Game Balancing using Koopman-based Learning

Allan M. Avila1,2, Maria Fonoberova2, João P. Hespanha1, Igor Mezic1,2,Daniel Clymer3, Jonathan Goldstein3, Marco A. Pravia3 and Daniel Javorsek II4

Abstract— This paper addresses the analysis of howthe outcome of a zero-sum two-player game is affectedby the value of numerical parameters that are part ofthe game rules and/or winning criterion. This analysisaims at selecting numerical values for such parametersthat lead to games that are “fair” or “balanced,” inspite of the fact that the two players may have distinctattributes/capabilities. Motivated by applications of gamebalancing for the commercial gaming industry, our effortis focused on complex multi-agent games for which low-dimensional models in the form of differential or differenceequations are not possible or not available. To overcomethis challenge, we use a parameter-dependent Koopmanoperator to model the game evolution, which we trainusing an ensemble of simulation traces of the actualgame. This model is subsequently used to determine valuesfor the game parameters that optimize the appropriategame balancing criterion. The approach proposed hereis illustrated and validated on a minigame derived fromthe StarCraft II real-time strategy game from BlizzardEntertainment.

I. INTRODUCTION

The commercial gaming industry has a long-standinginterest in maintaining game balance as balanced gamesare typically more entertaining, and market pressureshelp drive their development [1–6]. Moreover, the con-temporary method for assessing and balancing games isa trial-and-error approach, thus representing an oppor-tunity for the application of Artificial Intelligence (AI).Normally, game developers release and observe an initialconfiguration of the game in large scale play. Then,developers gather high-level win/loss statistics whileplayers provide feedback about elements of the gamethat are overpowered or imbalanced. Finally, updates tothe game are made in which elements are buffed (per-formance increases) or nerfed (performance decreases)

1University of California Santa Barbara, Santa Barbara, CA 931172AIMdyn Inc., Santa Barbara, CA 931013BAE Systems, Arlington, VA 222034U.S. Air Force, Arlington, VA 22203*This work was supported by the Defense Advanced Research

Projects Agency (DARPA). Any opinions, findings, conclusions orrecommendations expressed in this article are those of the authorsand do not reflect the views of DARPA, the U.S. Air Force, the U.S.Department of Defense, or the U.S. Government. This document wascleared by DARPA on October 15, 2020 and is approved for publicrelease, distribution unlimited.

to achieve game balance. To date, little quantitativemodeling of game balance exists, and research on theapplication of AI algorithms to automating game balanceassessment (formally referred to as quantitative balanceanalysis) is extremely limited

When designing games there are two important con-cepts that a game designer must keep in mind, that ofgame mechanics and game dynamics. Game mechanicsare the technical underpinnings of the game and areessentially the game rules and objects that all playersnecessarily interact with in order to play. On the otherhand, game dynamics are the emergent behaviors andpatterns that arise from the specific way players chooseto interact with and utilize the defined game mechanics.As mentioned, balancing a game is the iterative processof defining game mechanics, observing the dynamicsthat arise, and then tuning the mechanics to improve anyundesirable dynamics that were observed. Traditionally,game developers have utilized human testing to performthis iterative procedure and for certain games, suchas single-player or player versus environment (PVE)games, this method can be effective albeit expensive.However, in the case of player versus player (PVP)games, such as on-line role-playing games (RPG), thedesigner can not ensure that dominating strategies do notexist a priori. Thus, a PVP game may be severely unbal-anced from the beginning and a considerable amount ofhuman testing must be deployed, keeping in mind that itis not possible for a group of human testers to identifyall possible strategies that could arise. Furthermore,even when an imbalance is identified finding a minimalcost and minimal effort solution may not always be sostraightforward. If the game mechanics are simple thenthe dynamics are often easily controlled and the gamecan be balanced with reasonable effort. However, gamescan quickly become highly complex [6] involving manydifferent types of agents, rules, terrains, vehicles, and soon. In which case the input-output relationship betweenmechanics and dynamics quickly becomes intractable.Thus, there is a need for automated game balancingmethodologies that are capable of scaling to complexgames.

Prior research efforts have utilized pattern mining

algorithms [5], Attribute search algorithms [6], andevolutionary algorithms [4, 7] to search for dominatingstrategies. Once dominating strategies or features arefound the mechanics of the game are tuned to coun-teract these imbalances. While some of the previouslymentioned approaches are automated balancing method-ologies, others are only partially automated methods thatrequire manual assistance. Furthermore, in many of thepreviously mentioned works the game mechanics areultimately hand-tuned which can be time-consuming andlack the ability to scale to complex games. The approachthis work takes is to assign a balance criterion to thegame dynamics as a function of the game mechanics.Game balancing is then obtained via optimization of themechanics to produce the desired balance criterion. Inorder to accomplish this, we quantify the time evolutionof the game dynamics as a dynamical system where wedenote the state space by X and the game dynamicsevolve according to

xpk ` 1q “ F pxpkq,u1,u2,θq (1)

where x is the asset state vector, u1,u2 are decisionvectors for both players, and θ is the vector of tuneablegame parameters representing the game mechanics. Aspreviously mentioned, games can be quite complexwhich in turn would lead to a high-dimensional com-plex state-space representation. In order to address thisdifficulty, we will utilize Koopman operator methods[8, 9] to enable estimation of the governing equations forthe game evolution directly from game simulation data.Figure 1 demonstrates a flowchart of the methodology.Koopman operator methods have emerged as a power-ful model reduction technique for analyzing complexnonlinear dynamical systems. The Koopman group ofoperators induced by a dynamical system propagate theevolution of functions, referred to as observables, underthe dynamics of the system. Specifically, the action ofthe Koopman operator group Kt on a function ψ : X Ñ

Cm is Ktfpxq “ ψ˝Stpxq, where Stpxq represents theflow of the dynamical system.

The Koopman operator is linear regardless of thenonlinearity of the underlying system and hence linearoperator theory on Hilbert spaces can be used to analyzethe evolution of observables. Specifically, the spectrum,eigenvalues/eigenfunctions, of the induced Koopmangroup reveal geometrical properties of the state space,[10, 11] and provide a simplified time evolution ofthe complex dynamics. Furthermore, there has been aplethora of algorithms developed for estimating spectralproperties of the Koopman operator directly from data[12–16]. This has enabled an enormous and successful

amount of data-driven modeling, analysis, and forecast-ing of complex systems [17–19].

II. GAME MODELING USING KOOPMAN OPERATORS

In its original formulation, the Koopman operator wasdeveloped for the analysis of autonomous uncontrolleddynamical systems. However, the works of [20] extendthe Koopman operator framework to allow for controlledsystems. This achieved by first reformulating equation(1) as an equivalent dynamical system evolving on anextended space consisting of a product Xˆ `2 betweenthe original state space X and the space of all squaresummable control sequences `2. The dynamics of theextended state x “

“

x u‰ᵀ

are given by

xpk ` 1q “

»

–

F pxpkq,u1,u2,θqSu1pkqSu2pkq

fi

fl (2)

where S : `2 Ñ `2 is the left-shift operator, Suipkq “uipk ` 1q. We denote the set of observables by ψpxqand their functional form is restricted to be ψpxq ““

fpxq gpu1q hpu2q fpxqgpu1q fpxqhpu2q‰

,where f “

“

fipxq, . . . , fnf pxq‰ᵀ

is a vector offunctions of the state, and similarly for g and h. Thecorresponding dynamics are governed by

ψpxpk ` 1qq “ Apθqfpxpkqq `B1pθqgpu1pkqq

`B1pθqhpu2pkqq `C1pθqfpx, tqgpu1pkqq

`C2pθqfpxpkqqhpu2pkqq

(3)

where the matrices Apθq, B1pθq, B2pθq, C1pθq, andC2pθq constitute a finite dimensional approximation ofthe Koopman operator.

The result of the above formulation is a family ofKoopman operator models parameterized by the gameparameters. In principle, the parameter dependency canbe captured by a single Koopman model if the param-eters are also lifted and the dynamics are considered toevolve on Xˆ `2 ˆΘ. Specifically, we can extend thestate space to include the parameters x “

“

x u θ‰ᵀ

and the resulting Koopman representation would be

ψpxpk ` 1qq “ Afpxpkqq `B1gpu1pkqq

`B2hpu2pkqq `C1fpxpkqqgpu1pkqq

`C2fpxpkqqhpu2pkqq `Dppθq

`Efpxpkqqppθq

(4)

where p is a vector of functions of the game parameters.The Koopman representations (3)-(4) generated by thedynamics of equation (1) represent the most general sce-nario. In this formulation, the game dynamics are mod-eled in terms of the state, control (player inputs), andparameters (game mechanics). However, it is possible to

Fig. 1. Flowchart of the Game Balancing Methodology. At the highest level, we generate several parameter variations with which we generategame replay data for training of the Koopman model and sensitivity analysis. The game replay data is used to train the Koopman model onthe lifted state space of weighted-densities (Section V-A). Once the Koopman model is trained we can use the model to generate game replaydata and produce an equivalent sensitivity analysis via the GoSumD software and compare that the sensitivities of the Koopman model matchthe sensitivities of the model (Section V). Furthermore, since the game mechanics were included in the lifted state, we can run a minimizationof a specified balance criterion with respect to the parameters to obtain optimal parameters that balance the game (Section V-B). In the topright corner of the figure, we demonstrate the original state of a game, consisting of two teams (Red/Blue), and this can be compared with thegraphic depicting the lifted state of weighted densities, shown in the bottom left corner of the figure, colored in green.

reduce the complexity of the above mentioned Koopmanrepresentations by instead considering the dynamics ofthe game as a closed-loop system. This is justified whenthe control inputs u1 and u2 are deterministic functionsof just the state variable x. This leads to the followingclosed-loop evolution of the game dynamics

xpk ` 1q “ F pxpkq,θq (5)

Under the closed-loop dynamics, the observables ψ areonly functions of the state and parameter leading to areduction in the complexity of the Koopman model tothe following form

ψpxpk ` 1qq “ Mψpxpkq,θq (6)

Where the observable ψ, on the right-hand side of (6),consists of functions of the state, functions of param-eters, and products of those functions. The resultingdimension of ψ would be at most nx ` nθ ` nxθ,where nx is the number of functions of the state, nθis the number of functions of the parameter and nxθis the number of products between functions of stateand parameter. Lastly, ψ appearing in the left-hand sideof (6), is a truncation of the full observable which onlyaccounts for the functions of the state and is accordinglyof dimension nx. This is due to the fact that the choice ofdeveloping a Koopman operator model on the extended

state space is to capture the influence that parametersand control inputs have on the dynamics however, weare ultimately interested in only the evolution of thestate and not the parameters or controls. The formulationin equations (3)-(4) are provided to present the fullgenerality and capability of Koopman operator methodsfor game balancing however, in this work we haveconsidered a Koopman representation given by equation(6) resulting from the closed-loop dynamics of the gamedynamics.

III. LEARNING

A convenient feature of the Koopman model in (6)is that the matrix M can be estimated from simulationdata collected by running a large number of games.Specifically, denoting by θi the parameter vector usedin the simulation of game i P t1, . . . , Nu and byxipkq P Rnx the corresponding state of the gameat time k P t1, . . . ,Ku, we can estimate the matrixM P Rnxˆpnx`nθ`nxθq using the following least squaresproblem

minM

Nÿ

i“1

K´1ÿ

k“1

}Mψpxipkq,θq ´ ψpxipk ` 1qq}2 “

minM

nxÿ

j“1

Nÿ

i“1

K´1ÿ

k“1

}mjψpxipkq,θq ´ ψjpxipk ` 1qq}2,

where mj denotes the jth row of the matrix M andψjpxpk`1qq is the jth entry of the vector ψpxpk`1qq.Straightforward algebra can be used to show that therows mj of the matrix M can be obtained independentlyby solving the following system of linear equations

´

Nÿ

i“1

K´1ÿ

k“1

ψpxipkqqψpxipkqqᵀ¯

mᵀj “

Nÿ

i“1

K´1ÿ

k“1

ψpxipkqqψjpxipk ` 1qq.

(7)

One should note that the computation needed to con-struct the matrix and vector in the left- and right-hand sides of equation (7) equation, respectively, scaleslinearly with the number of games N used to learndata, whereas the memory complexity scales with thesize of the matrix M, but not with N . This enables theuse of a very large number of simulations at a smallcomputational/memory cost.

IV. GAME BALANCING

Game balancing can be viewed as imposing a desir-able behavior for the evolution of the game, such askeeping the “scores” of the two players as similar aspossible. Formally, this corresponds to selecting valuesfor the parameter θ to minimize a criterion of the form

Jpxp0q; θq “kfinalÿ

k“kinit

|sc1pxpkqq ´ sc2pxpkqq|2 (8)

where sc1pxpkqq and sc2pxpkqq denote the score func-tions of players 1 and 2, respectively, at the states xpkq;and kinit, kinit`1, . . . , kfinal the range of times for whichwe seek to keep the scores similar.

When using a Koopman representation of the game,minimizing a criterion of the form (8) becomes espe-cially easy if the scores sc1pxpkqq and sc2pxpkqq areincluded in the set of observables. In this case, thecriterion (8) becomes a quadratic function of the observ-ables, which evolve according to the linear dynamics in(6), leading to a convex quadratic optimization. Whilemany solvers are available to solve such problems, inour work we used the toolbox [21], which provides aconvenient interface that can be used to add constraints

or additional cost terms that can be used to penalizeunsuitable choices of parameters.

V. STARCRAFT II MINIGAME

In this work, we have chosen to apply the above-described balancing methodology to the popular real-time strategy game known as StarCraft II (SC2). Thegame centers around three species known as the Terrans,the Zerg, and the Protoss. While the objective of thegame is to simply destroy your opponent in battle, SC2is known to have vast and complex game mechanicsthat can involve the collection of resources, the buildingup of structures, and soldiers among many others. Dueto its popularity and complexity, SC2 has been thecenter of numerous AI research projects which attemptto develop AI players. For example, the DeepMindsdivision within Google has developed an SC2 minigameplatform, known as the DefeatRoaches minigame, for thedevelopment and testing of their AI players. Specifically,the minigame consists of a battle between a team ofMarines from the Terran race and a team of Roachesfrom the Zerg race. Due to the availability of pre-trainedAI players, we have chosen to apply our methodologyto the balancing of the DeepMinds SC2 minigame. Theagents used in the generation of the datasets were thepre-trained AI agents developed by Google Deepmindsto play the DefeatRoaches minigame. The same agentswere used for every parameter combination consideredin this study so that the only differences in the outcomesof the game arise from parameter changes and initialconditions. The assumption is made that agents arerelatively matched in skill level throughout the range ofexplored parameter settings. For this SC2 miniGame, wehave chosen 4 game parameters Marine Health, MarineAttack, Roach Health, and Roach Attack as the modifi-able game mechanics. Health is the number of life unitsan agent has and Attack is the number of life units anagent can remove from an enemy’s health. Additionally,there are two relevant in-game metrics called Score andKilled Value Units (KVU) which measure the state ofthe game and are calculated by the minigame itself asfollows

Score “ 10ˆNRK ´ 1ˆNMK

KV U “ 100ˆNRK(9)

where NRK and NMK are the number of Roaches andMarines Killed. Note, the in-game Score metric above inequation (9) is not to be confused the the score functionsappearing in the balance criterion (8).

A. Koopman Modeling of StarCraft IIIn order to develop the Koopman model, we must

first select the type of observables to use for lifting the

dynamics. For this analysis, we use the health-weighteddensities of players as functions of the state as well asthe product between parameters and the health-weighteddensities. The health-weighted densities are obtained bypartitioning the game domain into Nbox boxes, taking acount of the number of Marines and Roaches that fallwithin each box and scaling the count by the respectivehealth of the agents. We also include the Score and KUVmetrics into the lifted state and choose Nbox “ 100,resulting in 202 functions of the state. The previouslymentioned choices ultimately lead to a model of thetype shown in equation (6), where nx “ 202, andnxθ “ 808. In order to train the Koopman model,we generated a uniform sampling of 601 parametervariations within ˘50% of their default values. We thensimulated 10 game replays for every parameter resultingin a data set consisting of 6010 game replays. Figure 2below demonstrates the health-weighted densities of theplayers.

Fig. 2. Plot of the Resulting Health-weighted Densities of Players.The densities are obtained by partitioning the game domain into boxes,taking a count of the corresponding Roaches/Marines within eachbox and weighing the count according to the health of the agents.Additionally, we also include the product between the health-weighteddensities and the game parameters into the lifted state ψ. With theobservables in hand we then proceed to compute a finite dimensionalrepresentation of the associated Koopman operator which governs theirevolution in time.

Prior to balancing the game, we begin by under-standing the sensitivity of the Score and KVU (dynam-ics) to the health and attack parameters (mechanics).This is achieved by utilizing AIMdyn’s software knownas Global Optimization Sensitivity and Uncertaintyin Models and Data (GoSumD). GoSumD is capableof producing a sensitivity analysis from samples ofinput and output data. The inputs (parameters) andoutputs (final Score and final KVU) of the game arefed into GoSumD, and GoSumD utilizes this data to

learn an input-to-output model based on Support VectorRegression (SVR) algorithms. Once the SVR model islearned, its derivatives with respect to inputs are utilizedfor producing a sensitivity analysis to determine whichparameters influence which outputs the most or least.In addition to determining which parameters affect thedynamics of the game the most, the sensitivity analysiscan also be used to validate the learned Koopman modelby comparing the sensitivity of the game data to thesensitivity of the learned Koopman model. In figure 3we demonstrate a comparison between the sensitivitiesof the SC2 data and the learned Koopman model. Itis clear to see that both outputs are most sensitive tochanges in the Marine attack while variations in theother parameters don’t seem to affect the game outputs.Interestingly, there is a well-known strategy amongstSC2 players which consists of upgrading the attack ofthe marine units early in the game in order to obtain atactical advantage and the sensitivity analysis is in linewith this known strategy. Furthermore, the sensitivity ofthe Koopman model matches the sensitivity of the SC2data, validating the fidelity of the learned model and itsability to reproduce the game dynamics.

Once the Koopman model is trained and validated wecan proceed to develop a balance criterion. For this workwe chose a balance criterion according to equation (8)with the specific score functions shown below

sc1pxq :“

ř

M HM pkqř

M HM p0q, sc2pxq :“

ř

RHRpkqř

RHRp0q

where HM pkq, HRpkq is the health of the Marines andRoaches at step k and the scores are computed forkinit “ 15 and kfinal “ 20. Essentially, the balancecriterion J can be thought of as the two-norm of thedifference between the normalized health of the gameagents. This balance criterion is a function of the gamemechanics and serves to measure how balanced thedynamics are. Thus, we solve a minimization problem tofind the set of balanced parameters which produce near-zero values of J . A flowchart of the game balancingmethodology can be referenced in figure 1.

B. Balancing Surface

The space of parameters for this SC2 minigame isfour-dimensional and it turns out that there is a locust ofpoints inside this four-dimensional space that yield a lowvalue of the balance criterion J . We refer to this locustof balanced points as the balanced surface and attemptto solve the minimization problem along slices of thissurface. This is achieved by holding two parametersfixed while minimizing the other parameters. Figure 4below demonstrates the results for slices along varying

Fig. 3. Comparison of SC2 and Koopman Model Sensitivity Analysis.Each column represents a parameter and each row represents anoutput. The color scale is such that blue corresponds to low sensitivityand red corresponds to high sensitivity. It is clear to see that bothoutputs are most sensitive to changes in the Marine attack whilevariations in the other parameters don’t seem to affect the gameoutputs. Furthermore, the sensitivity of the Koopman model veryclosely matches the sensitivity of the SC2 data validating the fidelity ofthe learned model and its ability to reproduce the game dynamics. Inorder to train the Koopman model, we generated a uniform sampling of601 parameter variations of health and attack of Roaches and Marineswithin ˘50% of their default values. We then simulated 10 gamereplays for every parameter resulting in a data set consisting of 6010game replays. The comparison shown here is between the sensitivityof the learned Koopman model and the training data.

Marine attack - Marine Health and varying Marine attack- Roach attack.

The resulting plot of the balanced points appears to becontours along the balanced surface, which itself appearsto contain some curvature. The balanced points shownin figure 4 were obtained via the minimization of thebalance criterion J subject to the dynamics estimatedvia the Koopman model. In other words, the balancedpoints are balanced according to the estimated dynamics.In order to verify that the minimization indeed convergedto balanced points, we generated 10 replays of gamedata for several points within a small neighborhood ofthe balanced points on the Marine Attack - Roach Attack

Fig. 4. Two Slices Along the Balanced Surface. Upon solving theminimization problem we find that there are several points for whichthe balance criterion J has a small value. We plot the locust of suchpoints, referred to as balanced points, and can infer that they seemto form contours of an embedded surface within the four dimensionalparameter space. The minimization problem was solved by holdingtwo parameters fixed while varying the others. Thus, we obtain slicesalong the balanced surface and here we display the Marine attack -Marine Health slice along with the Marine attack - Roach attack slice.It is also interesting to note the apparent curvature of the balancedsurface.

slice and compute the balance criterion resulting fromthe SC2 game data. We then utilize that data to verifythat the balanced points resulting from the Koopmanmodel are indeed balanced. Figure 5 below demonstratesthe validation of the Koopman model’s estimation. How-ever, we recall that the data used to train the Koopmanmodel was generated via 601 parameter sets sampleduniformly within ˘50% of the default parameter values.Essentially, the training data was sampled within ahyper-rectangle of the entire four-dimensional parameterspace. In certain cases, the balanced parameters thatresulted from the minimization of J fell outside of thishyper-rectangle and those cases have been colored redin figure 5 below.

To further demonstrate the difference between an un-modified game, specifically [MH=45, RH=140, MA=7,RA=21] and its corresponding balanced game, [MH=45,

Fig. 5. Validation of the Koopman Based Game Balancing Method-ology. Once the minimization problem is solved for the balancedparameters we generate 10 replays of game data for a set of parametersin a small neighborhood of the balanced surface and compute theresulting balance criterion of the games. We then plot the resultingbalance criterion from the SC2 minigame against the criterion pro-duced by the Koopman model to verify the fidelity of our balancingapproach. If points very close to the computed balanced surface indeedcorrespond to balanced games we expect to see a linear trend betweenthe Koopman model criterion and the minigame data. It is clear to seethat a trend is present amongst a majority of the points with relativelylittle scattering. Furthermore, we recall that the data used to trainthe Koopman model was generated via 601 parameter sets sampleduniformly within ˘50% of the default parameter values. In certaincases, the balanced parameters that resulted from the minimization ofJ fell outside of the range of the training data and those cases havebeen colored red.

RH=140, MA=4.39, RA=17.23] we plot the time seriesof the normalized health’s of the agents, produced by theKoopman model, and compare the slopes of the curves.The dashed curves in figure 6 clearly demonstrate how,in the unmodified game, the health of the Roaches decaymuch faster than the Marine’s health. The solid curvesdemonstrate the revised evolution of the health afterbalancing the game and the slopes of the curves arenow nearly equal.

Lastly, we compare the game statistics, computedover the 10 replays of the balanced and unmodifiedparameters, below in table I. It is clear to see thatthere was an apparent imbalance against the roachesand that the unmodified games ended within roughly20 game steps. Upon balancing, the final health of theplayers is indeed statistically near zero, as expectedsince the balance criterion was based on the health ofthe players. Interestingly, the balanced games now last,roughly three-times longer than the unmodified games.Lastly, the statistics show that the balanced game does

Fig. 6. Comparison of the Normalized Health Curves for anUnmodified Versus Balanced Game. The dashed curves correspondto the unmodified game and clearly demonstrate how the health of theRoaches decay much faster than the Marine’s health. The solid curvescorrespond to the balanced fame and demonstrates how the slopes ofthe curves are now nearly equal.

not produce a 50{50 probability of winning and thereason for this is that StartCraft is a fully deterministicgame. Hence, if you start from the same initial conditionthe same team will always win, even if both teams havenear-zero health at the end of the game. The reasonwhy Marines win 2 games out of ten, is because 2of the initial conditions slightly favor the marines andthe other 8 slightly favor the roaches. Overall, in afully deterministic game, it is problematic to associatebalance with the probability of a win, that is why wechose to balance according to the health rather than theprobability of winning.

Original Balanced% Marines Win 100% 20%Game Duration 14.5 ˘ .7 [Steps] 65 ˘ 8 [Steps]

Final Marine Health 55% ˘ 4 2% ˘ 4Final Roach Health 3% ˘ 2 16% ˘ 12

TABLE IBALANCED VERSUS UNBALANCED GAME STATISTICS.

VI. CONCLUSIONS

In this work, we have presented an automated gamebalancing framework that relies on a novel modeling ofthe relation between game dynamics (balance) and thegame mechanics (parameters). The game dynamics esti-mation is based on Koopman operator techniques whichhave previously never been applied to this domain. The

framework leverages high-performance nonlinear pro-gramming solvers to optimize a user-specified balancingcriterion and the effectiveness of the framework is val-idated by balancing a StarCraft II minigame. Thus far,our results demonstrate the ability of the framework’sability to learn the game dynamics from a limited set oftraining data.

Future works lie in scaling the framework to a morecomplex SC2 minigame and expanding the dimensionof the parameter space considered. Furthermore, thereis interest in quantifying the effects that the initialconditions, both in the game space and the lifted space,can have on a balanced game. Exploring the sensitivityof game balance to different initial conditions can yieldfurther insight into the dynamics of a game.

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