 1. When all the cards have been dealt, all the remaining players, in clockwise order, can choose to either show their poker combination or to admit defeat. The best combination wins the game. The combinations, beginning with the worst, are High Card, Pair, Two Pairs, Three of a Kind, Straight, Flush, Full House, Four of a Kind, Straight Flush and Royal Straight Flush. Every move or action performed in a game of poker is based on the information or data available to the player at the time of the move.
  • the game state in, for example a game of Texas Hold ' em, is constituted of the player's closed and open cards, betting round, position from dealer, the player's previous moves and the opponents previous moves.
  • the player agent or model shall generate an estimation and move based on the unique game state for every given moment in the card game.
  • a state vector which is unique for the given game state, is therefore established describing all the relevant details of the game process leading up to that particular moment.
  • a state representation for a Texas Hold ' em player agent is divided into two logic sections.
  • the first section describes the card state and consists of 2 * 52 flag values (1 or 0), which represent the closed cards of the player, so called pocket cards, and the open table cards, so called community cards. Since the number of cards available per player is constant irrespective of the number of players, the card state vector always consists of 104 elements and can, according to one embodiment of the invention, have an appearance as shown in table 1 below.
  • the two closed cards 2 of hearts and 5 of spades are described through a state vector according to table 2 below.
  • the state vector for the open cards is designed in the same way, for example as shown in table 3 below for the open cards: 4 of diamonds, 8 of clubs, ace of hearts, 3 of spades and ace of clubs.
  • the second state section i.e. the player state, describes data on player position, betting round and previous bets.
  • the number of player positions and the size of the bets are dependent on the number of players. Thereby the size of the second state vector varies with the number of players.
  • the player state representation where P is the number of players, then, in one embodiment of the invention, can have an appearance as shown in table 4 below.
  • An example of how this state looks like for the first betting round in a game of poker between two people, also called heads up, is shown in table 5 below. The player is dealer and responds to the opponent calling his/her cards.
  • the player agent or agent array comprising neural networks, trained as described above, respond to game states with signals corresponding to the norm player's tendency to choose fold, check/call or bet/raise.
  • the out signal is an approximation of the average of the corresponding target values in the training sets.
  • the thus achieved generalizing capabilities of the neural networks are utilized for other states to provide target values that correspond to similar states in the training sets.
  • Fig. 2 schematically illustrates a player agent or agent array according to the preferred embodiment of the invention, comprising three neural networks, i.e.
  • three structurally identical three layer perceptrons one for each of respective game move fold, check/call and bet/raise, and is thus trained to respond with one of the respective moves corresponding to the move of a norm player for a given state in a Texas Hold ' em game for example.
  • the strongest out signal determines which move will be executed by the player agent or model for the given game state.
  • the three multilayer perceptrons rank the different moves based on a complete state vector or state array comprising amalgamated card state data and player state data.
  • Fig. 3 schematically illustrates a player agent or agent array, according to another embodiment of the invention, where the state underlying a move has been separated into two parts.
  • the card state describes the player's own cards and the cards that are available on the table.
  • the player state comprises game size or number of game participants, table position and the moves and bets made by the player and by the opponents.
  • the state assessment can be distributed onto several multi layer perceptrons.
  • a multilayer perceptron ranks the card state and the ranking is sent onwards to three further multilayer perceptrons. These three multilayer perceptrons rank fold, check/call and bet/raise respectively from the card ranking and the player state. Since the desired rank values for the individual states are unknown during training, this state representation is only useful when combined with reinforcement learning.
  • a separate neural network based player agent or agent array has to be trained for every sought after game type, size, i.e. number of participating players in a game, and betting structure, e.g. limit, pot limit, no limit etc, since both the appearance of the strategies and the dimension and composition of the state vectors or state arrays are dependent on these variables.
  • the player moves or actions are compared with the moves suggested by the player agent, for example the three neural networks trained for the specific type of poker game.
  • a scalar deviation is calculated as the difference between the out signals corresponding to the agent- and the player moves.
  • the deviations, accumulated over all the moves of a hand and normalized over the number of moves, then represents a quantization of the probability of the player's strategy, where a small deviation means that the hand is normal and vice versa.
  • one embodiment of the invention comprises that the average of the difference of several hands is combined with other factors such as difference distribution, e.g. a few highly divergent hands or constantly divergent, cash flow analysis and how many opponents there are, their distribution and divergence.
  • the results from such analysis's or comparisons are, in the preferred embodiment of the invention, graphically visualized through a pre-trained map array such as, for example a SOFM.
  • the map array or SOFM for this purpose has a graphical surface where every point on the surface corresponds to an attribute vector.
  • the size of the attribute vector corresponds to the dimensionality of the data set to be projected thereon.
  • a pattern from the set of data is matched against the attribute vectors of the map array or SOFM one by one.
  • the attribute vector which according to a set dimension represents the closest match pattern-wise, represents the position of that pattern in the map array or SOFM.
  • the map array or SOFM achieves the function of a classifier of analyzed players, i.e.
  • the training of a SOFM is accomplished by computing, for each pattern in a training set, the corresponding position in the SOFM and iteratively making the attribute vectors in the area around the position more similar to the pattern.
  • measurements for similarity and regional neighborhood must be specified.
  • Euclidean distance is used as a measurement of the similarity between patterns and attribute vectors and Gauss functions are utilized for updating the . attribute vectors in each region.
  • the training sets of the map array or SOFM according to the invention are constituted of manually selected sets of player hands, where both players of normal and "criminal" behavior of different types are represented.
  • the surface of the map array or SOFM is thus prepared with regions or clusters of vectors each corresponding to players with different attributes as exemplified in Fig. 4, in which a player with, for example abnormal play and a big loss, is positioned in one region or cluster, whereas a player with normal play and a medium win is placed in another region or cluster.
  • an agent array such as, for example software with a neural network pre-trained player agent or model according to the preferred embodiment described above, and a map array, for example software with a neural network pre-trained SOFM model according to the preferred embodiment described above, for detecting and visualizing fraud behavior in online gambling with bets, including valuables.
  • Online gamblers are connected through a PC to an Internet gambling site, to play a game of, for example, Texas Hold ' em against one another for money or other valuables, through betting.
  • Each player preferably logs onto the game through his/her individual player account, containing certain personal data required of the player in a registration procedure before gambling is allowed on the site.
  • the individual data are preferably checked by game administrators for authenticity and against possible earlier records of misconduct, before allowing/stopping the player participating in an online game with bets.
  • the individual player data as well as the data of the poker hands played through such a registered account are stored in a poker database 130.
  • the poker hand data for each participating player comprises at least card state data, player state data and data on decisions taken for each state of the played hand arranged in a play array, which is individual for each player. Thereby each played hand of poker can be re-created and connected to individual player accounts for analysis of possible fraud behavior.
  • the first analysis step comprises that a polling or fetching means in the analysis server 140 fetches the data or play array of a previously played hand of, for example, Texas Hold ' em from database 130 storage for re-creating the game through the agent array or player agent via system comprised software.
  • the player agent or agent array preferably being pre-trained according to the steps described above for said game type and size.
  • the moves or actions of the agent array or player agent for the thus re-run game are then for each game state, through means for comparing, compared with those of the play array, comprising the actual moves or actions taken by the players at each game state, and a resulting scalar difference is, through a computing means, computed for each action or move.
  • the deviance average and distribution of all moves in the hand is computed for each player and is mapped pattern-wise fitting within prepared regions on the map array, for example such as a SOFM, preferably according to the steps described above, wherein at least one region defines fraud behavior and at least one region normal behavior.
  • the analysis is then stored in the analysis database 150.
  • the map array for example the SOFM according to the embodiment shown in Fig. 4, as discussed above, thus receives the function as classifier of data as being able to classify players into the surface region of the map array or SOFM having the attribute data vector pattern most resembling the pattern of the data set originating from the above described comparison/analysis of an actually played hand with that of the agent array or player agent.
  • the map array or SOFM distinguishes the fraudsters from the honest players graphically, for example digitally on the screen of a digital display, and is also able to visualize possible relations between players, such as the relationship between a dumper and a receiver.
  • the game administrators are thus able to visually detect accounts, i.e. players, showing obvious fraud behavior in an online game of, for example Texas Hold ' em, through their computed positions in the map array and are hence able to take appropriate measures against these for avoiding such as dumping/receiving and collusion behavior.
  • Step two of the analysis comprises that the result of step one also is combined with other data such as cash flow data, opponent data and any previously logged analysis results of each player fetched from database 150 through the polling means in the analysis server 140 for a more finely tuned computing of the position in the map array, according to the steps described above, for each player.
  • a client instance 120 such as a web browser on a PC
  • the application server 110 By connecting a client instance 120, such as a web browser on a PC, to the application server 110, a network connection between the application server and its clients allows for the interactive inspection of analyzed players through a graphic rendering of the SOFM with the analyzed players plotted on the surface at their computed positions, as illustrated in Fig. 4.
  • Fig. 6, schematically shows system events for a continuous evaluation of fraud behaviour in an online game of Texas Hold ' em with bets between at least two parties, according to one embodiment of the invention.
  • the poker database is, through system comprised software, queried for a newly played hand 210. If a new hand is not found, the system "sleeps" 290 or rests for set time
  • the player agent is then exposed to the state vector and the probability of fold, check/call and bet/raise actions or moves is computed 260.
  • the scalar difference between the action preferred by the agent or model and the actual move taken is computed 270 through a comparator and the game model is updated with the actual move taken 280.
  • the game state is again advanced 240. If there are no player actions left, the game is finished and the deviance average and distribution is computed for each player 300, through system comprised computing means.
  • the cash flow and relations between participating players are computed 310 through said means and the accumulated values are combined with previously logged analysis of each player 320 and the positions in the map array, for example SOFM, are computed through said computing means for each player 330.
  • the analysis is finally stored in an analysis database.
  • the present invention comprises that a polling means fetches a plurality of input attributes for a specific state in a game between at least two players, wherein the state demands a decision from the players.
  • the attributes and decisions taken are stored in a play array for each piayer.
  • a predetermined agent array is arranged with attributes closest to represent the specific state for each player and a conventional decision taken for the specific state for each player.
  • a map array is pre ⁇ arranged with a set of possible agent arrays for specific state arrays as attributes, subdividing the set in at least two subsets. Each subset determines a class for the play arrays and at least one subset classifies a play array as conventional behavior and at least one subset classifies a play array as fraud behavior.
  • a comparator determines a difference between the play array and the agent array for each player and the difference is mapped on the map array positioned within one subset.
  • Each player decision is classified in a subset during play, determining an individual player pattern over said subsets.
  • the pattern comprises every decision taken by said individual player and determines said player as fraud if the corresponding pattern fits to a predetermined pattern of fraud behavior within the map array.
  • the input attributes can comprise at least one of card data, betting round, position from dealer and earlier player moves.
  • the map array for example such as a pre-trained SOFM, can be prepared with regions for classifying the behavior of said player. At least one region can then correspond to the agent array.
  • the agent array can comprise at least one neural network trained on multiple previously played games through error back propagation.
  • the agent array can be iteratively trained in norm playing tactics through utilizing training sets with the most recently analyzed games fitting within the frame of normal behavior in the map array or SOFM, thus accomplishing a periodic updating and fine-tuning of the norm playing tactics of the agent array.
  • a digital screen can preferably be used for displaying the map array with the regions and players visualized discrete. Alternatively the regions of the map array is visualized on another media, such as paper or the like printout media.
  • a first, a second and a third mutually separate and structurally identical neural network of the kind fully connected, feed forward, multilayer perceptron can be used for said agent array, each outputting a respective signal response corresponding to a degree of probability for fold, check/call and bet/raise respectively for a given state in the game, wherein the strongest signal of the three determines the move of the array for the state.
  • the difference for each game action can be added and normalised over all actions in the game to form a basis for evaluating and classifying the behaviour of the players as normal or fraudulent depending on the degree of divergence from preset threshold values for norm play according to the agent array.
  • At least one of a number of further game attributes including at least economic net, number of played hands and number of highly divergent hands can be considered in the evaluation or analysis.
  • Means mentioned in the present description can be software means, hardware means or a combination of both.
  • Position 1 0 Position 2 1 Betting round 1 1 Betting round 2 0 Betting round 3 0 Betting round 4 0 Bet 1, player 1 Bet 2, player 0 Bet 3, player 0 Bet 4, player 0 Bet 1, opponent 1 1 Bet 2, opponent 1 0 Bet 3, opponent 1 0 Bet 4, opponent 1 0

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Abstract

La présente invention concerne un système et un procédé pour évaluer un éventuel comportement frauduleux dans le cadre de jeux en ligne, tels que des jeux de poker sur Internet, entre au moins deux parties, avec des mises comprenant des objets de valeur. Un système d'interrogation extrait une pluralité d'attributs d'entrée pour un état spécifique dans un jeu entre au moins deux joueurs, l'état demandant une décision de la part des joueurs. Les attributs et les décisions prises sont enregistrés dans un tableau de jeu pour chaque joueur. Un tableau d'agents prédéfini présente des attributs les plus proches pour représenter l'état spécifique pour chaque joueur et une décision classique prise pour l'état spécifique pour chaque joueur. Un tableau de cartes présente un ensemble de tableaux d'agents possibles pour des tableaux d'états spécifiques comme attributs, subdivisant l'ensemble en au moins deux sous-ensembles. Chaque sous-ensemble détermine une classe pour les tableaux de jeu et au moins un sous-ensemble classifie un tableau de jeu comme comportement classique et au moins un sous-ensemble classifie un tableau de jeu comme comportement frauduleux. Une différence est établie entre le tableau de jeu et le tableau d'agents pour chaque joueur grâce à un comparateur, puis la différence est mise en correspondance sur le tableau de correspondances placé dans un sous-ensemble. Chaque décision de joueur est classée dans un sous-ensemble lors du jeu, déterminant un schéma de joueur individuel sur lesdits sous-ensembles. Le schéma comprend toutes les décisions prises par le joueur individuel et détermine que le comportement du joueur est frauduleux lorsque le schéma correspondant correspond à un schéma prédéfini de comportement frauduleux dans le tableau de correspondances.
PCT/SE2005/001351 2004-09-29 2005-09-16 Evaluation d'un eventuel comportement frauduleux dans le cadre de jeux en ligne Ceased WO2006036100A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP04023180A EP1642626B1 (fr) 2004-09-29 2004-09-29 Evaluation des cas de fraude pour jeux en ligne
EP04023180.5 2004-09-29

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WO2006036100A1 true WO2006036100A1 (fr) 2006-04-06

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EP (1) EP1642626B1 (fr)
AT (1) ATE457794T1 (fr)
CY (1) CY1110654T1 (fr)
DE (1) DE602004025574D1 (fr)
ES (1) ES2341445T3 (fr)
WO (1) WO2006036100A1 (fr)

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104850726A (zh) * 2014-02-13 2015-08-19 博雅网络游戏开发（深圳）有限公司 基于碰撞游戏的异常数据检测方法和系统
US9833710B2 (en) 2014-03-13 2017-12-05 Scientific Games International, Inc. Method and system for providing a secure shuffle of game objects across multiple entities
US10617957B2 (en) 2016-06-29 2020-04-14 Peerless Media Ltd. Systems and methods for reducing fraud in electronic games having virtual currency
US10657764B2 (en) 2016-06-29 2020-05-19 Peerless Media Ltd. Systems and methods for securing virtual currencies and enhancing electronic products
CN111886059A (zh) * 2018-03-21 2020-11-03 威尔乌集团 自动减少在在线游戏环境中使用欺骗软件
US10896574B2 (en) 2018-12-31 2021-01-19 Playtika Ltd System and method for outlier detection in gaming
US10926175B2 (en) 2016-06-29 2021-02-23 Peerless Media Ltd. Systems and methods for reducing impact of network disruptions
JP2024029112A (ja) * 2016-06-24 2024-03-05 エージー １８，エルエルシー 複数のプレイヤーの間の双方向型ゲームのシステム及び方法
US20250307960A1 (en) * 2024-03-29 2025-10-02 Christopher Cullen Poker study device

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008084334A2 (fr) * 2007-01-04 2008-07-17 Playtech Software Limited Procédé et appareil de détection de collusions dans des jeux en ligne
SG2014011399A (en) * 2011-06-01 2014-05-29 Gamblit Gaming Llc Systems and methods for regulated hybrid gaming
US11495086B2 (en) 2016-12-28 2022-11-08 Microsoft Technology Licensing, Llc Detecting cheating in games with machine learning

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6152824A (en) * 1997-03-06 2000-11-28 Mpath Interactive, Inc. Online gaming architecture
US20010031663A1 (en) * 2000-01-20 2001-10-18 Johnson Richard A. Safe gaming system
US20020138461A1 (en) * 2001-02-06 2002-09-26 Sebastian Sinclair Monetary behavior detection in a networked environment method and apparatus
EP1362623A2 (fr) * 2002-05-17 2003-11-19 Microsoft Corporation Empêchement de la communication verbale de et pour une partie choisie dans un système de jeu
US20040097287A1 (en) * 2002-11-14 2004-05-20 Richard Postrel Method and system for gaming over a computer network
US20050054445A1 (en) * 2003-09-04 2005-03-10 Cyberscan Technology, Inc. Universal game server

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6402614B1 (en) * 1995-06-30 2002-06-11 Walker Digital, Llc Off-line remote system for lotteries and games of skill
US5970143A (en) * 1995-11-22 1999-10-19 Walker Asset Management Lp Remote-auditing of computer generated outcomes, authenticated billing and access control, and software metering system using cryptographic and other protocols
US20020082065A1 (en) * 2000-12-26 2002-06-27 Fogel David B. Video game characters having evolving traits

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6152824A (en) * 1997-03-06 2000-11-28 Mpath Interactive, Inc. Online gaming architecture
US20010031663A1 (en) * 2000-01-20 2001-10-18 Johnson Richard A. Safe gaming system
US20020138461A1 (en) * 2001-02-06 2002-09-26 Sebastian Sinclair Monetary behavior detection in a networked environment method and apparatus
EP1362623A2 (fr) * 2002-05-17 2003-11-19 Microsoft Corporation Empêchement de la communication verbale de et pour une partie choisie dans un système de jeu
US20040097287A1 (en) * 2002-11-14 2004-05-20 Richard Postrel Method and system for gaming over a computer network
US20050054445A1 (en) * 2003-09-04 2005-03-10 Cyberscan Technology, Inc. Universal game server

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104850726A (zh) * 2014-02-13 2015-08-19 博雅网络游戏开发（深圳）有限公司 基于碰撞游戏的异常数据检测方法和系统
US9833710B2 (en) 2014-03-13 2017-12-05 Scientific Games International, Inc. Method and system for providing a secure shuffle of game objects across multiple entities
JP2024029112A (ja) * 2016-06-24 2024-03-05 エージー １８，エルエルシー 複数のプレイヤーの間の双方向型ゲームのシステム及び方法
US10617957B2 (en) 2016-06-29 2020-04-14 Peerless Media Ltd. Systems and methods for reducing fraud in electronic games having virtual currency
US10657764B2 (en) 2016-06-29 2020-05-19 Peerless Media Ltd. Systems and methods for securing virtual currencies and enhancing electronic products
US10926175B2 (en) 2016-06-29 2021-02-23 Peerless Media Ltd. Systems and methods for reducing impact of network disruptions
CN111886059A (zh) * 2018-03-21 2020-11-03 威尔乌集团 自动减少在在线游戏环境中使用欺骗软件
US10896574B2 (en) 2018-12-31 2021-01-19 Playtika Ltd System and method for outlier detection in gaming
US20250307960A1 (en) * 2024-03-29 2025-10-02 Christopher Cullen Poker study device

Also Published As

Publication number Publication date
DE602004025574D1 (de) 2010-04-01
ES2341445T3 (es) 2010-06-21
EP1642626B1 (fr) 2010-02-17
EP1642626A1 (fr) 2006-04-05
CY1110654T1 (el) 2015-06-10
ATE457794T1 (de) 2010-03-15

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