The most popular software tools – in both social and commercial terms – are those that allow users to express themselves creatively, either by facilitating the construction of creative outputs (documents, images, videos, web pages, etc.) or by facilitating the sharing of these outputs (via blogs, social networks, etc.). However, no matter how powerful or how rich in features the software happens to be, the user remains the “creator”, while the computer/software remains the “tool”. This is true whether one is using consumer-grade software like Microsoft Word or professional-grade software like Adobe Photoshop. In short, while these tools facilitate the creation of new digital artifacts, the creativity still resides entirely in the user.
Computational Creativity (CC) is an emerging field that studies and exploits the potential of computers to be more than feature-rich tools, and to act as autonomous creators and co-creators in their own right. In a CC system, the creative impetus comes from the machine, not the user, though in a hybrid CC system a joint impetus may come from both together. As a field, CC draws on research in Artificial Intelligence, Cognitive Science, Psychology and Social Anthropology to understand:
- What does it mean to be “creative”? Does creativity reside in the individual, in the process, in the product, or in a combination of all three together?
- How does creativity relate to expertise and necessitate domain knowledge?
- How does creativity exploit and subvert norms and expectations?
- How are the outputs of creativity judged and evaluated? How can we meaningfully measure creativity? What knowledge is needed (of the creator, of the process) before we can label a product as “creative”?
- What constitutes creativity in different domains and modalities?
- How does creativity emerge from group behavior and collective action?
- What cognitive paradigms offer the most explanatory theories of creativity (e.g., search in a conceptual space, conceptual blending, etc.)?
Of course, all of these questions are just as valid to the study of human creativity as they are to the study of machine creativity (e.g. see Amabile, 1983,1996; Bruner, 1962; Csíkszentmihályi, 1990,1996; Finke et al., 1992; Giora, 2002; Guilford, 1950,1967; Koestler, 1964; Loewenstein and Heath, 2009; O’Toole, 1994; Rozin et al. 2006; Sawyer, 2003, 2006; Sternberg, 1995, 1996, 2003). What makes CC different is that it adopts an explicitly algorithmic perspective on creativity, and seeks to tie down the study of creative behavior to specific processes, algorithms and knowledge structures (e.g. see Boden, 1999; Colton, 2008; Duch, 2007; Hofstadter et al., 1995; Indurkhya, 1992; McCarthy, 1999; McCorduck, 1991; Minsky, 1980; Newell et al., 1963; Pereira, 2007; Petrič et al., 2010; Raskin, 1985; Ritchie, 1999,2006; Veale et at. 1999, 2000; Veale, 2004, 2006a, 2006b, 2007, 2011, 2012; Wiggins, 2006a,2006b). The goal of CC is not just to theorize about the generative capabilities of humans and machines, but to build working systems that embody these theoretical insights in engineering reality. As such, CC is an engineering discipline and an experimental science. in which progress is made by turning insights into applications that can be experimentally tested and evaluated. The purpose of these applications is to create novel artifacts – stories, poems, metaphors, riddles, jokes, paintings, musical compositions, games, etc. – in which a large measure of the perceived creativity is credited directly to the machine. We believe that the future of intelligent computers lies in transforming our computers from passive tools into active co-creators, and that Computational Creativity is the field that can make this transformation a reality.
Creativity is a multi-faceted phenomenon that manifests itself in different guises in different domains. Thus, creativity in the sports domain (e.g. consider a team sport like Soccer, or a strategic sport like Chess) is clearly different to creativity in the arts domain (e.g. consider Painting or Poetry), though there are enough similarities for exemplary outcomes in each domain to be deserving of the same label, “creative”. This heterogeneity makes creativity a notoriously difficult concept to pin down in formal terms, and definitions that favor one area of human activity (like art) are unlikely to do justice to other areas (like science). Our definitions of creativity – and a great many have been considered in the scientific literature – are no more than accepted conventions, and it is in the very nature of creativity to bend and subvert these conventions. As such, CC researchers tend not to trade in definitions of creativity per se, but to focus on those aspects of behavior – in both humans and computers – that produce outputs that are novel or surprising and which yield unexpected value. It was in this vein that Newell, Shaw and Simon (1963) suggested their four different criteria for categorizing a given answer or solution as creative:
- 1. The answer has novelty and usefulness (either for the individual or society)
- 2. The answer demands that we reject ideas we had previously accepted
- 3. The answer results from intense motivation and persistence
- 4. The answer comes from clarifying a problem that was originally vague
Though obviously incomplete, each criterion is instinctively appealing because each recapitulates in more literal terms the meaning of a conventional metaphor of creativity. For instance, (1) simply reflects the folk view that creative solutions should be “fresh” and “innovative”, perhaps even “ground breaking”; (2) suggests that one must “think outside the box” and reject conventional categories and labels; (3) suggests that to be creative, one must expend copious amounts of “mental energy” in tenaciously exploring a particular avenue or wide-ranging conceptual space; and (4) espouses the common belief that creativity requires “illumination” and “insight”.
Given the obvious difficulties in distilling a pure definition of creativity – “pure”, at least, in the sense of being metaphor-free and grounded in objective fact rather than in human intuition – CC researchers pursue one or all of the following approaches:
- 1. They ignore the need to define the phenomenon objectively, to perhaps employ instead an ad-hoc definition of convenience; this allows practical work on creative systems to continue, perhaps even to an extent that practical results can eventually inform a fuller and more satisfying definition of creativity.
- 2. They embrace the metaphorical foundations of creativity, to identify processes and mechanisms within our repertoire of computational algorithms and representations that best seem to embody these folk metaphors.
- 3. They identify an archetypal area of creative endeavor and attempt to model that area computationally. In such work, a formal definition is not needed to underwrite the research as “creative”. However, as in (1) above, the outputs of this research may then feed back into a later formalization of creativity.
These three alternatives summarize, more or less, the research assumptions made by contemporary CC researchers. Because CC is grounded in engineering and experimentation, CC systems produce concrete outputs whose novelty and value can be assessed by human judges in the absence of any formal definition of creativity. Though CC believes that machines can exhibit creativity on their own terms, perhaps even by using algorithms and knowledge structures that are different to those used by humans, the goal of CC is for machines to exhibit human-level creativity that humans will also perceive as “creative.”
While humans and computers can be creative in the absence of a formal definition of how they are being creative, both still need a level of self-understanding and critical awareness to justify the use of the label “creative”. Computers which generate outputs for an external user to evaluate are merely generative in their behavior, and mere generation does not rise to the level of human creativity. Rather, the generation of outputs must be coupled with an awareness of the value of the output in terms of its novelty and its utility. A creative computer must embody a particular view of creativity that the computer itself understands, so that the computer can justify its outputs much as a human creator would do. Such a computer cannot be a dumb savant that naively flings outputs at an audience. Crucially, it must exhibit an ability to filter its outputs for quality, and just as importantly, it must exhibit an ability to articulate why these outputs may have unexpected value for its audience. Thus, in the words of Sternberg and Lubart’s (1995) investment theory of creativity, a creative computer must be able to articulate its sense of how a particular product or idea can be “bought low and sold high”.
Though developments in the field of AI have become fixtures of the technological landscape (e.g., machine translation, speech recognition, and most impressively, automated chess; for an analysis of the latter, see Campbell et al., 2002), humans still instinctively cling to the idea that creativity is a uniquely human (or at least, biological) preserve. In this view, when computers do exhibit apparent creativity, this arises from some specifiable slice of the programmer’s own creativity having been imprinted onto the algorithmic workings of the system. In CC research this idea is known as pastiche, since such computers unknowingly resort to the same kinds of stylistic mimicry that is knowingly exploited by uncreative human artists. For instance, careful musicological analysis of the structure of Bach cantatas can allow a programmer to write software that generates its own novel cantatas in the style of Bach. Though these outputs may fool the human listener, and even delight the unsophisticated ear, they are the product of a system that mimics rather than creates. Such a system has no awareness of its inherent limitations, nor does it have any conceptual input into the hardwired (albeit pseudorandom) processes that it follows. Such systems are more akin to skilled forgers than to creative artists; while they can expertly mimic and recycle, they cannot innovate and they cannot surprise. Moreover, because they explore a pre-defined sweet-spot in the space of possible outputs, pastiche systems take no risks, always produce well-formed outputs, and have no need to self-evaluate or learn from their failures.
Of course, pastiche has its place, both in human and machine creativity. One can learn from pastiche, and even good creators occasionally lapse into pastiche (recognizing this tendency in himself, Picasso once noted of his own paintings (quoted in Koestler, 1964) that “Sometimes I paint fakes”). Pastiche thus serves as a useful boundary case for computational creativity. Indeed, there are cases where pastiche is precisely what the human co-creator desires (e.g. “let’s explore more variations on this theme.”). Pastiche-based systems are a useful starting point for the computational exploration of creativity, but the goal of CC as a field is to actively transcend pastiche, to demonstrate that computers are capable of true, human-level creativity.
CC is a multidisciplinary research field that sits at the intersection of the fields of AI, Cognitive Science, Psychology, Linguistics, Anthropology and other human-centered sciences. Given its focus on system-building, the field has most in common with AI, and builds on many of the same foundations, such as intelligent search in a conceptual/problem/state space. Nonetheless, the field has a distinctive character of its own, which shapes its use of ideas and techniques from other fields. For instance, CC views creativity as arising from more than a merely systematic search of a space of possibilities. Rather, it recognizes that these spaces are deeply-rutted with conventional pathways, and that creativity arises from how an intelligent agent knowingly exploits or subverts these conventions. Thus, Margaret Boden (1990) suggests ways in which creativity might arise from the exploration of a conceptual space, while Wiggins (2006a,b) has formalized the CC components of this perspective.
A visual representation of search in a conceptual space is provided in Figure 1. Here, flowers depict acceptable solutions – goal states at which a search can profitably terminate – while footprints illustrate the paths taken by a cognitive agent as it explores the space. Since this model projects physical search into mental spaces, we can understand mental agility as the cognitive equivalent of those qualities that are desirable for an agile physical search. For instance, one often needs to backtrack gracefully when at a dead-end, and shift smoothly to an alternate avenue of search.
Figure 1. Search in a state-space. (from Veale (2012), “Exploding the Creativity Myth”). Flowers represent acceptable goal-states / solutions, while footprints illustrate the paths pursued via various cognitive agents.
Adaptability, in particular, appears to be a highly salient quality of creative behavior. Boden (1990) offers an intriguing view of adaptive creativity, of a kind that not only delivers surprising solutions to a problem, but that also changes the way we view the problem itself. Boden argues that one should distinguish exploratory creativity – of the kind visualized in Figures 1 and 2 – from transformational creativity. While the former explores the space as it is defined by the problem, looking for previously undiscovered or unappreciated states of unexpectedly high value, the latter actively transforms the space, thereby re-defining the very criteria of value that drive the search in the first place.
Figure 2. A creative searcher (shown here as a bare-footed explorer) finds novel ways to navigate a search space, by e.g., looking in hard-to-reach areas, or by identifying unconventional connections between states that previously did not appear connected. (also from Veale (2012)).
Boden cites the development of atonal music as a dramatic example of transformational creativity, and one can also point to key developments in science, such as the transformational shift from a Newtonian (absolute) to Einsteinian (relativistic) world-view, or from a classical (determinate) to quantum-mechanical (indeterminate) conception of reality. When searching through a space, whether that space is physical or abstract, a searcher can either contort itself to fit the constraints of the space, or as shown in Figure 3, contort the space to fit the needs and values of the searcher.
Figure 3. Transformational thinkers alter the very space that they are exploring, to identify high-value targets that lie outside the original space, and which would not have been considered solutions in the original formulation of the problem. (also from Veale (2012)).
Transformations of the kind analyzed by Boden are the exception rather than the rule in creativity, in either its small-C (everyday creativity on a mundane scale) or big-C (exemplary Creativity on a historical scale) guises. One finds a more commonplace form of agile exploration of a state space in the narrative jokes that are the common currency of social interaction. Jokes exploit the fact that we all navigate through shared state spaces in our everyday lives, to explain the events in the world around us, and to understand the behaviors of our friends and colleagues. These shared spaces have well-trodden pathways that correspond to the common-sense norms of conventional thought processes, but these rutted paths do not always offer the quickest or surest routes to a solution. In cases when the best path to a solution is circuitous and non-obvious, mental agility is not a matter of speed but of sure-footedness. The shortest path can sometimes lead to incongruity and failure.
Figure 4. Some state-spaces are deliberately constructed to be misleading, and the most obvious or conventional path to the solution can lead to a surprising dead end. A sure-footed explorer who knows the space will take a more circuitous route. (From Veale (2012), “Exploding the Creativity Myth”).
Jokes employ state-spaces that have been deliberately warped, so as to fool the unsuspecting explorer into believing that the quickest and most conventional route is also the most intelligent route. In other words, jokes subvert the logic of intelligent search in a state space, and thereby demonstrate the limits of conventionalized thought processes (see Minsky, 1980). The mathematician John Allen Paulos (1982) used the framework of catastrophe theory to characterize the kinds of warped spaces that are most used in narrative jokes: as shown in Figure 4, these typically contain an unexpected “kink” or discontinuity that corresponds to a surprising gap in the logic of the narrative. Explorers who jump to conclusions by pursuing the path of the discontinuity can thus be humbled and surprised by their unthinking use of conventional logic.
It is on the computational treatment of discontinuity, incongruity and contradiction that CC most distinguishes itself from AI as a discipline. In a conventional state-space search, contradictions are viewed as dead-ends from which a computational agent must backtrack. AI operates on the assumption that search is important but the avoidance of search is more important still, so contradictions serve as useful boundaries with which to limit an otherwise costly search. CC, however, views incongruity and contradiction as opportunities for further search, to determine whether an anomaly can be resolved on another level of representation to yield outputs that are surprisingly meaningful (See Suls, 1972; Raskin, 1985; Ritchie, 1999. Resolvable contradictions of this kind underpin not just the incongruity of jokes, but the absurdity of surrealist paintings, the semantic tension of metaphors, the pragmatic insincerity of ironic statements, the plot twists of mystery stories, and even the unexpected discoveries of mathematics and science. In his wide-ranging theory of “Bisociation”, Arthur Koestler (1964) argued that the creativity of these diverse phenomena emerges from the collision of two seemingly incompatible frames of reference. Koestler’s ideas have formed the basis of Fauconnier and Turner’s (1998, 2002) influential theory of Conceptual Blending, though it falls mainly to researchers in CC to anchor these ideas in the algorithmic specificity that can only come from computational model-building (e.g., see Veale and O’Donoghue, 2000; Pereira, 2007; Veale, 2012).
Computational Creativity as a Discipline
PROSECCO is anchored in the belief that our computers can be more than mere “tools”, and can actually rise to the level of co-creators that proactively share the creative responsibility with a human peer. As co-creators, our computers will be capable both of generating their own ideas and of framing those ideas in the appropriate modalities (e.g. in language, image, sound). Our vision of a co-creator is more than a facilitator or enabler for human creativity (in the sense e.g. that Microsoft Word or Adobe Photoshop facilitates content creation, or in the sense that Facebook facilitates collaborative creation), but envisions a largely autonomous agent that explores its own conceptual spaces and expresses its own ideas in its own terms. CC conducts application-driven research into this notion of a computational co-creator in two guises: autonomous systems that receive little or no human input; and semi-autonomous hybrid systems that interact with humans as peers.
This view is shaped not by a desire to replace humans with machines, nor by a perceived lack of human creativity in modern society, but by the belief that large amounts of human creativity remain untapped because users lack the appropriate co-creation software. No matter how richly featured a conventional software tool may be, users are still forced to start from a blank page or an empty screen, or a pre-determined template that simply encourages recycling and pastiche. CC systems will not only suggest ideas to users, but articulate, demonstrate and critique those ideas as would a human teammate. By providing humans with partners that can share the creative responsibilities and the creative credit, the goal is not to replace human creativity, but to engage and foster human creativity as only a creative equal can.
This is an ambitious vision that will take decades to fully realize, though in the interim, the CC field continues to build systems that serve useful (and steadily improving) creation and co-creation roles. To meet these challenges, CC must go from being a growing area of niche interest to being a scientific discipline in its own right. The challenges are both organizational and research-based. As a field, we must coalesce around a clear set of principles, an unambiguous and comprehensive terminology, and a canonical set of techniques, metrics and approaches to evaluation (e.g. see Ritchie, 2007). We must consolidate our own identify as a field while actively engaging with neighboring disciplines.
Computational Creativity has long been an implicit element of AI research, one that comes to the fore when AI addresses topics of an obviously creative bent, such as painting (e.g. Harold Cohen’s Aaron), analogical reasoning (e.g. Dedre Gentner’s SMT/SME, 1983), music generation (e.g. David Cope’s EMI), story telling (e.g. Meehan’s TALE-SPIN, Turner’s MINSTREL), joke generation (e.g. Binsted and Ritchie, 1996), poetry generation (e.g. Gervás, 2001), metaphor generation (e.g. Veale, 2012) and even painting generation (e.g. Colton, 2008). However, most of this work was generally seen as AI work, and not as a product of a specific movement for computational creativity.
CC first began to acquire a distinctive identity of its own with the organization of a series of workshops and symposia that were explicitly dedicated to the issues of creativity in computers, such as the 2nd Mind conference (1997) and events co-located at AISB (1999-2003), ICCBR (2001), ECAI (2002), EuroGP (2003 & 2004), IJCAI (2003), ECCBR (2004), IJCAI (2005) and ECAI (2006). In 2007 the community re-launched the International Joint Workshop on Computational Creativity (IJWCC) as a stand-alone event, and since 2010 this event has became an annual conference (ICCC), with 2010 in Lisbon, 2011 in Mexico City and 2012 in Dublin. These events were organized by a small core of researchers who first coalesced as a working group through EU COST action 282 (Knowledge Exploration in Science and Technology). Since then, this core group has pursued a range of community-building activities, from the annual organization of IJWCC/ICCC to the creation and maintenance of a comprehensive Wikipedia entry on Computational Creativity to the publication of several special journal issues on CC (such as Knowledge-Based Systems, 9(7), 2006; New Generation Computing, 24(3), 2006; and Minds and Machines, 20(4), 2010). The history of the IJWCC/ICCC events is described in a special issue of AI Magazine on CC (Cardoso, Veale and Wiggins, 2009).
Though stalwart, the community remains small. ICCC 2011 in Mexico City attracted 60 attendees, while attendance at ICCC 2012 in Dublin is projected at 80 attendees. Though we are encouraged by the steady growth in attendance, it seems clear that we are not reaching many of the researchers in other disciplines that we need to reach, nor indeed many of the researchers in AI whose work is more closely aligned to CC than to traditional AI. To reach the latter, CC researchers continue to publish in specific CC events like ICCC and in other, more mainstream conferences (such IJCAI, ECAI, ACL, etc.). Likewise, special journal issues are designed to reach readers who would not otherwise recognize CC as a distinct field. In addition, various CC researchers make available their results as online applications that can be publically accessed on the web, while some promote CC as a field at Dagstuhl events, summer schools (such as ESSLLI), autumn schools, and other forums. However, these strategies are only modestly effective, given the number of quality publications that can be generated in a single year by a small group of researchers. Broader dissemination channels are needed, both to establish CC as a distinct field and to promote the benefits of being part of the CC community (such as a growing scientific literature; theoretical frameworks; common evaluation practices; shared resources; cross-fertilization events; etc.). Perhaps more importantly still, we require more active means to reach other disciplines that can engage in a mutually-enriching relationship with CC, such as psychology, anthropology, literary studies, art theory, musicology, design, etc. And, of course, we need to reach developers in industry, who can most benefit from the technologies that CC provides, or indeed influence and contribute to the development of new CC technologies and techniques.
However, most importantly of all, we need a means of reaching future and emerging researchers in CC while they are still in the development stages of their education. That is, we need to reach graduate students in a variety of disciplines to create the next wave of CC researchers. By reaching these students at a time where their PhD plans are still at a formative stage, we can bring the necessary knowledge on design, psychology, anthropology, sociology, art, language, music, science and math into the field, and demonstrate that CC is a research area in which students can pursue a cross-disciplinary exploration of creativity at the intersection of experimental science, system-building engineering and the humanities.
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