Monsters of Darwin: a strategic game based on Artificial Intelligence and Genetic Algorithms

Abstract

The production of video games is a complex process, which involves several disciplines, spanning from art to computer science. The final goal is to keep entertained the players, by continuously providing them novel and challenging contents. However, the availability of a large variety of pre-produced material is often not possible. A similar problem can be found in many single-player game genres, where the simulated behaviour generated by the Artificial Intelligence algorithms must be coherent, believable, but also adequately variegate to maintain a satisfactory user experience. To this aim, there is a growing interest in the introduction of automatic or semi-automatic techniques to produce and manage the video game contents. In this paper, we present an example of strategic card battle video game based on the applications of Artificial Intelligence and Genetic Algorithms, where the game contents are dynamically adapted and produced during the game sessions.

-

Author Keywords Strategic Games; Video games; User experience; Artificial Intelligence for video games; Genetic Algorithms INTRODUCTION The production of video games is a complex process, which involves several disciplines, spanning from art to computer science. Regardless of the specific game genre, the design and development processes must have as final goal the production of a video game able to propose an enjoyable gaming experience even after long periods of time.

GHITALY17: 1st Workshop on Games-Human Interaction, September 18th, 2017, Cagliari, Italy.

Copyright © 2017 for the individual papers by the papers’ authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.

Procedural Content Generation (PCG) is an approach with long and established history in different fields of Computer Graphics, aimed at creating data algorithmically. Examples of procedurally generated contents are textures [ 5 ] , buildings and cities [ 27, 28 ]. In the context of video games, PCG can be exploited to increase randomness of content and/or gameplay, with also the positive effects of reducing development time. For example, it can be used to automatically create a game level for platform games to achieve a desired level of complexity [ 26 ]. In other works, the application on PCG and AI techniques has been applied to several specific aspects of game optimization, such as: impact of Non Player Characters (NPCs) ( [ 1, 18, 25, 31, 33 ]), and adaptive or personalized content generation ( [ 10, 32 ]). Finally, there is a large literature related to the applications of Genetic Algorithms (GAs) in video games. The proposed techniques are mainly used to generate or evolve the game environment [ 3, 7, 24, 29 ], or for the evolution of the game agents behaviour, in order to produce more challenging opponents to the players [ 6, 11, 12, 21, 22, 23 ].

Differently from the other works on GAs in video games, the recently proposed GOLEM (Generator Of Life Embedded into MMOs) algorithm [ 9 ] addresses explicitely the need to introduce more variety and unpredictability in the monsters inside MMOs, in order to avoid that the players consider the game repetitive and less enjoyable after a long period of time spent playing. The main idea in GOLEM is to characterize each monster specie present in the game through its genome, and to generate new species by recombining their chromosomes, which represent a set of physical characteristics and skills. Each monster in GOLEM is represented by a chromosome composed by 53 genes, and the recombination process evaluates also the probability for the new monster to actually survive in the habitat in which it is born (e.g., a marine-like animal will unlikely survive in a desert). Moreover, other parameters are included in order to manage and, if needed, limit the population growth.

The concept of evolution of a population of creatures have been addressed also in a small number of commercial video games [ 8, 30 ], even if in a very different way than the approach proposed in GOLEM.

In this paper, we present a strategic card battle video game called Monsters of Darwin (MOD). The game applies the GA approach of GOLEM, but to a different game genre: in this case, the monsters are depicted on virtual cards, and at each turn, a new card with a new monster may be generated by the combination of the monsters on the cards currently played. Moreover, being MOD a single-player video game, an AI component has been implemented, to manage the game dynamics and to act as Non Playable Character (NPC). The paper is organized as follows: in the following sections we will recall the fundamental concepts related to AI and GA in video games, and then we will describe MOD design and implementation choices. Finally, we will draw conclusions and discuss major future developments.

ARTIFICIAL INTELLIGENCE IN VIDEO GAMES Contemporary video games are often characterized by the use of advanced AI techniques. AI solutions for video games [ 17 ] are quite different from those typical of classic AI, because while the latter are usually aimed at optimizing solutions for specific problems under no time or resources limits, the former must provide a believable solution to complex decisional processes in (nearly) real-time with limited computational resources. AI in video games has a huge amount of possible usages, mostly depending on the specific game genre. One example is represented by Non-Playing Characters (NPCs) in First Person Shooter (FPS) games, where the AI is responsible of the simulation of a believable behaviours for a group of enemies [ 16, 25 ].

In this paper, we consider a strategic video game. The video games in this genre are characterized mainly by actions based on tactics and planning, in order to achieve victory. Player’s decisions have a key role in the game, while chance is less relevant than players’ ability. There are two sub-genres, turnbased or real-time, and the role of AI can vary relevantly depending on the nature of the strategic video game. Real-time strategic games have usually a simpler AI, which is mainly used as a strategic layer. On the contrary, turn-based games may have very sophisticated and complex AI, which may even run on dedicated hardware, like in case of simulated complex board games (e.g., Chess) [ 17 ].

The characterization of an AI algorithm for strategic video game shares its terminology with the game theory. A game is classified according to the number of players (usually two, even if it can be extended to a higher number of players), the goal of the game, and the information each player has about the game. With respect to the goal, two subclasses of strategic games exist: • Zero-sum game: a player wins only if the other opponent lose. • Non zero-sum game: the focus of the player is on the winning, which may happen even without the opponent’s loss. Moreover, we can identify two cases also for the level of information the players have regarding the game: • Perfect information: the player knows everything about the state of the game, and about the possible options the opponent has for the next move. • Imperfect information: there is some random element in the game that make uncertain the possible evolution of state of the game.

The most popular strategic video game AI technique is the minimax algorithm. The main concept is that, during a turnbased game, a player tries to play the best move possible to achieve victory, while the opponent tries to use the best strategy to avoid this, by minimizing the player’s score. The minimax algorithm is a recursive algorithm, which works on a tree data structure (the game tree) where each node represents a board position, and each branch represents one possible move, leading from one board position to another. At each iteration, the AI algorithm evaluates the current state, considers each possible move and corresponding new board positions, and recursively calculates the score for each new possible situation, in order to choose the most appropriate one. The choice is performed using a heuristic called static evaluation function [ 17 ].

GENETIC ALGORITHMS IN VIDEO GAMES GAs are a particular class of algorithms, mainly belonging to the AI field. They are applied to solve many classes of problems but, more in general, they are useful in heuristic search processes and in optimization problems. They have been inspired by Darwin’s evolution theory: a population is represented by the chromosomes of a set of individuals, and a new generation in the population is produced by recombining the genetic material according to specific rules. For each generation, a fitness function selects the most “suitable” parents and iterates the reproduction process on them. To produce “children”, the algorithm applies crossover (a genetic recombination technique) and mutations on chromosomes, represented by bit sequences. The algorithm then evaluates, once the new generation has been created, if the population registers any improvement in any relevant feature: if this is the case, parents will be discarded and the new children will substitute them in the reproduction process. Generally, these steps are iterated until some optimal solution is reached [ 19, 20 ]. Crossover is used by GAs to mix the genes of two elements of the population. Some different approaches can be applied: 1. Single point crossover: the chromosomes of the parents are split into two parts in a randomly selected point. Two new chromosomes are generated, by combining the first and second parts of the original parents. 2. Two points crossover: the chromosomes of the parents are split into three parts. The chromosome of one child is composed by the first and third part of the first parent, merged with the second part of the second parent. Another chromosome is created using the remaining parts. 3. Uniform crossover: this approach provides a higher genetic variation, since each gene of the child is copied randomly from one of the corresponding genes belonging to one of the parents. The genes not chosen for the first child are used for the second one 4. Arithmetic crossover: the offspring chromosomes are the results of some arithmetic operation on the parents’ genes. Finally, genetic mutations can be useful for inserting into the new generation’s chromosomes some characteristics not inheritable from parents, since not present in their genetic heritage. Similarly to what happens in nature, mutations can introduce a new characteristic or modify/destroy an existing one.

MONSTERS OF DARWIN (MOD) Monsters of Darwin has been developed in order to investigate novel approaches aimed at maximizing the user experience in a particular game genre that, after long periods, may suffer because of the repetition of game contents. MOD is a zero-sum, imperfect information, turn-based singleplayer strategic card battle video game. The player plays against an AI opponent in order to collect the highest possible number of Monster Cards.

The game dynamics of MOD are quite simple. At each turn, two cards are played, one from the player on turn (the “real” player or the AI component) and one from the opponent. Then, the player on turn must choose between two different actions among the two cards: Monster Duel or Monster Coupling. Monster Duel follows rules similar to other card battle games, while Monster Coupling represents a procedural approach aimed at lower the game contents repetitivity, by the automatic generation of new Monster Cards depicting new monsters. Monster Coupling is based on the application of a GA, derived from the GOLEM algorithm [ 9 ]. A game session ends when a player defeats all the cards of the opponent. The final winner is the first player winning 3 game sessions. In the following subsections, we provide further details on the features and contents of MOD.

Monster Cards The main contents of MOD are the Monster Cards, depicting different kinds of monsters, using a graphic style inspired by grimoires and medieval manuscripts. Figure 1 shows an example of Monster Card. Each card is composed by three areas: in the bottom area it is indicated the natural element of the monster; in the central area it is depicted the monster, and, finally, the attack, defense and vital forces of the monster are shown in the top area.

The four natural elements (air, earth, water and fire) are used to create a hierarchy between the Monster Cards, used during the Monster Duel or Monster Coupling stages. In particular: • air rules over earth • earth rules over water • water rules over fire • fire rules over air To allow the graphical representation of the new monsters generated by the GA, each monster is composed by a set of 2D patches, each having a texture of a different physical part (head, body, legs, etc). With a similar approach, a new monster can be simply represented by assembling patches from different sets. Figure 2 shows a monster, and the patches composing its graphical representation.

At the beginning of the game, the player has a card deck composed by 8 Monster Cards (2 for each natural element). In the following turns, the player will choose 8 cards among those available in her deck. The AI module creates its deck choosing the Monster Cards randomly, even if balancing among the number of cards belonging to the different natural elements. Monster Duel During a Monster Duel, the damage a Monster Card can inflict to the vital force of the opponent’s card is established by the difference between the corresponding attack and defense force values (e.g., a card with an attack force of 8 will inflict a damage of 2 to a card with defense force 6). The damage can be modified on the basis of their relation in the natural elements hierarchy: the damage is raised of 1 unit in case the element of the attacking card rules over the element of the other card, otherwise it is lowered of 1 unit. All the Monster Cards have an initial vital force equal to 6. When the value reaches 0, the Monster Card is removed by the current game session, and it will become available only in the following games. Monster Coupling and Genetic Algorithm in MOD The GA implemented in MOD is applied during the Monster Coupling stage. As in GOLEM [ 9 ], each monster is described by a chromosome, which maps its characteristics and skills. The characteristics are related to the physical aspect (e.g., number of legs, wings, etc.), to the force (i.e., the attack, defense and vital forces), and to the natural element, as illustrated in Table 1.

Each characteristic can assume a value in a certain range. For the physical features, the range from 1 to 8 indicates one Monster Card from the initial deck: therefore, a value of 2 for the Head means that the new monster will be created using the head patch from the monster of the second card selected in the deck, etc. For the force features, the value changes depending on the subcategories: attack force can have values from 4 to 7, defense force can have values from 1 to 3, vital force can have values from 0 to 6. These values have been chosen after a tuning and testing stage of the game. The Monster Coupling stage begins evaluating the compatibility between the two Monster Cards currently played. Two monsters are evaluated as compatible for the coupling if the natural element of the card of the player on turn rules over the element of the opponent’s card, and if the difference in vital force between the two monsters is equal of higher of 2. If these constraints are not valid, then the Monster Card of the player on turn (i.e., the one who has tried the Monster Coupling) will be inflicted a damage following the rules of the Monster Duel stage. Otherwise, the Crossover function of the GA algorithm is applied to the chromosomes of the two monsters. To obtain the broadest diversity among the generated monsters, we have opted for the uniform crossover approach: for each characteristic in the chromosome, one value from the two parents is randomly selected and assigned to the new monster. Even if the crossover creates two new monsters, only the first is considered, and it is added to the player’s personal deck, while the two original cards subject to the coupling are discarded from the game session.

Artificial Intelligence in MOD The main operations of the AI component in MOD are: the selection of the Monster Card to play, and the choice between Monster Duel or Monster Coupling actions. In both cases, the AI component uses the minimax algorithm for the evaluation. Characteristic Head Eyes Arms Body Legs Tail Wings Attack Defense Vital Element

Type physical physical physical physical physical physical physical force force force natural element

Range 8 8 8 8 8 8 8 3 3 3 4 During the selection of the Monster Card to play, if the AI is the player on turn, the choice of the card is completely random. If, otherwise, the AI is responding to the player’s card choice, than the algorithm evaluates the played card, and performs a selection of a “stronger” card among the available ones in the deck. A card is “stronger” if its natural element rules over the element of the first card, and/or its attack force is higher. During the choice between Monster Duel or Monster Coupling, the AI algorithm evaluation is based on the number of remaining Monster Cards in the deck, and on the difference of vital force and compatibility between the two Monster Cards.

MOD implementation details MOD has been implemented using the Unity 3D game engine. For its characteristics, Unity 3D represents a powerful and flexible solution well suited for fast implementation of different game genres, allowing the integration of different techniques and material, and the development for different gaming platforms.

Figure 3 shows some screenshots of the Monster Duel and Monster Coupling stages.

CONCLUSIONS AND FUTURE WORK In this paper, we have presented a strategic card battle video game called Monsters of Darwin (MOD). The game uses a combination of AI and GAs in order to generate new Monster Cards, by combining the characteristics of the cards played in each turn.

The approach we are proposing with MOD is a preliminary attempt to address one of the features that can impact on players’ user experience. In fact, repetition of contents and mechanics in some game genres can lead to a loss of interest in the game after some time. One possible solution to avoid this issue is to introduce procedural and dynamic techniques to continuously generate novel contents and to adapt the game behaviour to the players’ actions, in order to maintain the game enjoyable and fun.

From the preliminary evaluation tests, the performances of MOD are promising: the GA in MOD is able to produce a high number of new monsters even from a limited set of basic Monster Cards, while the evaluation made by the AI algorithm is performed smoothly, limiting the waiting time to the minimum.

Future developments will consider the extension of the characteristics of the monsters chromosomes, and of the database of available monsters among which the players can choose the initial deck. Moreover, we aim at extending the AI component, by introducing new possible actions during the game turns, like e.g., a defense action to oppose the attack from the opponent player.

References

1. Gustavo Andrade , Geber Ramalho, Hugo Santana, and Vincent Corruble . 2005 . Automatic Computer Game Balancing: A Reinforcement Learning Approach . In Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS '05) . ACM, New York, NY, USA, 1111 - 1112 .

2. Richard Bartle . 2003 . Designing Virtual Worlds . New Riders Games.

3. Leonardo F. B. Silva de Carvalho , Helio C. Silva Neto, Roberta V. V. Lopes , and Fábio Paraguaçu . 2010 . An application of genetic algorithm based on abstract data type for the problem of generation of scenarios for electronic games . In 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems , Vol. 2 . 526 - 530 .

4. Daniele De Felice, Marco Granato, Laura Anna Ripamonti, Marco Trubian, Davide Gadia, and Dario Maggiorini . 2017 . Effect of Different Looting Systems on the Behavior of Players in a MMOG: Simulation with Real Data . In eHealth 360°: International Summit on eHealth, Revised Selected Papers . Springer International Publishing, 110 - 118 .

5. David S. Ebert , F. Kenton Musgrave , Darwyn Peachey, Ken Perlin, and Steve Worley . 2003 . Texturing & Modeling: a procedural approach . Morgan Kaufmann.

6. Anna I. Esparcia-Alcázar and Jaroslav Moravec . 2013 . Fitness approximation for bot evolution in genetic programming . Soft Computing 17 , 8 ( 2013 ), 1479 - 1487 .

7. Miguel Frade , Francisco Fernandez de Vega, and Carlos Cotta . 2012 . Automatic evolution of programs for procedural generation of terrains for video games . Soft Computing 16 , 11 ( 2012 ), 1893 - 1914 .

8. Galactic Arms Race homepage . 2017 . ( 2017 ). http://galacticarmsrace.blogspot.it.

9. Andrea Guarneri , Dario Maggiorini, Laura Anna Ripamonti, and Marco Trubian . 2013 . GOLEM: Generator Of Life Embedded into MMOs . In Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems: Advances in Artificial Life, ECAL . 585 - 592 .

10. Erin J. Hastings , Ratan K. Guha , and Kenneth O. Stanley . 2009 . Automatic Content Generation in the Galactic Arms Race Video Game . IEEE Transactions on Computational Intelligence and AI in Games 1 , 4 (Dec 2009 ), 245 - 263 .

11. Yuan Hong and Zhen Liu . 2010 . A First Study on Genetic Algorithms Based-Evolvable Motivation Model for Virtual Agents . In 2010 International Conference on Multimedia Technology. 1-4.

12. Johannes Inführ and Günther R. Raidl . 2012 . Automatic Generation of 2-AntWars Players with Genetic Programming . In Computer Aided Systems Theory - EUROCAST 2011 : 13th International Conference, Revised Selected Papers, Part I. Springer Berlin Heidelberg, Berlin, Heidelberg, 248 - 255 .

13. Raph Koster and Will Wright . 2004 . A Theory of Fun for Game Design . Paraglyph Press.

14. Dario Maggiorini , Alessandro Nigro, Laura Anna Ripamonti, and Marco Trubian . 2012a. Loot Distribution in Massive Online Games: foreseeing impacts on the player base . In 8th International Workshop on Networking Issues in Multimedia Entertainment (NIME'12) Int. Conf. on Computer Communication Networks ICCCN.

15. Dario Maggiorini , Alessandro Nigro, Laura Anna Ripamonti, and Marco Trubian . 2012b . The Perfect Looting System: Looking for a Phoenix? . In 2012 IEEE Conference on Computational Intelligence and Games (CIG) . 371 - 378 .

16. Dario Maggiorini , Laura Anna Ripamonti, and Samuele Panzeri . 2013 . Follow the Leader: a Scalable Approach for Realistic Group Behavior of Roaming NPCs in MMO Games . In Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems: Advances in Artificial Life, ECAL 2013 , Sicily, Italy, September 2- 6 , 2013 . 706 - 712 .

17. Ian Millington and John Funge . 2009 . Artificial Intelligence for Games, Second Edition . Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.

18. Olana Missura and Thomas Gärtner . 2009 . Player Modeling for Intelligent Difficulty Adjustment . In Discovery Science: 12th International Conference . Springer Berlin Heidelberg, Berlin, Heidelberg, 197 - 211 .

19. Melanie Mitchell. 1998 . An Introduction to Genetic Algorithms . The MIT Press.

20. Charles . J. Mode and Candace K. Sleeman . 2012 . Stochastic Processes in Genetic and Evolution: Computer Experiments in the Quantification of Mutation and Selection . World Scientific Publishing Company.

21. Antonio M. Mora, Antonio Fernández-Ares, Juan J. Merelo , Pablo García-Sánchez, and Carlos M. Fernandes . 2012 . Effect of Noisy Fitness in Real-Time Strategy Games Player Behaviour Optimisation Using Evolutionary Algorithms . Journal of Computer Science and Technology 27 , 5 ( 2012 ), 1007 - 1023 .

22. Antonio M. Mora, Ramón Montoya, Juan J. Merelo , Pablo G. Sánchez, Pedro Á. Castillo, Juan L. J. Laredo , Ana I. Martínez , and Anna Espacia . 2010a. Evolving Bot AI in Unreal™ . In Applications of Evolutionary Computation: EvoApplicatons 2010 . Springer Berlin Heidelberg, Berlin, Heidelberg, 171 - 180 .

23. Antonio M. Mora, Mária A. Moreno , Juan J. Merelo , Pedro A. Castillo , Maribel G. Arenas, and Juan L. J. Laredo . 2010b. Evolving the cooperative behaviour in Unreal™ bots . In Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games . 241 - 248 .

24. Fausto Mourato , Manuel Próspero dos Santos, and Fernando Birra . 2011 . Automatic Level Generation for Platform Videogames Using Genetic Algorithms . In Proceedings of the 8th International Conference on Advances in Computer Entertainment Technology (ACE '11) . ACM, New York, NY, USA, Article 8 , 8 pages.

25. Laura Anna Ripamonti, Silvio Gratani, Dario Maggiorini, Davide Gadia, and Armir Bujari . 2017a. Believable group behaviours for NPCs in FPS games . In Proceedings of IEEE Digital Entertainment, Networked Virtual Environments, and Creative Technology Workshop (DENVECT 2017 ).

26. Laura Anna Ripamonti, Mattia Mannalà, Davide Gadia, and Dario Maggiorini . 2017b. Procedural content generation for platformers: designing and testing FUN PLEdGE . Multimedia Tools and Applications 76 , 4 ( 2017 ), 5001 - 5050 .

27. Manlio Scalabrin , Laura Anna Ripamonti, Dario Maggiorini, and Davide Gadia . 2016 . Stereoscopy-based procedural generation of virtual environments . In Proceedings of IS&T's Stereoscopic Displays and Applications XXVII (28th Symposium on Electronic Imaging : Science and Technology) . 1 - 7 .

28. Michael Schwarz and Pascal Müller . 2015 . Advanced Procedural Modeling of Architecture . ACM Trans. Graph . 34 , 4 , Article 107 ( July 2015 ), 12 pages.

29. Nathan Sorenson and Philippe Pasquier . 2010 . Towards a Generic Framework for Automated Video Game Level Creation . In Applications of Evolutionary Computation: EvoApplicatons 2010 . Springer Berlin Heidelberg, Berlin, Heidelberg, 131 - 140 .

30. Spore homepage. 2017 . ( 2017 ). http://www.spore.com.

31. Pieter Spronck , Marc Ponsen, Ida Sprinkhuizen-Kuyper, and Eric Postma . 2006 . Adaptive Game AI with Dynamic Scripting . Mach. Learn . 63 , 3 ( 2006 ), 217 - 248 .

32. Julian Togelius , Renzo De Nardi, and Simon M. Lucas . 2007 . Towards automatic personalised content creation in racing games . In Proceedings of the IEEE Symposium on Computational Intelligence and Games . Togelius is working at IDSIA on SNF grant 21-113364 to J. Schmidhuber.

33. Georgios N. Yannakakis and John Hallam. 2009 . Real-Time Game Adaptation for Optimizing Player Satisfaction . IEEE Trans. Comput. Intellig. and AI in Games 1 , 2 ( 2009 ), 121 - 133 .