Computational Game Creativity

Computational creativity has traditionally relied on well-controlled, single-faceted and established domains such as visual art, narrative and audio. On the other hand, research on autonomous generation methods for game artifacts has not yet considered the creative capacity of those methods. In this paper we position computer games as the ideal application domain for computational creativity for the unique features they offer: being highly interactive, dynamic and content-intensive software applications. Their multifaceted nature is key in our argumentation as the successful orchestration of different art domains (such as visual art, audio and level architecture) with game mechanics design is a grand challenge for the study of computational creativity in this multidisciplinary domain. Computer games not only challenge computational creativity and provide a creative sandbox for advancing the field but they also offer an opportunity for computational creativity methods to be extensively assessed (via a huge population of gamers) through commercial-standard products of high impact and financial value. Games: the Killer App for Computational Creativity More than a decade of research in computational creativity (CC) has explored the study of autonomous generative systems in a plethora of domains including non-photorealistic art (Colton 2012), music (Wiggins et al. 1999), jokes (Binsted and Ritchie 1997), and stories (Peinado and Gervás 2006) as well as mathematics (Colton 2002) and engineering (Gemeinboeck and Saunders 2013). While commercial games have used computer generated artifacts such as levels and visuals since the early 1980s, academic research in more ambitious and rigorous autonomous game artifact generation methods, e.g. search-based procedural content generation (Togelius et al. 2011), is only very recent. Despite notable exceptions (Cook, Colton, and Gow 2013; Zook, Riedl, and Magerko 2011; Smith and Mateas 2011), the creation of games and their content has not yet systematically been explored as a computationally creative process. From a CC perspective, procedural content generation (PCG) in games has been viewed — like mathematics and engineering — as a potentially creative activity but only if done exceptionally well. The intersection of CC, game design and advanced game technology (e.g. PCG) opens up an entirely new field for studying CC as well as a new perspective for game research. This paper argues that the creative capacity of automated game designers is expected to advance the field of computational creativity and lead to major breakthroughs as, due to their very nature, computer games challenge computational creativity methods at large. This position paper contends that games constitute the killer application for the study of CC for a number of reasons. First, computer games are multifaceted: the types of creative processes met in computer games include visual art, sound design, graphic design, interaction design, narrative generation, virtual cinematography, aesthetics and environment beautification. The fusion of the numerous and highly diverse creative domains within a single software application makes games the ideal arena for the study of computational (and human) creativity. It is also important to note that each art form (or facet) met in games elicits different experiences to its users, e.g. game rules affect the player’s immersion (Calleja 2011); their fusion into the final software targeting the ultimate play experience for a rather large and diverse audience is an additional challenge for CC research. Second, games are content-intensive processes with open boundaries for creativity as content for each creative facet comes in different representations, under different sets of constraints and often created in massive amounts. Finally, the creation (game) offers a rich interaction with the user (player): a game can be appreciated as an art form or for its creative capacity only when experienced through play. The play experience is highly interactive and engaging, moreso than any other form of art. Thus, autonomous computational game creators should attempt to design new games that can be both useful (playable) and deemed to be creative (or novel) considering that artifacts generated can be experienced and possibly altered. For example, the game narrative, the illumination of a room, or the placement of objects can be altered by a player in a game; this explodes in terms of complexity when the game includes user-generated content or social dynamics in multiplayer games. Another unique property of games is that autonomous creative systems have a long history in the game industry. PCG is used, in specific roles, by many commercial games in order to create engaging but unpredictable game experiences and to lessen the burden of manual game content creation by automating parts of it. Unlike other creative domains where computational creativity is shunned by human artists and critics (Colton 2008), the game industry not only “invented” PCG but proudly advertises its presence as a selling point. Diablo III (Blizzard 2012), which set a record by selling 3.5 million copies in the first 24 hours of its release, proudly states that “[previous] games established the series’ hallmarks: randomized levels, the relentless onslaught of monsters and events in a perpetually fresh world, [...]”1. Highlyawarded Skyrim (Bethesda 2011) boasts of its Radiant A.I. (which allows for the “dynamic reaction to the player’s actions by both NPCs and the game world”) and its Radiant Story (which “records your actions and changes things in the world according to what you have done”). The prevalence of e.g. level generators in games makes both developers and end-users acceptant of the power of computational creativity. Unlike traditional art media, where CC is considered more of an academic pursuit, PCG is a commercial necessity for many games: this makes synergies between game industry and CC research desirable as evidenced by Howlett, Colton, and Browne (2010). This paper introduces computational game creativity as the study of computational creativity within and for computer games. Games can be (1) improved as products via computational creations (for) and/or (2) used as the ultimate canvas for the study of computational creativity as a process (within). Computational game creativity (CGC) is positioned at the intersection of developing fields within games research and long-studied fields within computational creativity such as visual art and narrative. To position computational creativity within games we identify a number of key creative facets in modern game development and design and discuss their required orchestration for a final successful game product. The paper concludes with a discussion on the future trends of CGC and key open research questions. Creative Facets of Games Games are multifaceted as they have several creative domains contributing substantially to the game’s look, feel, and experience. This section highlights different creative facets of games and points to instances of algorithmically created game content for these facets. While several frameworks and ontologies exist for describing elements of games, e.g. by Hunicke, Leblanc, and Zubek (2004), the chosen facets are a closer match to established creative domains such as music, painting or architecture. This section primarily argues that each facet fulfills Ritche’s definition of a potentially “creative” activity (Ritchie 2007, p.71). Additionally, it uses Ritchie’s essential properties for creativity, i.e. novelty, quality and typicality (Ritchie 2007) in terms of the goals of each creation process; whether these goals (or the greater goal of creativity) are met, however, will not be evaluated in this paper. From the official ‘What is Diablo 3?’ page at Blizzard’s website: http://us.battle.net/d3/en/game/what-is Visuals As digital games are uniformly displayed on a screen, any game primarily relies on visual output to convey information to the player. Game visuals can range from photorealistic, to caricaturized, to abstract (Järvinen 2002). While photorealistic visuals as those in the FIFA series (EA Sports 1993) are direct representations of objects, in cases where no real-world equivalent exists (such as in fantasy or sci-fi settings) artists must use real-world reference material and extrapolate them to fantastical lengths with “what if” scenarios. Caricaturized visuals often aim at eliciting a specific emotion, such as melancholy in the black and white theme of Limbo (Playdead 2010). Abstract visuals include the 8-bit art of early games, where constraints of the medium (low-tech monitors) forced game artists to become particularly creative in their design of memorable characters using as few pixels or colors as possible. In terms of computer generated visual output for games, the most commercially successful examples thereof are middleware which algorithmically create 3D models of trees with SpeedTree (IDV 2002) or faces with FaceGen (Singular Inversions 2001). Since such middleware are used by multiple high-end commercial games, their algorithms are carefully finetuned to ensure that the generated artifacts imitate real-world objects, targeting typicality in their creations. Games with fewer tethers in the real world can allow a broader range of generated visual elements. Petalz (Risi et al. 2012), for instance, generates colorful flowers which are the core focus of a flower-collecting game. Galactic Arms Race (Hastings, Guha, and Stanley 2009), on the other hand, generates the colors and trajectories of weapons in a space shooter game. Both examples have a wide expressive range as they primarily target novelty, with uninteresting or unwanted visuals being pruned by the player via interactive evolution. In order to impart a sense of visual appreciation to the generator, Liapis, Yannakakis, and Togelius (2012) assigned several dimensions of visual taste inspired by cognitive research on “universal” properties of beauty (Arnheim 2004); the algorithm was able to evaluate generated spaceships based on size, simplicity, balance and symmetry and adjust the generative rules via artificial evolution. The model of visual taste could further be adapted to a human user, with visual properties prominent in chosen spaceships being targeted in the next evolutionary run. In terms of creativity, this spaceship generator targeted typicality via vertical symmetry and constraints on what constitutes a valid spaceship, as well as quality via the computational model of visual taste. Beyond generating in-game entities, Howlett, Colton, and Browne (2010) generate pixel shaders which substantially change the appearance of a game scene, pointing to a broad expressive range. The shaders’ novelty is significant, while their quality is based on a user’s a priori specification of target hues; however, the resulting scenes are often too bright and objects are hard to make out, pointing to a low typicality with traditional game shaders.