US20060281535A1

US20060281535A1 - Game optimization system

Info

Publication number: US20060281535A1
Application number: US11/344,661
Authority: US
Inventors: Nathaniel Bogan
Original assignee: 7 Systems LLC
Current assignee: 7 Systems LLC
Priority date: 2005-05-20
Filing date: 2006-02-01
Publication date: 2006-12-14
Also published as: WO2006127357A3; WO2006127357A2

Abstract

A game optimization system is disclosed. The game optimization system may take the form of a game balancing engine that is able to both monitor and identify components of games that form dominant and/or weak strategies. If desired, the game optimization system may further make or suggest changes to the game in order to bring out-of-balance components into balance.

Description

PRIORITY CLAIM

The present application claims priority to U.S. Provisional Patent Application Ser. No. 60/682954, filed May 20, 2006, the entirety of which is hereby incorporated by reference for all purposes.

BACKGROUND

Games have always been a popular pastime. Of late, computer gaming has become an important part of the game world. Computer networks such as the internet allow players on different computers in different locations to play with each other in real time.
Many popular on-line games involve the selection and use of multiple game elements, or components, that a player manipulates in order to succeed in the game, typically by defeating an opponent and/or accomplishing one or more tasks. Examples of games including player-selected game components include, but are not limited to, collectible card games (CCGs), role playing games (RPGs), and real time strategy games (RTS).
In contrast to games like chess, checkers, or hearts, where the playing components are static (pre-determined pieces with pre-determined characteristics), in the CCG and RPG-style games, players make choices regarding which playing components they will use in order to play any given game. For example, in one style of CCGs, players typically purchase (or trade for) large numbers of cards from which they are able to design and build their own customized deck(s). The player then uses the customized deck to battle one or more opponents, typically by playing combinations of cards and engaging in various duels. Thus, the player chooses which playing components he or she wishes to use. In one style of RPGs, players typically create one or more characters having various characteristics (strength, agility, intelligence, etc.) and then travel through the game collecting various game components, which may include, without limitation, tangible and intangible components such as abilities, spells, weapons, items, etc. that help the player's character battle opponents, accomplish tasks, and/or otherwise progress through the game. Thus, like the CCGs, RPG players often make choices regarding some or all of the components with which they play the game. Moreover, in many RPGs, the character in the RPG is limited to only a certain number of items or game components and must, therefore, select from between several different items, thereby forcing the player to make choices during the game regarding the items or game components with which he or she wishes to play.
Currently, thousands or even hundreds of thousands of players are able to access and play on-line games. As a game is played more and more, it may become apparent that, despite all efforts of the game designers, a particular game component strategy, by which is meant the use of a particular game component or a particular combination of game components in game play, unfairly dominates the game. In the CCG context, a dominant strategy may become apparent by the repeated, non-random appearance of one or more specific cards in a statistically significant number of winning decks. In the RPG context, a dominant strategy may become apparent by the repeated, non-random appearance of one or more characters having given characteristics, and/or selecting similar abilities, and/or the ownership and/or use of one or more items, by successful players. Conversely, it may become apparent that there are (weak) strategies which are nearly always unsuccessful.
Thus, in the context of the present disclosure, the existence of dominant and weak strategies essentially becomes a requirement that players who wish to “win” or be successful limit their playing component choices to those components that are dominant and avoid those components that are weak. Because these types of games are designed and intended to allow players to make choices regarding game strategy and devise their own unique strategies, significant limitations on the choices that players make can negatively impact enjoyment of the game. Accordingly, many game designers spend large amounts of time during game development attempting to balance the game. However, because such efforts are made during game design and, typically, before the game is played by the general public, these efforts often ultimately fail, as it is often only after extensive game play that unfairly dominant and weak strategies become apparent.
Thus, a game that is designed to avoid the presence of unfairly dominant or weak strategies is greatly desirable. Therefore, a system that is able to both monitor for and identify game components that form or lead to the presence of unfairly dominant or weak strategies is greatly desired. Moreover, a system that is able to dynamically adjust the game, in order to account for and counteract any unfairly dominant or weak strategies is similarly greatly desired.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of one embodiment of a game balancing system for networked game play according to the present disclosure.
FIG. 2 is a schematic illustration of one embodiment of a game balancing system for non-networked game play according to the present disclosure
FIG. 3 is a flowchart depicting a first embodiment of a game balancing engine according to the present disclosure.
FIG. 4 is a flowchart depicting a second embodiment of a game balancing engine according to the present disclosure.
FIG. 5 is a flowchart depicting a third embodiment of a game balancing engine according to the present disclosure.
FIG. 6 depicts an exemplary playing piece according to one embodiment of a game according to the present disclosure.
FIG. 7 depicts an exemplary playing field according to one embodiment of a game according to the present disclosure.

DETAILED DESCRIPTION

The present disclosure describes a game balancing engine (GBE) that is able to both monitor and identify components of games that form dominant or weak strategies. In its most basic embodiment, the GBE comprises a computer program that monitors game play and measures the usage of the individual game components in order to identify those game components that create an unfair advantage or unfair disadvantage to a player using those components in game play.
For the purposes of the present disclosure it will be understood that the following terms are defined as follows:
“Game system” includes any hardware system by which the game is communicated to the player. The game system may be a dedicated single game system (Plug it in and Play TV Games, Jakks Pacific, Malibu, Calif.), a dedicated multi-game system (e.g. the Play Station & Play Station 2 game systems from Sony Co.; the X box game system from Microsoft Corp.; or the Game Boy, Nintendo, and Nintendo 64 game systems from Nintendo Corp.), or a general or multi-purpose device capable of playing games such as a personal computer, cellular phone, personal data assistant (PDA), handheld computer, or the like. It should be noted that the software and/or code for playing the game need not reside locally on the game system. Thus a computer that links to the internet and allows a player to play a game that resides entirely on a separate server would still be considered a game system for the purposes of the present disclosure.
“Networked game play” includes all ways and methods by which two or more game systems can communicate or link with one another, for example, in order to allow two or more players to interact with one another or the same environment. Networked game play may take place whether or not the players play “against” one another. Thus, networked game play includes games in which player vs. player combat is strictly banned, as well as cooperative play games and traditional player vs. player combat games. Networked game play includes, but is not limited to, both private and public internet and intranet game play.
“Non-networked game play” includes any game play that is not networked game play. Accordingly, non-networked game play may include, without limitation, stand alone games played on a single computer, or on-line single player games that do not involve communication, linkage, or interaction with other players or shared game environments.
“Game component” includes any tangible or intangible element existing in the game that a player may choose to acquire and/or use. Examples of game components include, without limitation, skills, items, cards, spells, characteristics, personality traits, the right to go first or make a move, and the like. In other words, game components are a manifestation of the strategic choices a player makes between two or more game elements. Thus, a game component may further include paths, routes, rooms, actions, etc. where a player is presented with the choice between whether to take path A or path B, whether to engage in action A or action B, or whether to take path A or engage in action A, etc.
As stated above, according to one embodiment, the GBE of the present disclosure is intended to reduce or eliminate the existence of unfairly advantageous or disadvantageous strategies in games, thereby encouraging strategic diversity and more interesting game play.
Exemplary game balancing systems according to the present disclosure are shown in FIGS. 1 and 2. In FIG. 1, game balancing system 100 is used to balance networked game play. In this particular example, a server 102 is in electronic communication with personal computers 104 and 106. Server 102 and computers 104 and 106 are capable of cooperatively running a game 108. The code for game 108 may reside on server 102, computer 104, computer 106, or a combination of any of the above. Moreover, it will be appreciated that either computer 104 or 106 may act as a server. In the embodiment shown in FIG. 1, GBE 110, resides on server 102. However, as shown with dashed lines, it will be appreciated that GBE 110 may reside in an alternate location and simply collect game play data from server 102, computer 106 and/or computer 108. Moreover, it will further be appreciated that while not shown in the Figure, some or all of GBE 110 may reside on either computer 104 or computer 106. In this system, game play data may be collected by the GBE during or after game play.
In FIG. 2, game balancing system 200 is used to balance non-networked game play. In this particular example, personal computers 202 and 204 each independently run a computer game 206 a and 206 b, respectively. Personal computers 202 and 204 are each capable of communicating with a GBE 208 located at a distant location, such as a personal computer or server. Computers 202 and 204 are not necessarily in communication with one another. Furthermore, as shown by broken line 210, games 206 a and 206 b are not necessarily in communication with each other. Game play data on each of computer 202 and 204 is received by the GBE during or after game play.
As shown in FIG. 3, in one particular method, the GBE collects data regarding actual game play in order to monitor usage of the various game components in the game. The GBE then determines the acceptable usage range for each monitored game component and identifies those components for which usage falls outside of a set of predetermined acceptable parameters. The game is then adjusted to encourage the usage of out of balance game components back to within the acceptable range. The adjustment may be performed automatically by the GBE, in which case the game designer may or may not be alerted as to which game components were out of balance. Alternatively, the GBE may identify out of balance game components to the game designer, who then adjusts the game accordingly.
As briefly discussed above, data collection may take place at any time. As non-limiting examples, data may be collected in real time, (i.e. as the game is being played), actively sent to the GBE by players upon completion of a game, or periodically uploaded by the GBE at random or predetermined intervals.
The GBE described in the present disclosure is suitable for use with a wide variety of game types including, but not limited to, collectible card games (CCGs), role playing games (RPGs) and Real Time Strategy Games (RTSs).
As a simple example of a GBE suitable for use with a CCG, the GBE may monitor how often players incorporate particular cards in their personalized decks. If the GBE identifies a particular card that is being used in a higher proportion of player decks than defined by the game designers as being acceptable, the GBE can label the card as being out of balance and alert the game designer to the fact. The game designer can then alter the out of balance card in any number of ways, for example by making the card more expensive or harder to use, reducing the effects of using the card, or making a new card available that is more effective in counteracting the effects of the out of balance card.

Alternatively, instead of simply alerting the game designer to the existence of the out-of-balance card, the GBE may be instructed to automatically adjust one or more specific features of any card identified as being out of balance (i.e. by increasing the card's cost, reducing the card's effects, etc.). Accordingly, the GBE may be provided with a list of parameters by which a card may be out of balance and a set of solutions, an example of which is shown in the following chart:



Out of balance usage	Solutions

Present in 90%-100% of decks	Increase cost by 100% -or-
	Decrease power by factor of 2
Present in 80-89% of decks	Increase cost by 75% -or-
	Decrease power by factor of 1.5
Present in 70-79% of decks	Increase cost by 50% -or-
	Decrease power by factor of 1

In the above chart, the GBE has been provided with two solutions for each out of balance scenario. Thus the computer may elect one of the solutions based at random or based on an algorithm of the designers choosing. Of course it will be understood that the above chart is intended solely for the purpose of description and understanding of the disclosure and should not be considered as limiting in any sense. It will also be understood that in many CCGS, there are often cards that must be present in all decks. Thus, in some cases, whether or not a card is out of balance and the possible solution(s) for fixing the card imbalance may be determined on a card-by-card basis.
In contrast to many CCGS, where the players' choices regarding which cards to include in their deck are made before game play starts, in many RPGS, players are often required to make choices involving game components during the game. For example, a player may be allowed to collect a certain number of items throughout the course of the game. Often, the number of items a particular player may have in his or her possession at any one time is limited, either by specific rules (i.e. each player can have a maximum of 5 items), by current circumstances (player X has $10 to spend, there are four items available for purchase in the store, but each item in the store costs $5), or by a combination of the rules and circumstances. Thus, each player is forced to choose which specific item(s) he or she wishes to use in the game. Alternatively, a player may simply elect to trade away or sell a particular component. These choices can be monitored by the GBE to determine whether specific items are being chosen more frequently than is desirable. Thus, when item X, costing $5 is consistently chosen over item Y, which also costs $5, and the game designers intended for the two items to be roughly equivalent in their impact on the game, the GBE may identify items X and Y as being out of balance.
FIG. 4 provides another method for implementing the GBE of the present disclosure. In this method, each player's usage statistics are weighted by a success calculation. As a simplistic example, the success calculation may be determined by whether the player was successful or not successful in the game when in the possession of a particular game component. Typically, a component will have to be part of a successful game in order to be part of the data set used to determine whether or not the component is in balance. The definition of “successful” usage may differ from game to game and as desired by the game designer. For example, in a CCG, a card may be required to be part of a winning deck in order for that card to be labeled as having been used successfully, therefore making the usage of that card acceptable for data collection purposes. Alternatively, in an RPG where the goal is often to complete one or more tasks, “successful implementation” may be identified when a player succeeds in a task or advances to a new level. One advantage of this method is that it reduces the possibility that a game player might intentionally lose with a given strategy in order to trick the GBE into altering one or more game components.
Alternatively, the GBE may be designed to factor in a player's degree of success during a game in calculating whether or not a component is out of balance. In this example, rather than using binary calculation (successful or unsuccessful) to determine whether a particular player's game components should be included in the GBE calculation, a player's degree of success is used as a factor to determine the representational relevance of that player's component usage. For example, the data generated by a player who enters a game for 10 seconds and gains 5 experience points may be less representationally relevant than a player who enters a game for 100 hours and gains 1 million experience points. However, using the degree of success calculation, the 10 second/5 experience point player's data may still be statistically significant and thus should be included in the GBE calculation, but would simply carry less weight than the 100 hour/1 million experience point player's data. Other ways of determining degree of success include progression through the game (i.e level or other advancement), number of wins, win/loss record, time spent playing the game, player rankings or ratings, and the like.
Those familiar with games will be aware that in many, if not most, games, it will be expected that some game components will be used more frequently than others, and the game will purposefully be designed as such. In such situations, it is desirable to ensure that game pieces are being used “fairly,” but not necessarily desirable that game components be used equally. As described above, in this context the term “fair use” is intended to mean that no one particular strategy, or a small set of strategies, become so dominant that a player must adopt one of those strategies in order to be competitive. One method of ensuring fair use is by calculating the relative expected usage (REU) of each component and then factoring in the REU of the component when determining whether the component is, in fact, out of balance.
A basic example of a GBE incorporating REU is shown in FIG. 5. According to FIG. 5, the GBE initially monitors usage of each game component. The GBE then weights each player's usage by that player's degree of success. In combination with the degree of success weighting, the GBE factors in the REU to determine whether the usage was within acceptable parameters. If the usage was outside of acceptable parameters, the GBE identifies out of balance components to the game designers and/or adjusts one or more features of the game components or game rules.
In general, REU describes the likelihood of a game component being encountered and used by a particular player during a particular game, as desired by the game designers. For example, game designers will often create very powerful game components, somewhat powerful game components, and weak game components. (It will be understood, of course, that these categories are created for the ease of description and that commonly game components will fall along a sliding scale of strength.) These component categories typically differ in some way in an attempt to balance their usage. One method for attempting to balance the usage of more and less powerful components is to limit the public distribution of the more powerful cards (i.e. rare cards in CCGs.) However, this balancing technique does not necessarily guarantee fair game play, as dedicated gamers will typically purchase any rare card(s) they desire, no matter what the cost—often forcing other players who would like to be competitive to purchase the same card(s) and adopt the same strategies. Other methods for balancing these component categories include making very powerful game components more costly, requiring a higher skill level to play the very powerful game components, or making the more powerful game components more difficult to use.
According to one embodiment of the present disclosure, REU provides a method by which one can determine specifically how to adjust out-of-balance game components by reverse engineering actual games and determining probabilistically what component choices a given player would have made, had the game been balanced. More specifically, Expected Usage is a specific game by game, player by player, choice by choice calculation of the probability, given all available knowledge about a particular player, that this player would have used a specific component if all the game components had an average marginal game value exactly proportional to their cost. It will be appreciated that in this context, the term “cost” is applied broadly to mean whatever it is that a player must do (pay money, pay points, acquire skills, etc.) in order to acquire and/or use the component. In other words, “cost” can be any change in game state associated with using and/or acquiring the component that has a negative effect on the player. Relative Expected Usage (REU) is any measure that is proportional to Expected Usage.
In general, there are at least three different types of components, each having different types of costs that must be incorporated into the REU calculation. These are pay-to-play components, skill-point acquired components, and effort-based components.
Pay-to-play components are components where a player must pay some amount of currency in order to play the component. These types of components are most commonly seen in CCG-type games, but may appear in other types of games. The amount paid for the component is intended to be commensurate with the power level of the component. Thus, the expectation in a hyper-balanced world (a world in which each component has an average marginal game value exactly proportional to its cost) is that these components are used interchangeably. In such a case, the REU calculation is essentially the availability calculation with some adjustment. The adjustment is related to the fact that players are no more likely to use a component they have 1000 of than one they have 2 of.
Skill-point acquired components are obtained by paying points, where points are accumulated through the game as the player advances in levels. These types of components are most commonly seen in RPG-type game, but may be seen in any type of game. Similar to pay-to-play components, the total point cost of the component is intended to be commensurate with the power level of the-component. However, because skill points are accumulated by level advancement, usage should be commensurate with power level. In other words, more powerful components are harder to obtain (as opposed to play). It will be appreciated that in this context, the term “power” is applied broadly to mean whatever it is that a player gains upon using or acquiring the component. In other words, “power” can be any change in game state associated with using and/or acquiring the component that has a positive effect on the player. For these types of components, the REU calculation may be based on the assumption that all possible skill-point paths should be used equally by all players who have the skill points required to access those paths.
Effort-based components use up some (limited) resource each time the component is used. These types of components are commonly seen in both CCG and RPG-type games. Often, effort-based components are abilities. The calculation of REU for effort-based components is very similar to the calculation for pay-to-play components because the cost of usage (in this case resource depletion) should be equivalent to the power of the component. Thus, as with pay-to-play components, a hyper-balanced game would expect players to be indifferent between such components.
Of course it will be appreciated that it is not the goal of every game designer to hyper-balance their games. Rather, the goal is generally to increase strategic diversity by reducing or eliminating unfairly dominant or weak strategies. Thus, while the REU calculation may depend on what should or would have happened in a hyper-balanced game, the determination of whether a given component or set of components is, in fact, out-of-balance is typically determined by a range of acceptable (or non-acceptable) divergences from the hyper-balanced scenario.
Of course it will be appreciated that the range of acceptable (or non-acceptable) divergences from the hyper-balanced scenario may be a component-specific calculation. For example, a game designer may desire for some components (or some choices) to be hyper-balanced, while other components (or choices) need only fall into an acceptable range. As a specific non-limiting example, a game may include the provision that the player who goes first is penalized by some amount. For example, the player going first may be required to pay some amount of currency for the privilege of going first and/or not allowed to engage in certain actions or make certain choices during the first turn. However, the game designer may desire for this choice to be hyper-balanced, such that whether or not a player goes first provides no advantage to either player.
In order to provide a more specific description of how REU may be calculated for a specific game, it is helpful to describe an exemplary game suitable for use with a GBE employing an REU. It will be understood that the above-described game balancing system and method is applicable to a wide range of networked and non-networked games that utilize various game components, including, but not limited to, CCGS, RPGs, adventure games, racing games, etc. Thus, the example below is to be taken in a non-limiting sense as numerous variations and manifestations are possible. Moreover, it will be understood that the game described below may be played and appreciated independently of the GBE and should not be considered as requiring the implementation of a GBE (with or without an REU calculation.)
One embodiment of the presently described game suitable for use with the above-described GBE involves an online game that combines certain elements of card-based strategy games (CCGs) with other elements of level-based RPG's. In the most general sense, the game combines the mechanic of each player custom-designing a collection of game components (i.e. cards) into his or her own arsenal of components (i.e. deck), but then requires each player to progress through an experience system (i.e. different levels) in order to access subsequent content (i.e. expansion cards). As with most games, the present game may incorporate some centralized theme in order to tell a story and generate increased player interest. An exemplary theme might be espionage. For the purpose of the present description, specific game examples will be provided with reference to the espionage theme. However, it will be understood that the game may incorporate any or no theme while retaining similar game mechanics. Moreover, it will be appreciated that the game described below may be implemented using only a subset of the game mechanics described below and/or incorporating additional game mechanics not included in the description below.
Typically, each player will initially create a character in the game system. In order to create the character, the player may select from a number of different traits, or foci. The number of traits that are selected may be limited by the game rules. For example, in a game incorporating a total of seven distinct traits, each player may select two characteristics for his or her character. In such a game, the player may designate a primary trait and a secondary trait. Of course it will be appreciated that the game may include a greater or lesser number of traits overall and that each character may be allowed to select more or less than two traits for his or her character. Moreover, the game may be designed to allow different players to select different numbers of traits.
According to one method of playing the game, the selected primary and secondary traits dictates the set of game components that will be available to each player's character when the player is designing his or her character's arsenal. For example, in a game incorporating the following foci: paramilitary, mastermind, corporate, psychic, rogue, science, and hacker; a player who selects the foci paramilitary and psychic will have an entirely different set of game components from which to design an arsenal than a player who selects the foci rogue and mastermind.
According to one method of playing the game, the objective of the game is to sequentially complete a predetermined number of tasks. During game play, the players have the option of playing game components from their individually designed arsenals that prevent the opponent from completing his or her task, playing game components from their individually designed arsenals in an attempt to complete their own task, or both. For example, in an espionage-themed game, the game may be won by sequentially capturing three secrets from the opposing player. During game play, the players have the option of playing game components from their arsenals that protect their secrets, attempt to uncover their opponent's secrets, or both.
Various game components are available to the players in forming their arsenals. The game components may be divided into various categories such as: secondary, or controlled, character pieces, effect pieces and object pieces. Secondary or controlled character pieces typically represent characters that are playing in the game (i.e. warriors, athletes, magicians, etc.) Effect pieces typically perform one or more actions that affect other pieces in the game. Object pieces typically represent some object that may be used in the game. Of course it will be appreciated that additional or alternative categories of components may be utilized.
According to the above-described espionage-themed game, the game components may include agents (secondary pieces), plans (effect pieces) and devices (object pieces). Each agent piece represents an operative. Once deployed, the operative stays in play until captured by the opponent. Agents may be deployed during a player's turn. Agents are capable of infiltrating or securing secrets.
Each plan piece has an effect on other pieces in play. The plan piece may be moved to an “inactive area” (described below) after it is executed.
Each device piece represents a piece of technology or other artifact that may be used against an opponent. Devices may be deployed during a player's turn, but may be activated at any time.
Each game component will typically include a variety of indicia indicating information about the game component. This information may include, for example, the component's category (i.e. secondary, effect, or object), conditions under which the component may be brought into play, effects and/or abilities. The information may be indicated in any meaningful way, including by pictures, words, colors, etc.
An exemplary game component useful for the espionage-themed game is shown in FIG. 6. As shown in FIG. 6, the depicted game component includes indicia corresponding to: Piece Name; Deployment Cost; Piece Type; Manifest Rating; Piece Abilities; Infiltrate/Elude, Observe/Secure; and Activity Points.
As might be expected, the Piece Name indicates the name of the individual piece.
The Deployment Cost represents the amount of money which must be budgeted to deploy the piece. Deployment Cost is expressed in International Currency (IC).
The Piece Type specifies whether the piece is an agent, device, or plan.
The Manifest Rating is a number indicating the relative availability of the piece. A higher number indicates that the piece is more available. Generally, the manifest rating will equal the percentage chance that a piece will manifest (i.e. be made available for play) during a given turn. (This is described in greater detail below.)
Piece Abilities lists the actions the particular piece can take. For pieces that are capable of moving between zones (see below) the movement costs may either be identified on the card, or assumed for each piece type, i.e. all agents may have a predetermined movement cost while all devices may have a different predetermined movement cost, both of which are assumed and not identified on the particular playing piece.
Infiltrate/Elude, Observe/Secure represents agent ratings. Each agent receives four ratings. The first two ratings, Infiltrate and Elude, reflect the agent's capability to enter behind enemy lines and discover enemy secrets. The second two ratings, Observe and Secure, reflect an agent's ability to protect secrets.
Activity points are illustrated by a bar on the right side of the playing piece. The bar represents the number of activity points the piece currently has at its disposal. An agent, device, or plan may trigger abilities once they have accrued a certain number of activity points. A piece may not accrue more than 8 activity points.
As mentioned above, each player may design his or her own unique arsenal of playing components. The set of cards from which the player's arsenal may be designed is determined by the primary and secondary traits or foci selected by the player. The number of game components that may be included in the arsenal may be limited according to the game rules. For example, one embodiment of the presently described game may limit the number of game components to 50. Of course it will be appreciated that the game rules may impose any type of upper or lower limit on the number of game components in an arsenal, or impose no limit at al.
The playing field may incorporate a number of different zones. The zones may have different characteristics, for example, placement in different zones may indicate whether a game component is in use, available for use, or not available for use. In addition, game components located in some zones may not be visible to one or more players.
In the espionage-themed game described above, each player controls a playing field having seven different zones: an Arsenal Zone, an Activated Zone, a Deployed Zone, three Secret Zones, and an Inactive Zone. An exemplary game set up is shown in FIG. 7.
The Arsenal Zone is the starting point for all pieces in the game. The Arsenal represents the group of actively cultivated contacts, research programs, and other tools. Pieces in the arsenal may not be seen by either player.
The Activated Zone includes pieces that are ready for play (i.e. available for use). Pieces move from the Arsenal Zone to the Activated Zone based on their Manifest score (as described in greater detail below). Pieces in the Activated Zone may be deployed, when appropriate, by paying their Deployment Cost. Pieces in the Activated Zone may not execute abilities, and do not accrue Activity Points. Pieces in the Activated Zone may be seen by their owner, but not by the opponent. However, an opponent is allowed to see how many pieces are in the Activated Zone.
The Deployed Zone includes pieces that have been put into play. Accordingly, these pieces can execute abilities and may be moved to either the player's or the opponent's Secret Zones. Pieces in the Deployed zone may become visible to an opponent under certain conditions, but are typically hidden when they come into play. An opponent may see that a piece has been deployed, but will not typically be able to see the details of the piece, including its type, cost, abilities, etc.
There are three Secret Zones for each player. Each Secret Zone represents one player objective. Pieces in the Secret zones can use their abilities and accrue Activity Points.
The Inactive Zone includes pieces that have been neutralized (typically agents or devices). Pieces in the inactive zone are visible to both players, but have no Activity Points.
According to one method of playing the espionage-themed game, players take turns activating components, moving components to different zones, and engaging in battles. Each player's turn proceeds through a series of stages: Manifest phase, Accrual phase, Main phase, and Attack phase.
During the Manifest phase pieces in the arsenal are checked to determine whether or not they will manifest into the Activated Zone. As an example, a piece in the Arsenal Zone that has a Manifest rating of 10 will have a 10% chance of manifesting into the Activated Zone during any given Manifest Phase. A calculation is performed to determine whether the card successfully Manifests (i.e. whether on this particular turn the card has beaten the odds and can be introduced into play). This calculation may be performed, for example, by a computer program capable of simulating a probabilistic event. Those of skill in the art will be familiar with the concept of pseudo-random number generation as a means of simulating probabilistic events and any suitable means for doing so may be employed. Alternatively, the calculation could be performed by the simple mechanism of rolling a die (i.e. for a card having a Manifest rating of 17, a roll of 1 on a six-sided die results in the card Manifesting while a role of 2, 3, 4, 5, or 6 results in the card remaining in the Arsenal). Thus, it will be understood that on any given turn, any number (or no) cards may be moved from a particular player's Arsenal Zone into the player's Activated Zone.
During the Accrual phase, the activity point total of each card is incremented by 1. Also, players receive additional cash (20,000IC, or International Currency) and interest (25%) on their unspent cash.
During the Main phase, activated pieces may be deployed by paying their deployment cost in cash. Agent pieces in the Deployed zone may be moved into a Secret Zone and vice versa. A player can move his or her Agent pieces into any player-owned uncaptured Secret Zones at no cost. Agent pieces may be moved into opponent's ‘outermost’ Secret Zone at the cost of two Activity Points. Pieces may use abilities.
During the Attack phase, any pieces that are in an opponent's zone place that zone into contention. Battles take place in the following sequence:

1. The defending player, if they have pieces in the zone, may voluntarily reveal any of those pieces that remain hidden. Pieces that remain hidden may not participate in the defense of the zone.
2. Players may use piece's abilities.
3. The attacking player reveals all pieces he or she has in that zone.
4. Players may use piece's abilities.
5. The Infiltrate ratings of all attacking pieces are totaled. The Observe ratings of all revealed defending pieces are totaled. The attacking player assigns Infiltrate points from his or her total to revealed defending pieces as desired and the defending player assigns Observe points from his or her total to any attacking pieces, as desired. Attacking pieces that are assigned observe points equal to their Elude ratings are captured, and defending pieces that are assigned Infiltrate points equal to their Secure rating are captured. After pieces are captured, if there are attacking pieces still present in the zone, and no remaining revealed defending pieces in the zone, the attacker captures the zone, and all pieces in that zone return to their owner's arsenals.

As stated above, the presently-described game provides level advancement game play. An example of level advancement game play in the context of the espionage-theme game allows players to advance in two ways:
First, players may advance by earning renown points whenever they win a game. When a player reaches a certain renown point total, the player will achieve a new renown level. At each successive renown level, access to certain pieces will be unlocked. Players earn more renown for defeating a player above their level. Players may also lose renown points if they lose a match to a player who is below their level.
Second, players receive additional “contacts” by playing games. During each game, depending on a player's performance, he or she will receive a varying number of new contacts, represented by game pieces. At level 1, for example, a winning player may receive 2 new contacts. A losing player may receive 1 new contact. These contact pieces are randomly selected from the pieces unlocked for that player based on his or her selection of primary and secondary foci.
According to one method of play, player matches are assigned at random and a player must play randomly assigned opponents at a renown level equal to or below their own. Additionally, players may elect to raise the ceiling on the highest level opponent they may be randomly assigned.
As stated above, a GBE employing an REU calculation may be employed to balance and maintain balance of the above-described game. According to one method, REU is first calculated for a given player, for a given game win, for a given component in order to determine the expected value of usage for a given component. This can be calculated in the following manner:
REU=O*A/N
Where:

A (player p, game g)=the number of unique components in that player's arsenal;
O (player p, game g, component c)=the probability that a player at this exact experience level AND with access to the component (ignoring any class distinctions), owns at least one of component c; and
N (player p, game g)=the expected number of unique components owned by that player.
“A” is determined by tracking and counting the actual number of unique components played by this particular game winning arsenal. “A” takes into account the fact that some players will tend to use 50 unique components, while other players will tend to use only 10 unique components.
“O” is the probability of owning a particular component, assuming the player is in the class and is therefore equal to 1 minus the probability that the player owns none of those particular components:
O=Prob(own>=1)=1−Prob(own=0)

Prob(own=0) can be calculated by multiplying together every chance the player had to not get the particular component:
Prob(own=O)=((1−C)/C)^T
where:

C=the count of components available to each class from the set this component is part of; and
T=the total number of components this player has acquired from that set.
Thus,
O=1−((1−C)/C)^T
For a given set, the expected total number of components owned is:
T*O
N is the sum of expected owned components over all the sets, so:
N=Σ(T _i *O _i)
i over all sets
So:
REU=A*(1−((1−C)/C)^T)/Σ(T _i *O _i)

Once REU is calculated, the GBE can monitor the actual usage (U), degree of success (S) and REU for each component and determine a Balance Rating (BR) for each component.
Balance Rating can be calculated in several different ways. Exemplary methods for calculating BR are illustrated below: ${BR}_{1} [component] = \sum_{all players} (S * U / REU)$ $or$ ${BR}_{2} [component] = \sum_{all players} (S * U) / \sum_{all players} (REU)$
In addition, the above calculations can be normalized by the sum of success of all players. Exemplary methods for calculating a normalized BR are illustrated below. ${BR}_{1} Norm [component] = \sum_{all players} (S * U / REU) / \sum_{all players} (S)$ $or$ ${BR}_{2} {Norm}_{1} [component] = \sum_{all players} (S * U) / \sum_{all players} (S * REU)$ $or$ ${BR}_{2} {Norm}_{2} [component] = [\sum_{all players} (S * U) / \sum_{all players} (REU)] / \sum_{all players} (S)$
In the case where an exact EU is used for REU, a normalized Balance Rating can provide the degree of over (or under) use of the component. Specifically, a normalized BR of 1 means the component is used exactly as it would be in a hyper-balanced game, a normalized Balanced Rating of 0.5 means that the component is used half as much as it would be in a hyper-balanced game, and a normalized BR of 4 means the component is being used 4 times as much.
Alternatively, a Balanced Range can be calculated by identifying an acceptable deviation from a summary statistic, such as the mean, median, or a specific percentile (e.g. 30% percentile, 35% percentile, 40% percentile, 45% percentile, etc.) of the components' balance ratings. Of course, any other suitable method of determining an acceptable balanced range may be employed. Components outside of the acceptable balanced range may then be adjusted, as described above.
It will be appreciated that this specific calculation for the above-described game can be extended to all types of games, where, for each game one must initially determine how to accurately calculate the success weighting (S) for each given player, the REU for each given player for each given component, and the actual usage (U) for each given player for each given component. The success weighting may be the number of game wins, the number of experience points earned, or some other calculation of success as determined by the specific game. In the above-described game, S is the number of game wins, REU is calculated in the manner described above, and U is the fraction of the player's wins where the given component was in the arsenal.
As above, Balanced Range can then be calculated and any components outside of the acceptable limits may then be adjusted.
It will be appreciated that one or more GBEs may be employed to balance a particular game. For example, where the game includes multiple independent component sets, a separate GBE may be employed for each set. For the purposes of the present disclosure, a “component set” includes game components between which a player may choose during the course of a game. An example of game components that would be included in a single component set are a weapon available for purchase and a skill available for purchase, where the weapon and skill can be purchased with the same type of currency (e.g. both the weapon and skill can be purchased with gold), or where one type of currency can be purchased/obtained by using another (i.e. the weapon can only be purchased with gold and the skill can only be purchased with skill points, but skill points can be purchased with gold). Components are considered to be in independent component sets if there is no way for a player to choose between the two, in other words, if the components are not tradable within the context of the game. An example of components in independent component sets would be weapons that can only be purchased with gold and skills that only be purchased with skill points where gold and skill points cannot be traded for one another.
Those familiar with gaming will be aware that many games can be played in multiple formats (or metagames), with each format employing some or all of the same game components, but often employing different rules. For example, the game Magic: The Gathering (Wizards of the Coast, Seattle, Wash.) is known to have at least three formats: a sealed deck format, often used in tournaments, where players are provided with a specific pool of cards from which they must build their personalized deck; a type II format, where players may include in their personalized decks any cards published in the last 6 sets of cards released by the game manufacturer; and a type I format, where players may include in their personalized decks any card ever released by the game manufacturer.
In the case that a game can be played in one of a number of different formats, it will be understood that it would be desirable to ensure that the game components are in balance regardless of the format of game in which they are used. However, it may often be the case that a component that is in balance in one format is out of balance in another, resulting in a format-dependent imbalance. One way to compensate for format-dependent imbalance is to employ individual GBEs for each format, and then have the individual GBEs communicate to make decisions about whether and how to adjust a particular component when the component is identified as being out of balance.
As a non-limiting example, each Magic format described above may be regulated by an individual GBE. When the individual GBEs identify out-of-balance components, they may alert one another of the imbalance and adjust the identified out-of-balance components as follows:
If a particular component is found to be too weak in all formats, the component can be made stronger.
If the component is too strong in any format, the component can be made weaker.
If the component is too weak in one or more formats, but within acceptable parameters in any format, the component is left as is.
Of course it will be appreciated that alternative methods of adjusting identified format-dependent imbalance may be employed.
Moreover, the multiple metagame balancing technique described above may be employed not just when a game exists in multiple formats, but also when a particular game is played differently by different types of players. For example, different GBEs may be employed to monitor how the game and components are played by players of different experience levels. This may be desirable in the context of a game where a particular component is very valuable to beginners, less valuable to experienced players, and then regains importance for very experienced players. Moreover, it will be appreciated that multiple GBEs may be employed to monitor individual metagames distinguished by the game designer's subjective preference.
It is believed that the disclosure set forth above encompasses multiple distinct inventions with independent utility. While each of these inventions has been disclosed in its preferred form, the specific embodiments thereof as disclosed and illustrated herein are not to be considered in a limiting sense as numerous variations are possible. The subject matter of the inventions includes all novel and non-obvious combinations and subcombinations of the various elements, features, functions and/or properties disclosed herein. Similarly, where the disclosure refers to “an” element or the equivalent thereof, such disclosure should be understood to include incorporation of one or more such elements, neither requiring nor excluding two or more such elements.
Inventions embodied in various combinations and subcombinations of features, functions, elements and/or properties may be claimed in a related application. Such claims, whether they are directed to a different invention or directed to the same invention, whether different, broader, narrower or equal in scope to any original claims, are also regarded as included within the subject matter of the inventions of the present disclosure.

Claims

1. A computer readable storage medium for use with a multi-component computer game, the computer readable storage medium comprising:

code for calculating actual usage of a game component within the computer game;

code for identifying an acceptable usage range for each game component; and

code for determining whether the actual usage of the game component is within the acceptable usage range.

2. The computer readable storage medium of claim 1 further comprising code for identifying the game component as out-of-balance when the calculated actual usage is outside of the acceptable usage range.

3. The computer readable storage medium of claim 2 further comprising code for weighting actual usage by degree of success.

4. The computer readable storage medium of claim 3 wherein weighting actual usage by degree of success comprises identifying the actual usage by a given player and identifying the player's success in the computer game.

5. The computer readable storage medium of claim 4 where identifying the player's success in the computer game comprises determining if the player won the game.

6. The computer readable storage medium of claim 4 where identifying the player's success in the computer game comprises monitoring the player's progression through the game.

7. The computer readable storage medium of claim 3 further comprising code for calculating relative expected usage.

8. The computer readable storage medium of claim 1 further comprising code for automatically adjusting the game parameters when actual usage is outside of the acceptable usage range.

9. The computer readable storage medium of claim 8 wherein the game component has a power level in the game and automatically adjusting the game parameters comprises altering the power level of the component.

10. The computer readable storage medium of claim 9 wherein the game component has a cost and automatically adjusting the game parameters comprises altering the cost of the component.

11. A method for identifying out-of-balance components in a computer game requiring players to make selections between multiple game components, the method comprising:

determining an individual player's actual usage of a game component;

calculating the player's relative expected usage of the game component;

identifying the player's rate of success in the game;

calculating a balance rating for a game component based on the players' actual usage, relative expected usage, and rate of success;

determining whether the balance rating is within the acceptable usage range; and

identifying the game component as being out-of-balance if the balance rating is outside of the acceptable usage range.

12. The method of claim 11 further comprising altering the game if the component is identified as being out-of-balance.

13. The method of claim 11 wherein calculating actual usage comprises identifying the component or components that are selected when a player selects between two or more components.

14. The method of claim 13 wherein calculating actual usage further comprises identifying the component or components that are not selected when a player selects between two or more components.

15. The method of claim 11 wherein calculating the player's relative expected usage of the game component comprises identifying and counting the number of unique components played by the player during the game.

16. The method of claim 11 wherein calculating the player's relative expected usage of the game component comprises determining the probability of the player owning the game component.

17. The method of claim 11 wherein calculating the player's relative expected usage of the game component comprises calculating the expected number of unique components owned by the player.

18. The method of claim 11 further comprising normalizing the balance rating.

19. The method of claim 11 wherein calculating the player's relative expected usage of the game component comprises mathematical analysis of a hypothetical hyper-balance game.

20. A computer game optimization system comprising:

a computer game including multiple game components;

a first computer running the computer game;

a game balancing engine in electronic communication with the first computer, the game balancing engine being configured to collect data regarding usage of the game components in the computer game and identify out-of-balance components based on the collected data.

21. The computer game optimization system of claim 20 wherein the game balancing engine is further configured to automatically alter the game when out-of-balance components are identified.

22. The computer game optimization system of claim 20 further comprising a plurality of computers running the computer game, wherein the game balancing engine is in electronic communication with the plurality of computers and the game balancing engine is configured to identify out-of-balance components based on data collected from the plurality of computers.

23. A business method comprising:

collecting data regarding player component selections by multiple players in a networked computer game; and

maintaining a database of component selection information.

24. The business method of claim 23 wherein maintaining a database of component selection information comprises identifying an actual usage of a component for a given player.

25. The business method of claim 24 wherein maintaining a database of component selection information comprises identifying a degree of success for the player.

26. The business method of claim 24 wherein maintaining a database of component selection information comprises identifying a relative expected usage of the component for the player.

27. The business method of claim 23 further comprising calculating a balance rating for a component in the networked computer game.

28. The business method of claim 27 further comprising comparing the balance rating for the component with an expected usage range for the component and identifying the component as out-of-balance if the balance rating is outside of the expected usage range.

29. The business method of claim 23 where the data is collected in real time, as the game is played.

30. The business method of claim 23 where the data is collected after the game has been released to the public.

31. A computer game to be played by one or more players where the game comprises:

multiple selectable components, where players select between at least two different components during game play; and

a usage database configured to collect usage data and electronically communicate with a game balancing engine.

32. A method for optimizing performance of a computer game employing multiple selectable game components and rules governing the cost and power associated with the use and acquisition of the components, the method comprising:

identifying a current game state;

hypothesizing a hypothetical game state in which all components are hyper-balanced;

incrementally altering the rules so as to create an altered game state; and

determining whether the altered game state more closely resembles the hypothetical game state than the current game state.

33. The method of claim 32 wherein identifying a current game state comprises monitoring game play by multiple game players and calculating each player's actual usage for a component.

34. The method of claim 33 wherein identifying a current game state further comprises calculating each player's rate of success in the game.

35. The method of claim 34 wherein identifying a current game state further comprises calculating a balance rating for the components.

36. The method of claim 35 further comprising determining an acceptable balance range for the component.

37. The method of claim 36, where the acceptable balance range is determined by identifying an acceptable deviation from a hyper-balanced balance rating.

38. The method of claim 36, further comprising determining an acceptable balance range for the component.

39. A computer readable storage medium for optimizing performance of a computer game employing multiple selectable game components and rules governing the cost and power associated with the use and acquisition of the components, the computer readable storage medium comprising:

code for identifying a current game state;

code for hypothesizing a hypothetical game state;

code for incrementally altering the rules so as to create an altered game state; and

code for determining whether the altered game state more closely resembles the hypothetical game state than the current game state.

40. The computer readable storage medium of claim 39 wherein the code for identifying a current game state comprises code for monitoring game play by multiple game players and code for calculating each player's actual usage for a component.

41. The computer readable storage medium of claim 40 wherein the code for identifying a current game state further comprises code for calculating each player's rate of success in the game.

42. The computer readable storage medium of claim 41 wherein the code for identifying a current game state further comprises code for calculating a balance rating for the component.

43. The computer readable storage medium of claim 42 further comprising code for determining an acceptable balance range for the component.

44. The computer readable storage medium of claim 43, where the acceptable balance rating range is determined by identifying an acceptable deviation from a hyper-balanced balance rating.

45. The method of claim 43, where the acceptable balance range is determined by identifying an acceptable deviation from a summary statistic of the components' balance ratings.