Multi-objective evolution for 3D RTS Micro
We attack the problem of controlling teams of autonomous units during skirmishes in real-time strategy games. Earlier work had shown promise in evolving control algorithm parameters that lead to high performance team behaviors similar to those favored by good human players in real-time strategy games like Starcraft. This algorithm specifically encoded parameterized kiting and fleeing behaviors and the genetic algorithm evolved these parameter values. In this paper we investigate using influence maps and potential fields alone to compactly represent and control real-time team behavior for entities that can maneuver in three dimensions. A two-objective fitness function that maximizes damage done and minimizes damage taken guides our multi-objective evolutionary algorithm. Preliminary results indicate that evolving friend and enemy unit potential field parameters for distance, weapon characteristics, and entity health suffice to produce complex, high performing, three-dimensional, team tactics.
Real-Time Strategy (RTS) games model decision making in wartime. The game genre has become a popular research platform for the study of Computational and Artificial Intelligence (CI and AI). In RTS games, players need to establish bases, collect resources, and train military units with the aim of eliminating their opponents. To be good, RTS game players require a variety of decision making skills [1, 2]. First, the dynamic environment of RTS games requires real-time planning on several levels - strategic, tactical, and reactive. Second, players have to make decisions with imperfect information within the “fog of war.” Third, like poker, there is no one optimal strategy; players must model their opponent and adapt their strategies and tactics to the opponent’s playing “style.” Fourth, players must employ spatial and temporal reasoning to exploit existing terrain and the time-sensitive nature of actions on a tactical and strategic level. All of the challenges described above and their impact on decision making are crucial for winning RTS games and there is a steep learning curve for novices. In the video game industry, Starcraft (SC) and Starcraft 2 (SC2) players earn significant prize money and points during a series of championship games worldwide culminating in a World Series Championship at Blizzcon . Combining the long time horizon of chess, with the opponent modeling of poker, and the hand-eye coordination necessary for fast, fine control of large numbers of game units, RTS games have garnered significant interest within the computational and artificial intelligence community. After the success of AlphaGo, many researchers consider RTS games a significant next frontier in the computational and articifial intelligence field [4, 2].
An RTS player builds an economy that provides resources to generate fighting units to destroy the opponent. The term “Macro” specifies economy building and choosing the types and numbers of units to produce. “Micro” refers to the fine-grained, nimble, control of units and unit groups during a skirmish to inflict maximum damage on the enemy while sustaining minimal damage to friendly units. Most RTS games only allow maneuvering in two dimensions (2D) with flying units restricted to a plane and no real three dimensional (3D) maneuvering (although there are notable exceptions like Homeworld.111http://www.homeworldremastered.com/ This paper attacks the problem of generating good micro for full 3D maneuvering in the context of RTS games. We believe our approach is extendable to teams of autonomous real-world entities.
Each RTS game unit type has different properties that determine the unit’s effectiveness in a skirmish. Depending on unit type, a unit’s weapons will have a specific range, a specific amount of armor, and deal different damage to different opponent unit types. Each unit type has different movement speed and can take different amounts of damage. Given this level of complexity, determining the outcome of a skirmish usually requires a simulation and good RTS micro can win skirmishes even when outnumbered and unfavourably positioned.
I-a Related work
Good micro for skirmishes in battle or more generally, coordinated group behavior for adaptive agents, has applications in many fields from wargaming and modeling industrial agents, to robotics, simulations, and video games [5, 6, 7, 8, 9, 10, 11, 12, 13, 14]. From flocking, swarming, and other distributed control algorithms [12, 13] drawn from observing natural behavior to evolutionary algorithms that tune the parameters of potential fields that control the movement of autonomous vehicles , much research has been done in the area of group tactics, adaptive behavior, and distributed AI. In RTS games, there is strong research interest in generating good micro since this can lead to your winning skirmishes even when your forces are outnumbered or otherwise disadvantaged. This paper focuses on an evolutionary computing approach to generating coordinated autonomous behaviors (micro) for groups of computer game units (or entities). Specifically, we use an influence map and several potential fields to represent entity disposition in order to evolve effective group tactics, or good micro, for controlling a group of entities battling an opposing group of entities. Good micro maximizes damage to enemy units while minimizing damage to friendly units.
Within the games community, Yannakakis  evolved opponent behaviors while Doherty  evolved tactical team behavior for teams of agents. Avery used an evolutionary computing algorithm to generate influence map parameters that led to effective group tactics for teams of entities [17, 18] against a fixed opponent.
In physics, a potential field is usually a distance dependent vector field generated by a force. For example, the force of gravity generates a gravitational field, an attractive force, on objects with mass. This gravitational force depends on the distance between the objects and on their mass. Well designed attractive potential fields can keep friendly units close together while repulsive potential fields enable keeping your distance from enemies. Combinations (usually linear vector sums) of several attractive and repulsive potential fields can lead to fairly complex behavior . Potential fields were introduced into robotics as a method for real-time obstacle avoiding navigation and have been used in many video games for controlling multi-unit group movement [20, 10, 11, 12]. In this context, Hagelback and Johnson used potential fields to drive micro in ORTS, a research RTS and partial clone of Starcraft, a very popular commercial RTS [21, 22].
An influence map structures the world into a 2D or 3D grid and assigns a value to each grid element or cell. Very early work used influence maps for spatial reasoning to evolve a LagoonCraft RTS game player . Sweetser worked on an AI player designed with an influence map and cellular automata, where the influence map was used to spatially model the game world and help the AI player’s decision making in their RTS game EmerGEnt . Bergsma et al. proposed a game AI architecture which used influence maps for a turn based strategy game . Preuss et al. introduced an influence map based path finding algorithm for group movement in the RTS game Glest [26, 27]. Su-Hyung et al. used evolutionary neural networks to evolve non-player characters’ strategies based on the information provided by a layered influence map algorithm in the RTS game Conqueror. Uriarte et al. used influence maps for generating kiting behavior and used this for their StarCraft (A popular RTS game and research platform) player Nova . We define potential fields and influence maps in more detail later in the paper.
To the best of our knowledge, Liu’s work on evolving micro bots for Starcraft Brood Wars is closest to the work reported in this paper . Although potential fields and influence maps guide unit movement, Liu’s approach relies on a hand-coded, parameterized, control algorithm that defines a space of targeting, firing, and fleeing behaviors. A genetic algorithm then searches this space for parameters specifying unit behaviors aiming to maximize damage inflicted on enemy units while minimizing damage to friendlies against a fixed opponent. Behaviors like kiting (also known as hit and run) and opponent encirclement emerge and lead to high performing micro that can defeat this opponent .
Our work differs from Liu’s in that we use a larger number of potential fields instead of a hand-coded control algorithm specifying behaviors, and we generalize movement and maneuvering to 3D. Our preliminary results show that we can quickly evolve unit specific complex behavior including kiting, fleeing, and englobing. In our experiments with two types of units called vultures and zealots with different movement speeds, weapons damage, and health, we can evolve high performing micro that leads to winning skirmishes. For example, three vultures (fragile, fast, longer ranged units) learn to disperse and kite a much larger number of baseline zealots (robust, relatively slow, short ranged units) while suffering relatively little or no damage. Furthermore, zealots then evolve against these high performing vultures and learn micro that increases vulture damage with tactics such as englobing or dispersing into smaller groups. This kind of manual co-evolution points towards our future work where we plan to co-evolve high performance micro for RTS games.
The next section introduces potential fields, influence maps, and describes our simulation test-bed. Section III specifies our representation and describes the genetic algorithm used to evolve potential field and influence map parameters. We then provide and discuss our results. The last section summarizes our results and conclusions and points out directions for future work.
Ii Simulation Environment
Starcraft, released in 1998 and Starcraft 2, released in 2010 are the most popular RTS games . In the RTS AI research community, StarCraft has gained popularity as a research platform due to the existence of the StarCraft: Brood War Application Programming Interface (BWAPI) framework, and the AIIDE and CIG StarCraft AI tournaments  which use the BWAPI to compare Starcraft “Bots,” or Starcraft AI players. Since Starcraft is essentially 2D and we do not have access to Starcraft’s source code, we cannot use the BWAPI for our research. Instead we use FastEcslent, our open source, 3D capable, modular, RTS game environment. FastEcslent was developed at the Evolutionary Computing System’s Lab for computational intelligence research in games, human-robot and human-computer interaction, and other applications . We note that FastEcslent’s graphics engine runs on a separate thread and can be turned off, a useful feature for use with evolutionary computing techniques. We modeled game play in FastEcslent to be similar to StarCraft.
We modeled two units from Starcraft, Vultures and Zealots. A Vulture is a somewhat fragile unit with low hit-points but high movement speed and a ranged weapon. Vultures are effective when outmaneuvering slower melee units. A Zealot is a melee unit with short attack range and low movement speed but has high hit-points. In the rest of this paper, we use the term FVulture and FZealot to indicate that our units operate in 3D and are the Flying versions of the Starcraft units. Since our research focuses on micro, we disable “fog of war” in our scenario.
We enabled 3D movement by adding maximum () and minimum () altitudes, as well as a climb rate, , of . All units use the RTS physics used in Starcraft and Starcraft2.
Noting that a fvulture’s faster speed theoretically makes it possible for a single expertly microed fvulture to kite a group of starcraft’s AI controlled fzealots until all fzealots are eliminated, our experimental scenarios pitted three fvultures against thirty fzealots. Our baseline AI consisted of hand tuned potential field parameters that essentially caused fzealots to move towards and attack the closest enemy unit.
To evaluate a member of the evolving population, we extracted potential field parameters, created and initialized a FastEcslent scenerio with these extracted parameters controlling our player’s micro and played against our baseline AI. At the end of simulation steps or if all units for one side were destroyed, the resulting score was returned as this member’s fitness. The next section describes our representation and evaluation function in more detail.
Iii Representation and Genetic Algorithm
When we start our FastEcslent scenario, our player moves towards a target location defined by the lowest value cell in our evolving 3D influence map (IM). An IM is a 3D grid with values assigned to each cell by an IM function. An IMFunction is usually specified by two parameters, a starting influence for the location of the entity and a maximum range (of this influence) in a 3D game world. The influence linearly decreases to zero as range increases. In this research, we extend our IMFunction from using two parameters (starting influence and maximum range) to five parameters in order to represent more information as shown in Equation 1.
Iii-a Influence maps
To calculate any grid-cell value , we add the influence from each of the units within the range from the cell. is measured in number of cells. The influence of a unit at the cell occupied by the unit is computed as the weighted linear sum below.
where is the starting influence, is the entity’s percentage health, is the entity’s percentage cooldown time, and the for are evolvable weights. For cells within cells of the unit, we then compute the decrease per unit as a fraction of the starting influence.
The influence exerted by a unit at a distance varying from to from the unit location is then
So the cell at the unit location receives an influence and the eight neighboring cells (at distance ) receive influence, while cells at distance receive . Thus the IM value at a particular sum given by the sum of all entities’ influence on that cell is
for all entities at distance of the cell. The genetic algorithm evolves and .
This IMFunction not only considers units’ positions in the game world but also includes the hit-points and weapon cooldown of each unit. For example, an enemy unit with low hit-points or its weapon in cooldown can, with the right , generate a low influence map value. Our player always select the cell with the minimum value as the target attack location. Thus for example, our units can move towards and attack a damaged unit (low hitpoints) that cannot fire until the weapon cooldown period is over. In essence, changes in the five parameters (, , , , ) of the IMFunction specifies a target location for friendly units. “Move towards the lowest IM value location attacking enemy units as they come within range” is our target selection algorithm. This very simple algorithm can be contrasted with the more complex target selection and movement algorithms found in .
Iii-B Potential Fields
The IM provides a target location to move toward and we use potential fields to control unit movement to this location. Potential fields are a type of vector field over space of the form
where is the field strength in the direction of the entity producing the field, and are constants and is distance (usually).Potential fields in robotics, and in games, have traditionally been used for obstacle avoidance where the target location manifests an attractive potential field and all obstacles repel the controlled entity. The vector sum of these potential fields guides navigation. The direction of the force is in the direction of the vector difference from the other unit. and are the evolvable parameters for each of the potential fields in our work.
For specific values of and , decentralized flocking and swarming behaviors emerge when large numbers of units independently use potential fields to maneuver . Since good RTS micro takes into account unit health, position, and weapon state we define and use an attractive and a repulsive potential field for each of these factors. In addition, we assume that enemy and friendly units will generate different potential fields.
Specifically, the potential field controlling a unit’s behavior is computed from:
The influence map provided target position ( )
The potential fields generated by health (), distance (), and weapon cool-down state () of all other units in the game
Thus the potential field acting on a unit at position is the sum of the four potential fields shown below in Equation 3.
This potential field controls the unit’s desired heading and desired speed. The desired heading points in the direction of the vector and the desired speed is proportional to the difference between current and desired unit heading. Unit speed thus increases (up to a maximum) if the unit is already pointing in the direction of the potential field and decreases otherwise. In our game, unit speeds vary from to a maximum ().
The , and potential fields are composed from
A direction given by the normalized vector difference between the current unit’s 3D position and the other unit’s 3D position ()
where denotes the length of the difference vector.
A magnitude given by either the distance to the other unit, the health of the other unit, or the cool-down time of the other unit.
Whether the other unit is a friend or enemy
For example, is given by
where is distance, is given by equation 4, and where odd subscripts for and correspond to attraction and even subscripts correspond to repulsion. The first summation is over friends () and the second over enemies (). In the same way, is given by
where denotes the health percentage of the other unit, and is
where denotes the percentage time remaining before the weapon on unit is ready to fire again. Finally, there is a single attractive potential field exerted by the target location.
where is the distance to the target location and is the direction vector of the target location.
Thus potential fields guide unit movement. The coefficients have a range of and we use bits for encoding these parameters. The exponents range from and we use bits for their encoding. We thus need bits per potential field and bits for all potential fields. The influence map parameter ranges between to cells while , and range between and . Finally also ranges between and . This requires bits for a total binary chromosome length of .
When FastEcslent receives a chromosome, it decodes the binary string into corresponding parameters. These parameters then control friendly units as they move in the game world. The fitness of this chromosome is then computed based on the amount of damage done to enemy forces and the amount of damage received by friendly forces during a simulation run in the game world.
Iii-C Fitness Evaluation
We evaluate individuals in the genetic algorithm’s population in FastEcslent. The decoded chromosome specifies the parameters needed to compute potential fields and the influence map within FastEcslent. Every tick of the underlying simulation clock, friendly units compute potential fields and update their desired headings and desired speeds. If enemy units come within weapons range of a friendly unit, the friendly unit targets the nearest enemy unit. One difference from Starcraft, is that all units in FastEcslent can fire in any direction (oriented firing will be investigated as future work).
With the right set of potential fields and influence maps we expect friendly units using this simple targeting algorithm to significantly damage enemy units while themselves taking minimal damage. The simple fitness function below tries to maximize damage done and minimize damage taken.
Where denotes damage to enemy units and , damage to friendly units. This simple fitness function however, tends to generate fleeing behavior that simply minimizes damage to friendly units . One approach to solving this issue is to wait for the GA to find some way out of this fleeing behavior, another approach is to fiddle with the fitness function (for example, give more weight for damage done) to enable the GA to more easily find a way out . Instead, in this paper, we re-frame the problem as the problem of exploring the tradeoff between fleeing and fighting. Evolutionary multi-objective optimization provides an elegant framework for finding a pareto front of solutions that explore the tradeoff between damage done and damage received.
Our two objective fitness function is then specified by
where both and are normalized to be between and and we are trying to maximize both objectives. We use our own implementation of Deb’s well known non-dominated sort elitist genetic algorithm NSGA-II as our evolutionary multi-objective optimization algorithm . The normalized, two-objective fitness function used within our NSGA-II implementation then produces the results described in the next section.
We begin by describing experiments conducted to investigate our approach to evolving RTS micro. We drew inspiration from Starcraft and earlier work by Liu using BWAPI and created two unit types similar to vultures and zealots. Our unit equivalents can fly and are hence named fvultures and fzealots. Unit properties were specified earlier in the paper in Table I. Because fvultures can kite much larger numbers of fzealots dealing massive damage while receiving little, our experiments used fvultures versus fzealots.
In our initial experiments, we had one clump of three fvultures on the left against a clump of fvultures on the right side of the map. A clump is defined by a center and a radius. All units in a clump are distributed randomly within a sphere defined by this radius (). Although fvultures learned to do well against the fzealot clump, their performance degraded significantly when fzealots were not initially clumped. In addition, when we tried to evolve fzealot micro against fvultures, we found the same kind of over-specialized behavior emerging. To reduce overspecialization, we created three training scenarios (or maps). These scenarios are defined in terms of clumps and clouds. We have already defined clumps. A cloud, like a clump is defined by a center and a radius. However, the units in a cloud are distributed randomly within units around the sphere boundary defined by the center and radius (also ). The first scenario consists of a clump of fvultures versus a clump of fzealots. The second, a clump of fvultures surrounded by a spherical cloud of fzealots, and the last scenario consists of a clump of fzealots surrounded by a (small) cloud of fvultures.
Unit locations within the clump and cloud were generated randomly. Our evaluation function ran each of these three scenarios and the objective function values for each objective were averaged over these three scenarios. Evaluations took longer but the resulting performance was better and seemed to generalize well.
Iv-a Pareto front evolution
Figure 1 shows the evolution of the pareto front at intervals of five () generations for one run of our NSGA-II. We used a population size of , run for generations. Simple two point crossover with a crossover probability of and flip mutation with a probability of worked well with our implementation of NSGA-II. The -axis plots the first objective, damage done to fzealots, and the -axis plots damage received by fvultures.
Figure 2 shows the same information for fzealots evolving against already evolved fvultures.
Figure 1 and 2 illustrate how the pareto front progresses for one run of the multi-objective GA. Broadly speaking, the pareto front moves towards on the plot making progress towards maximizing damage done and minimizing damage received. We evolve micro that can destroy all fzealots but this also involves taking significant damage as shown by the solutions on the bottom right of Figure 1. We also see that when battling fvultures from this area of the plot, fzealots can do significant damage but never evolve micro to kill all the fvultures during the single run of the NSGA-II shown in Figure 2.
In our experiments, we ran the genetic algorithm ten times with different random seeds and Figure 3 shows the result. This figure plots the combined pareto front in the first generation over all ten random seeds versus the combined pareto front in the last generation over the ten random seeds. That is, we first did a set union of the pareto fronts in the ten initial randomly generated populations. The points in this union over all ten runs are displayed as squares for the initial generation (generation ) points and as circles for the points in the final generation (generation ). Let us label the union of the generation pareto fronts as and the union of the generation pareto fronts as . We then compute and plot the pareto front of this set union - the line on the left. We do the same for the ten pareto fronts in the last generation - the line on the right. Let denote the combined ten run pareto front in the first generation and denote the combined ten run pareto front in the last generation; Figure 3 then shows progress between first and last generation over all ten runs against an fzealot from the bottom right of Figure 2. Note that represents not an average, but the best (non-dominated) individuals over all ten runs. Individual runs produce initial pareto fronts that look like the intial front in Figure 1. We also have videos that show how the fvultures and fzealots behave at http://www.cse.unr.edu/~sushil/RTSMicro.
Figure 3 shows that even initial generations contain a range of acceptable micro behaviors. Since the target location is near the opponents and exerts a purely attractive potential field and units fire when in range, fzealots and fvultures usually come together and fight. As the potential field parametes evolve, fvultures learn to take less damage while doing more damage to fzealots. The videos support this observation and also show the range of micro behaviors evolving. Some can be simply described as “fleeing,” while others, even in early generations, defy succint explanation. Broadly speaking, the micro behaviors being evolved tend to keep fvultures outside the weapons range of the fzealots while looking for an opportunity to dart in opportunistically and deliver damage. Early on, if fvultures are surrounded by zealots as in training scenario two, the fvultures inflict significant damage but have not learned to kite and so also suffer significant damage. It is worthwhile to note that the opponent fzealots tend to favor tight circling on a plane, sticking close together while the fvultures are more dispersed, tend to change altitude more frequently, and try to keep their distance from fzealots as they try to balance their attractive and repulsive potentials.
In later generations the fvultures have learned potential field parameter values that lead to kiting (or kiting-like) behavior and this is reflected in the pareto front moving further to the right (more damage done to fzealots) and higher (less damage taken by fvultures). At generation , we have a choice of micro control tactics provided by the points on the pareto front at generation . At one extreme, indicated by the point at the very top on the generation pareto front , all three fvultures stay alive with no damage while inflicting damage on the fzealots. damage corresponds to killing out of the fvultures. At the other extreme, denoted by the point on the bottom right , two fvultures die while killing of the fzealots. The points in between these extremes represent other damage done versus damage received tradeoffs and picking which of these tactics to use depends on the player’s in-game needs of the moment. That our multi-objective approach naturally provides a range of choices for micro is an elegant side affect of this approach.
Iv-B Random parameter values
We also investigated how random potential field parameter values perform on our three scenarios and on random scenarios. We generated random chromosomes that specified these parameter values and evaluated them on the three training scenarios and on a set of on hundred random scenarios. In these random scenarios, the starting positions and orientations of the fzealots and fvultures were randomly spread within a larger clump with radius . Figure 4 plots the average objective values and their standard deviations obtained from evaluting the randomly generated chromosomes on both sets of scenarios. The average objective value on the three scenarios is average along each objective over evaluations. For the hundred () random scenarios, the average is along each objective over evaluations. The comes from a population size multiplied by evolutionary generations equating to the number of evaluations during one run of the GA. The horizontal and vertical lines on the plot indicate variation along each objective and are centered at the average objective values over the random chromosomes on the three training scenarios and the hundred random scenarios. Specifically, the length of a line equals one standard deviation and we display half a standard deviation on either side of the average. We also plot and for reference.
Figure 4 shows that the average randomly generated micro behavior on our three training scenarios has fvultures fleeing fzealots and doing little damage while much little if any damage. On the other hand, randomly generated behaviors on random scenarios average out to be near the center of the pareto plot. They take a little less damage than they mete out. Both these behaviors can be explained by the positioning of units in the scenarios. Assuming random parameter values produce random movement, initial positioning will play a large part in determing both objectives in the fitness function. The three training scenarios seperate opposing units and so fvultures tend to stay seperated from other units and take little damage. On the random scenarios, randomly positioned fvultures and fzealots moving randomly will take damage from each other before their random movement places them out of range and the clump disperses.
Iv-C Random locations
These results provide evidence that we can use our approach to evolve a range of good micro against a fixed opponent on a given set of scenarios. But does the evolved micro generalize to other scenarios? To investigate this question we also ran the solutions in and on a set of random scenarios (as described above). The average value of the two objectives over all random scenarios and their standard deviations along each objective obtained by evaluating the individuals in and on these random scenarios is shown in Figure 5 along with and , again for reference.
With arbitrary starting locations and orientations that do not correspond to training scenarios, we do not expect fvultures to do as well on average. We also expect wide variation in performance. As expected the figure shows that the average performance of the individuals from on the random scenarios is dominated by the points on the initial pareto front provided by . Let us label this average performance . The average performance of the individuals from on the random scenarios () shows improvement over , but this is still dominated by points on . We also see the existence of significant variation along both objectives and that the standard deviations along both objectives decreases from to . It is encouraging to see that although performance degrades on these random scenarios, individuals from generation perform better on average than individuals from the initial generation on the training scenarios. The change in average performance from the initial generation to the final generation works out to approximately three () more fzealots eliminated and one more fvulture surviving. Although we were not specifically after robust solutions, the micro tactics from on average do eliminate the majority of fzealots, while keeping the majority of fvultures alive on random never-seen scenarios. The figure provides evidence that initial unit positioning can significantly affect skirmish outcome. This is true for initial unit positioning in RTS games as well.
V Conclusion and Future Work
This paper proposed a new approach to evolving micro for RTS games in three dimensions for two different units. Using an influence map and an attractive potential field to determine a target position to attack, we used attractive and repulsive potential fields for distance, health, and weapon cooldown to evolve micro behavior for units in our 3D RTS game arena. Our NSGA-II implementation optimized micro based on a multi-objective formulation of the fitness function that maximized damage done and minimized damage taken. The results show that we can evolve a range of micro behaviors that go from taking and doing little damage, through micro that balances damage done and taken, and finally to micro that does maximal damage while taking some damage. On the three training scenarios, the evolved micro can eliminate out of fzealots while keeping all three () fvultures alive. Other evolved micro (on the pareto front) can eliminate all fzealots but at the cost of two fvultures. The multi-objective problem formulation and the NSGA-II lead to evolving pareto fronts that directly and naturally produce this wide range of micro choices. The fast, longer ranged, but fragile units (fvultures) learned micro behavior similar to hit and run (kiting) that is used by human players in RTS games like Starcraft. Videos of the micro behavior of these fvultures versus fzealots is available at http://www.cseunr.edu/~sushil/RTSMicro and show the range of complex behavior evolved.
Our genetic algorithm took evaluations to evolve the above micro. We generated random chromosomes and evaluated their fitness on our three training scenarios as well as a hundred random scenarios. As expected, the average micro behavior of random parameter values on the three training scenarios was well dominated by the evolved pareto front on the three training scenarios. Random chromosomes also led to an average micro performance that balanced damage done and taken and were explained based on the random dispositions of units in these scenarios.
Finally, experiments on a hundred random, never before encountered scenarios show that the potential field values evolved on three training scenarios used during evolution do not perform badly on these new random test scenarios. Although we were not aiming for robustness, micro performance on random scenarios is balanced and shows improvement over time.
We plan to follow two paths in the future. First, we would like to investigate how our approach scales with multiple unit types. That is, we would like to investigate micro for a group composed from multiple unit types versus another group also composed of multiple unit types. Second, we plan to investigate the co-evolution of micro using our new representation and appoach. Our current results show that we can evolve good micro against a fixed opponent, we would like to use a multi-objective, co-evolutionary algorithm to co-evolve a range of micro that is robust against a range of opposition micro.
-  M. Buro, “Real-time strategy games: A new AI research challenge,” Proceedings of the 18th International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence, pp. 1534–1535, 2003.
-  S. Ontañon, G. Synnaeve, A. Uriarte, F. Richoux, D. Churchill, M. Preuss et al., “A survey of real-time strategy game AI research and competition in StarCraft,” IEEE Transactions on Computational Intelligence and AI in games, vol. 5, no. 4, pp. 1–19, 2013.
-  (2016) Starcraft world championship series. [Online]. Available: http://wcs.battle.net/sc2
-  D. Hassabis. (2016) Official google blog: What we learned in seoul with alphago. [Online]. Available: https://googleblog.blogspot.com/2016/03/what-we-learned-in-seoul-with-alphago.html
-  P. Vadakkepat, K. Tan, and W. Ming-Liang, “Evolutionary artificial potential fields and their application in real time robot path planning,” in Evolutionary Computation, 2000. Proceedings of the 2000 Congress on, vol. 1. IEEE, 2000, pp. 256–263.
-  J. Ferber and O. Gutknecht, “A meta-model for the analysis and design of organizations in multi-agent systems,” in Multi Agent Systems, 1998. Proceedings. International Conference on. IEEE, 1998, pp. 128–135.
-  M. Barbuceanu and M. Fox, “Cool: A language for describing coordination in multi agent systems,” in Proceedings of the First International Conference on Multi-Agent Systems (ICMAS-95). Citeseer, 1995, pp. 17–24.
-  N. Jennings, “Commitments and conventions: The foundation of coordination in multi-agent systems,” Knowledge Engineering Review, vol. 8, pp. 223–223, 1993.
-  ——, “Controlling cooperative problem solving in industrial multi-agent systems using joint intentions,” Artificial intelligence, vol. 75, no. 2, pp. 195–240, 1995.
-  R. Olfati-Saber, J. Fax, and R. Murray, “Consensus and cooperation in networked multi-agent systems,” Proceedings of the IEEE, vol. 95, no. 1, pp. 215–233, 2007.
-  M. Egerstedt and X. Hu, “Formation constrained multi-agent control,” Robotics and Automation, IEEE Transactions on, vol. 17, no. 6, pp. 947–951, 2001.
-  C. Reynolds, “Flocks, herds and schools: A distributed behavioral model,” in ACM SIGGRAPH Computer Graphics, vol. 21, no. 4. ACM, 1987, pp. 25–34.
-  Y. Chuang, Y. Huang, M. D’Orsogna, and A. Bertozzi, “Multi-vehicle flocking: scalability of cooperative control algorithms using pairwise potentials,” in Robotics and Automation, 2007 IEEE International Conference on. IEEE, 2007, pp. 2292–2299.
-  P. Dasgupta, “A multiagent swarming system for distributed automatic target recognition using unmanned aerial vehicles,” Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, vol. 38, no. 3, pp. 549–563, 2008.
-  G. Yannakakis and J. Hallam, “Evolving opponents for interesting interactive computer games,” From Animals to Animats, vol. 8, pp. 499–508, 2004.
-  D. Doherty and C. OâRiordan, “Evolving tactical behaviours for teams of agents in single player action games,” in Proceedings of the 9th International Conference on Computer Games: AI, Animation, Mobile, Educational & Serious Games, 2006, pp. 121–126.
-  P. Avery, S. Louis, and B. Avery, “Evolving coordinated spatial tactics for autonomous entities using influence maps,” in Computational Intelligence and Games, 2009. CIG 2009. IEEE Symposium on. IEEE, 2009, pp. 341–348.
-  P. Avery and S. Louis, “Coevolving team tactics for a real-time strategy game,” in Evolutionary Computation (CEC), 2010 IEEE Congress on. IEEE, 2010, pp. 1–8.
-  V. Braitenberg, Vehicles: Experiments in synthetic psychology. MIT press, 1986.
-  O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,” The international journal of robotics research, vol. 5.
-  J. Hagelbäck and S. Johansson, “Using multi-agent potential fields in real-time strategy games,” in Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems-Volume 2. International Foundation for Autonomous Agents and Multiagent Systems, 2008, pp. 631–638.
-  (2016) Starcraft. [Online]. Available: http://us.battle.net/sc2
-  C. Miles, J. Quiroz, R. Leigh, and S. Louis, “Co-evolving influence map tree based strategy game players,” in Computational Intelligence and Games, 2007. CIG 2007. IEEE Symposium on, april 2007, pp. 88 –95.
-  P. Sweetser and J. Wiles, “Combining influence maps and cellular automata for reactive game agents,” Intelligent Data Engineering and Automated Learning-IDEAL 2005, pp. 209–215, 2005.
-  M. Bergsma and P. Spronck, “Adaptive spatial reasoning for turn-based strategy games,” Proceedings of AIIDE, 2008.
-  M. Preuss, N. Beume, H. Danielsiek, T. Hein, B. Naujoks, N. Piatkowski, R. StuÌer, A. Thom, and S. Wessing, “Towards intelligent team composition and maneuvering in real-time strategy games,” Computational Intelligence and AI in Games, IEEE Transactions on, vol. 2, no. 2, pp. 82–98, 2010.
-  H. Danielsiek, R. Stuer, A. Thom, N. Beume, B. Naujoks, and M. Preuss, “Intelligent moving of groups in real-time strategy games,” in Computational Intelligence and Games, 2008. CIG’08. IEEE Symposium On. IEEE, 2008, pp. 71–78.
-  A. Uriarte and S. Ontañón, “Kiting in RTS games using influence maps,” in Eighth Artificial Intelligence and Interactive Digital Entertainment Conference, 2012.
-  S. Liu, S. Louis, and C. Ballinger, “Evolving effective micro behaviors in real-time strategy games,” IEEE Transactions on Computational Intelligence and AI in Games, vol. PP, no. 99, pp. 1–1, 2016.
-  S. Liu, S. J. Louis, and M. Nicolescu, “Comparing heuristic search methods for finding effective group behaviors in RTS game,” in Evolutionary Computation (CEC), 2013 IEEE Congress on. IEEE, 2013, pp. 1371–1378.
-  (2015) Fast evoltionary computing systems lab entity engine. [Online]. Available: http://ecsl.cse.unr.edu/
-  D. M. Bourg and G. Seemann, AI for Game Developers: Creating Intelligent Behavior in Games. O’Reilly Media, 2004.
-  K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: Nsga-ii,” IEEE transactions on evolutionary computation, vol. 6, no. 2, pp. 182–197, 2002.