Kinetic Imaginations: Exploring thePossibilities of Combining AI and Dance

Alexander Berman, Valencia James

AbstractThis paper presents an interdisciplinary projectwhich aims at cross-fertilizing dance with artificialintelligence. It utilizes AI as an approach to ex-plore and unveil new territories of possible dancemovements. Statistical analyzes of recorded humandance movements provide the foundation for a sys-tem that learns poses from human dancers, extendsthem with novel variations and creates new move-ment sequences. The system provides dancers witha tool for exploring possible movements and find-ing inspiration from motion sequences generatedautomatically in real time in the form of an impro-vising avatar. Experiences of bringing the avatarto the studio as a virtual dance partner indicate theusefulness of the software as a tool for kinetic ex-ploration.In addition to these artistic results, the experimentsalso raise questions about how AI generally relatesto artistic agency and creativity. Is the improvisingavatar truly creative, or is it merely some kind of ex-tension of the dancer or the AI researcher? By ana-lyzing the developed platform as a framework forexploration of conceptual movement spaces, andby considering the interaction between dancer, re-searcher and software, some possible interpreta-tions of this particular kind of creative process canbe offered.

1 IntroductionCan computers be creative and artistically inventive? Whatkind of exchanges or collaborations between artists, audi-ence and artificial intelligences might be useful or interest-ing? These are some of the motivating questions behind re-search into so called “computational creativity”. This paperapplies these issues in the area of dance and choreography andinvestigates whether AI can be kinetically inventive and if so,what this might mean for the interaction between dancers andintelligent software.

The results and discussions presented in the paper emanatefrom experiments performed in the interdisciplinary projectAI am bringing together a dancer, researchers and creative

technologists. The project investigates how AI can unveil newterritories of possible dance movements, thereby supportingthe creative process of dancers and choreographers.

The paper begins by briefly discussing how kinetic creativ-ity may be characterized in formal and abstract terms as anexploration of a conceptual space. This notion entails thepossibility of simulating and implementing kinetic imagina-tion in a computer, at least hypothetically. Formal and com-putational approaches to dance and choreography are thendiscussed, illustrating how formalization and abstraction ofmovement can unlock kinetic spaces of imagination in the-ory and practice. Some examples of how choreographers anddancers have applied software and AI in choreography arealso given.

The authors’ specific take on kinetically creative AI is thenoutlined: its assumptions and simplifications are briefly dis-cussed as well as the statistical approach to movement anal-ysis, based on dimensionality reduction of motion data andexploration of pose maps. The concept of a virtual dancingavatar which improvises in real time is presented, and expe-riences of bringing the avatar to the dance studio as a virtualdance partner are reported and discussed. This is followed byan analysis of the creative interplay between human dancerand avatar. Finally, some conclusions are presented.

2 Kinetic creativity in humans and computersCreativity is sometimes described theoretically as an explo-ration of conceptual spaces [Boden, 2004]. Within this theo-retical framework, the act of composing music, designing ob-jects or making any other form of art can partly be understoodas an investigation of possibilities distributed in an abstractspace. Metaphorically, the artist walks along a landscape ofboth habitual and hitherto unexplored regions. By examin-ing the contents of the conceptual map by various techniques,unexpected and potentially useful outcomes can be encoun-tered.

In the context of dance and choreography, this notion ofcreativity raises a crucial issue: Can one define a conceptualspace of possible dance movements? This is a complex andopen-ended question which cannot be answered without clar-ifying what one means by “possible” and “dance movement”.Do all mathematically conceivable configurations of limbsand joints represent “possible” poses, or should physiologicalor other constraints be considered? If so, which constraints,

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015)

2431

and how? Furthermore, movements consist not only of posesand spatiality, but also dynamics. Can temporal aspects ofmovement be represented conceptually as spaces?

This paper will briefly touch on these issues but does in noway attempt to resolve them. However, some general reflec-tions about the very idea of “conceptual movement spaces”can be made. First, it is worth noting that many contempo-rary dance theorists and choreographers approach movementin formal and systematic ways, conceiving of body and phys-ical space in abstract and geometrical terms. These system-atic approaches can partly be understood as efforts to con-struct conceptual spaces of possible movements. Arguably,this is one of the artistic benefits of formalizing kinetics: tounveil movement possibilities that might not appear “natu-rally”, by mere “intuition” and without deliberate intellectualeffort. Second, the notion of conceptual spaces lends itselfquite naturally to AI and design of computational systems.For example, exploration of “ideas” (locations in conceptualspace) can be implemented computationally as simple math-ematical operations.1

3 Formal and computational choreographyThe creative choreographic process typically starts with a pe-riod of movement exploration around a chosen theme rangingfrom a piece of music, a narrative, to simply a movement idea.At this stage, practitioners find ways to tap into their primal,intuitive sense, what can be referred to as “embodied knowl-edge” or “the body’s wisdom”. A variety of techniques canbe used such as improvisation and specific tasks or games asa means to stimulate new ways of moving and making. Onecommon approach in movement exploration is to investigatethe possibilities of the body in space.

There are a number of facets through which space can beexplored through improvisation. One is its geometrical man-ifestations and their consequent expressive possibilities. Thisapproach to the study of movement was first coined by RudolfLaban as Choreutics or Space Harmony. According to Laban,the body is seen in space as “living architecture”, linking therelationship of joints and limbs to each other with their possi-ble relations to imagined polygons and solids. The limbs cantrace shapes, move within and between vertical and horizon-tal planes, and inhabit dynamic relations of the diagonals inan imagined cube. Laban proposes the relation between spaceand its dynamic counterpart. For example, the lower level ofspace is associated with heaviness and high diagonals withlightness [Laban, 1966]. A dancer may explore these con-cepts of space through improvisation and then build on theresulting movements to create choreography.

The division of space into the basic components providesa comprehensive structure through which all movement canbe analyzed and explored. Creation can take place in a con-ceptual space where the body reacts and interacts with imag-ined objects and relations with their bodies. This work hasarguably been the fundamental influence that shapes what

1This does not imply that creativity as such is a trivial phe-nomenon, reducible to spatial exploration; Boden [Boden, 1998,p. 349] also emphasizes the importance of domain knowledge andexpertise.

we recognize today as contemporary dance [Preston-Dunlop,1998, p. 270-5] and has shaped the choreographic stylesof famous names such as William Forsythe, whose work isamong the pioneering efforts to present movement and chore-ographic strategies as models for interdisciplinary study.

Forsythe has revolutionized the ballet tradition by applyingLaban’s spatial ideas of tracing and transposing movementto traditional methods of choreography. Some ideas includetracing U’s and O’s in relation to three-dimensional axes, alsotracing everyday objects and manipulating them with differ-ent body parts. The result is an abstract way of thinking aboutspace, applying it to one’s own body and relating to othersin space. Forsythe’s ideas have been released as the CD-ROM Improvisation Technologies (1995), where the ideas arevisually presented to aid the user’s imagination. Forsythelater presented the interactive website Synchronous Objects(2009), a cross-disciplinary investigation into his piece OneFlat Thing reproduced (2000) in collaboration with scientistsand designers at Ohio State University. The work analyzesthe choreographic structures to produce visual and annotativerepresentations, thus making the case for the application ofchoreographic knowledge in other fields.

However, it is important to note that use of computers to vi-sualize possibilities of space and time was pioneered beforethis. In the 1980s Merce Cunningham started choreograph-ing with the aid of the computer software LifeForms, devel-oped at the Simon Fraser University. The software worked asa “visual idea generator” through which Cunningham couldview and manipulate movement poses and sequences as wellas combine multiple bodies and phrases in digital space andtime [Schiphorst, 1993]. In 1992, Trackers was premiered ashis first work choreographed with the aid of the software.

Cunningham continued to experiment with other technolo-gies such as real-time motion capture and AI within per-formance. From his collaboration with Open Ended Group(OEG), Loops (2001) is an example in which both were em-ployed at once. The movement of Cunningham’s hands wasrecorded and simultaneously analyzed by an AI, which au-tonomously determined how the resulting visuals would be-have.

OEG’s contribution to the use of AI in live dance perfor-mance has revolutionized the possibilities of exchange be-tween science, technology and dance in the past fifteen years.Other choreographer’s that have benefited from their exper-tise include Trisha Brown, Bill T. Jones and Wayne McGre-gor.

In the collaboration of OEG and McGregor, AI and chore-ography are fully merged into what they call the Choreo-graphic Language Agent (CLA). CLA is a software that usesa language system with which the choreographer instructs hisdancers to generate its own movement. The input to the sys-tem is a sentence which produces an animation in response.CLA has been used in rehearsal as well as development [de-Lahunta, 2009]. The abstract geometric forms generated bythe CLA software have recently been replaced by a morehuman-like shape, used as an “eleventh dancer” in the studio[Rothwell, 2014].

Another case of computer-aided choreography is Scuddle,a tool which generates incomplete movement material based

2432

on a Laban inspired formalism [Carlson et al., 2011].The examples above illustrate how dancers and choreogra-

phers use formal systems and computer software as choreo-graphic tools and catalysts for enhanced creativity. The pre-sented approach positions itself in the same tradition, but incontrast to previous work, our experiments center around animprovising, humanoid AI, acting as a virtual dance compan-ion. [McCormick et al., 2013] describe a similar approachwhere a dancing AI learns movements from its human part-ner. However, their AI is designed for movement recognitionrather than creative elaboration.

4 Pose mapsThe pursuit of creating a kinetically creative AI can be ad-dressed scientifically in different ways. Here we approach theproblem statistically and assume that human dancers have astyle or repertoire which can be characterized as a tendency toperform certain poses. By recording and analyzing a dancer’smovements and creating a statistical model of them, we as-sume that the dancer’s style – the “signal” in the data – canbe captured, at least to some extent. This model can thenserve as a basis for exploring novel variations of observedmovements.

For purposes of simplification, we focus on the posturalcontent of movements rather than their temporal dynamics.In other words, the statistical analysis only deals with howlimbs and joints are configured in particular moments, ratherthan how poses within a movement develop over time.

By performing a statistical analysis which reduces thecomplexity of the training data, a “map” of possible posesis created. This pose map preserves topological relations inthe training data and contains regions of observed as well asunfamiliar poses. A central notion in this approach is that itenables exploration of possible movements. In other words,the pose map can serve as a conceptual space of possibilities.

In concrete terms, the analysis consists of the followingsteps. First, recorded movements are stored as vectors con-taining orientation data for each joint. 3D orientations be-tween joints are represented by unit quaternions (4 values).Secondly, the dimensionality of the data is reduced by nonlin-ear kernel principal component analysis (KPCA) [Scholkopfet al., 1998]. A more detailed account of the method andsome of its limitations is given in [Berman and James, 2014].

For the purposes of our experiments, a database was cre-ated by recording about 5 minutes of material. The moveswere performed by the team’s dancer and were derived fromposes that relate to certain spatial points, and the move-ment that arises from moving back and forth between theseposes. This approach is directly influenced by Rudolf La-ban’s Choreutics or Space Harmony method [Laban, 1966].

A skeleton model with 52 joints was used, out of which 21finger joints with static orientations were excluded from theanalysis, yielding 31 × 4 = 124 input dimensions. By KPCAwe can reduce the input dimensionality to an arbitrary numberof map dimensions. The exact choice depends on the contextand can be guided experimentally, taking into account variousconsiderations such as accuracy versus simplification.

Figure 1 shows the contents of a pose map and illustrates

Figure 1: An example of a 2D pose map and a generatedmovement path. The low dimensionality enables a graspableillustration; in the actual experiments, a 7D map was used.The green area represents the region of observed poses, whilethe surrounding areas contain poses without any direct equiv-alents in the training data. The blue dotted line represents anexample of an automatically generated trajectory with a highnovelty parameter. As seen in the illustration, the noveltyconstraint causes the path to stretch beyond observed terri-tories. (The visualization uses a grid of poses but the actualpose map is an unbounded continuous space.)

several of its typical characteristics. First, the distribution ofposes embodies a topological structure where postural sim-ilarity is reflected in distance across the map. Second, themap accommodates not only examples that are representativefor the training set, but also less typical ones. This propertyof PCA distinguishes it from some other dimensionality re-duction techniques such as self-organizing maps [Kohonen,2001] and is highly relevant for our purposes.

5 Map explorationOur tool allows dancers and other users to manually ex-plore pose maps of arbitrary dimensionality. Furthermore,a method for automatic map exploration has also been devel-oped. Trajectories across the map are generated by an algo-rithm and can then be synthesized as full movements by se-quencing the constitutive poses denoted by the path. This al-gorithm constitutes a kind of automatic “improvisation” andhas been designed to satisfy several constraints. Primarily,it should be able to yield pose combinations that have notbeen observed in the original data. Furthermore, the algo-rithm should produce somewhat realistic and familiar outputby favoring familiar regions of the map. On the other hand, itshould also permit exposure of novel poses beyond observed

2433

territories. Additionally, the trade-off between familiarity andnovelty should be guided by a parameter, enabling users toexperiment with different options. Finally, the length of gen-erated paths should be controllable, and their shape shouldyield a desirable result when intermediate poses are alignedin time; sharp twists or turns may result in jagged or unnaturalmovement.

The requirements were satisfied by an algorithm based onrandomness, attraction to observed poses (familiarity) and de-viation from observed poses (novelty):

1. Select a random observed pose in the map as the depar-ture point p

2. Generate a set of destination candidates {qi}, whereeach candidate is a random observed pose plus a randomvector of magnitude N (the “novelty” parameter)

3. Choose the destination q as the candidate among {qi}whose distance to p has the smallest difference from thepreferred distance E (the “extension” parameter)

4. Choose some intermediate points between p and q,where each intermediate point lies between a point onthe straight line between p and q and its nearest obser-vation in the map (closer to the nearest observation forlower N values)

5. Smooth the resulting path using spline interpolation6. Create the next trajectory by treating the current destina-

tion as a departure and repeating steps 2-5The method allows movements to be generated and ren-

dered graphically in real time in the form of a dancingavatar.2 By adjoining paths so that the endpoint of onetrajectory becomes the departure for the subsequent one, acontinuous stream of movement is obtained. “Foot skat-ing”, resulting from a lack of physical constraints, is elim-inated with a simplistic friction model. A video showingan example of a generated motion sequence can be found athttp://tiny.cc/kinetic-imag

The example stems from a pose map of 7 dimensions,a value which was reached experimentally. As can be ex-pected, the map dimensionality governs the avatar’s degree offreedom. Lower values impose more dependencies betweenjoints and their orientations. Consequently, a small numberof dimensions constrains the postural repertoire, while highervalues cause unnatural and extreme poses.

6 AssessmentThe method developed within the project was assessed in thestudio by the team’s dancer. Her specialty lies in moderndance and improvisational techniques for movement explo-ration and performance. She is also a budding dance-maker,with experience in composing short works. The dancer wasactively involved in the research from its inception. The as-sessment consisted of a number of experiments aiming at in-vestigating the AI’s usefulness as a creative catalyst, and is

2In comparison to a previous version of the algorithm [Bermanand James, 2014], the new version honors the extension parameterbetter, thereby providing more fine-grained control over the behav-ior.

based on her own subjective analysis of her sensations andperception of new ways of moving and approaching move-ment. The dancer recorded her findings through note-takingand video.

The software was assessed as an improvisational and com-positional tool. In the improvisation experiment, the avatar’smovements were used as visual stimuli from which thedancer’s own movements could originate. The avatar’s de-gree of novelty, extension and speed were manipulated andthe effects of the parameter variations on the generated move-ment observed. The dancer noted how freely her movementideas flowed as a result of the stimulus. Video recordingswere taken so that she could assess her own improvisation.

The next stage of the experiment focused on compositionalstrategies referred to by the dancer as choreogravatar, freeassociation and spatial blueprint. For choreogravatar, thedancer learned the avatar’s movements as she would froma choreographer. A video of the avatar’s improvisation wasrecorded, with the novelty parameters set to minimum to en-sure that all movements would be humanly achievable. Thedancer attempted to learn the movements as precisely as pos-sible. This second experiment resulted in two movementphrases. A structured improvisation was then created basedon movements learned in the previous phrases, to further in-vestigate their possible expressive qualities.

In free association, a phrase was created based on thekinaesthetic memory of learning and improvising with theavatar. This was followed by a task of selecting or “cherry-picking” specific movements observed from the avatar ac-cording to the interest or surprise they evoked, and incorpo-rating them into the free-associated phrase.

Spatial blueprint shifted focus from the avatar’s visualform to the pose map, using it as a guide for generating move-ment. The software contained a window showing the real-time journey of the AI through the pose map where the cur-rent path and location was highlighted. Ten trajectories werechosen and drawn as ten separate frames in the order that theyappeared. The shape of the pathway and the direction of theAI’s journey along it was noted. This information was inter-preted by tracing the pathway with varying combinations ofbody parts and transposing the 2D information into 3D spaceintuitively. The pathways were also explored as floor plansdirecting the location of the body as a single unit. A fourthphrase was created.

7 ObservationsThe dancer was surprised by the complexity of the avatar’smovements in contrast to the simplicity of the dataset, andexpressed her eagerness to use it in the studio. She observedthat with parameters set at low novelty, the avatar performedmovements recognizable from the dataset but in a differentorder and interspersed with movements never seen before.Combined with a medium extension, she reported a “cau-tious” quality in its improvisation. When extension was in-creased, the dancer noted that the movement became moreexpansive, taking recognizable poses to unnatural extremes.

The dancer records that innovative behavior in the avatarincreased with the novelty parameter. With maximum novelty

2434

and medium extension, the avatar’s movement is described asmore brave and reminiscent of that of a break dancer. Whenextension and novelty were at their maximum settings, theavatar’s movement went to extremes humanly impossible toreplicate. Even then, the dancer has found this to be inspiring.

It was observed that although the dataset was composed toreflect only spatial aspects of the movements, the algorithmproduced interesting variations in flow and rhythm. Thesedeviations contributed to the expressive appeal of the avatar’snovel behavior and hence increased its capability to inspirecreative responses.

In the improvisation experiment, the effect of the avatar’sstyle on the dancer’s was immediately felt. She began tosequence movements in unusual ways and incorporate theavatar’s irregular dynamic variations. The dancer respondedto the avatar’s extremes in its conceptual space with her ownexploration of the extremes of her physical space.

In the choreogravatar experiment, the dancer reported dif-ficulties and frustration when trying to learn phrases from theavatar. Glitches, along with the avatar’s lack of real-worldphysical limitations contributed to this. During this experi-ence, the dancer realized that in her pursuit of precise replica-tion, she had plagued herself with the “detail overload” thatSchiphorst refers to in her account of Cunninghams use ofLifeForms [Schiphorst, 1993]. The excessive attention todetail blocked the intuitive aspect of composition and con-sequently the iterative process characteristic of developingchoreography.

The dancer’s solution to detail overload was to use thelearned movements as a basis for a structured improvisation,in which she could explore the spatial and dynamic potentialof the movements in a broader sense. She investigated the ex-pressive possibilities of repetition and how some movementscan be made locomotive. During this approach, the dancernoted a sense of flow.

The free-association and spatial blueprint experiments al-lowed the most intuitive approach to the avatar’s movements,facilitating greater creative transformation and flow. Thephrases derived from these methods were the most expres-sive and innovative, best demonstrating the creative potentialof human-algorithm collaboration.

The experiments described above resulted in four move-ment phrases of varying lengths. They can be used as mo-tifs in further choreographic development. Each motif can bevaried to explore countless spatial possibilities, with dynamicqualities and rhythmic variations added to produce a more nu-anced and therefore expressive result. The use of pathways asfloor plans could be explored as a way of connecting the mo-tifs with each other or converting otherwise static movementinto locomotive movement.

It was found that the large quantity of spatial information inthe movements became boring without variation in dynamicsand rhythm. This was not surprising given the essential rela-tionship to space and dynamics as defined in Laban’s SpaceHarmony. However, the minimal variation of dynamics islinked to the capacity of the software and presents possibilityfor future development.

8 DiscussionOur experiments indicate that statistically derived pose mapsconstitute a useful basis for exploration of possible dancemovements. The dancer in the team reports various posi-tive outcomes and potentials for artistic applications of thiskinetic AI. Using the software, the dancer finds movementinspiration and is able to further evolve and extend ideas gen-erated by the AI. She can use kinetic material from the AI asbuilding blocks for new choreography.

The dancer’s experiences from using the AI in the studiovalidates the relevance of the approach, identifies weaknessesin the system, but also raises several questions of more theo-retical and philosophical nature. Is it meaningful to call thisAI creative or artistic? And how should one characterize theinteraction between dancer/choreographer and software?

In order to address these questions, we return to the notionof creativity as exploration of conceptual spaces. In moreconcrete terms, what is the space that the dancer and the AIexplore, and how do they do it? [Wiggins, 2006] provides aformal language for talking about such matters. His “CreativeSystems Framework” formalizes ideas from [Boden, 2004].In Wiggins’ framework, any creative system – biological ornot – essentially consists of the following components:

The universe of the creative system is the set of all possible“concepts”. In the present context, concepts are movements(or aspects of movements), and the universe therefore con-tains all possible movements. It is important to note that theuniverse is inhabited by both relevant concepts and those ofno particular interest or value.

A conceptual space is a subset of the universe, and resultsfrom a selection procedure which carves out regions of theuniverse to be explored. In the context of dance, the concep-tual space may consist of humanly realistic movements.

Like the universe, the conceptual space makes no distinc-tion between concepts on the basis of their artistic potential.This is the purpose of the evaluation function: to attributevalue to concepts by assessing their usefulness or relevance.

Finally, the exploration strategy describes how the concep-tual space is searched and explored. Typically, the explo-ration mechanism uses the evaluation function as a “guide”.But it may also use “blind” strategies which serve to increasethe chance of reaching distant but desirable concepts.

This formalization enables us to ask whether the AI is cre-ative. First of all, what is its conceptual space? The pose mappresented in section 4 seems to be an obvious candidate. It isconstructed by reducing the space of all possible poses (theuniverse), in our case by dimensionality reduction. Further-more, an algorithm exists for exploring the contents of thepose map.

But if pose maps are conceptual spaces in Wiggins’ terms,they are spaces of poses, not of movements. When the soft-ware “improvises” by traveling along a pose map, the journeycan be visualized as a movement by rendering the encoun-tered poses as an animation. However, neither the AI northe dancer evaluate the constitutive poses within the move-ments per se. Hence, the pose maps do not serve as concep-tual spaces in our experiments.

Another option is to conceive of pose maps as components

2435

of movement spaces. This is a much more abstract notion.Our approach treats movements as sequences of poses. Morespecifically, a conceivable movement is a pose sequence con-stituting a possible trajectory across a pose map.3 The move-ment space is the set of possible movements. In contrast to apose map, whose shape is a Euclidean space, the shape of amovement space is somewhat more difficult to conceive. Butin a formal sense, the notion as such is perfectly workable.

How is this conceptual movement space explored? Whenthe AI “improvises”, it generates movements as paths across apose map, “performs” individual movements and steps fromone movement to another. In other words, the AI “walks”across the movement space. In the meantime, the dancer canobserve the exploration as it unfolds and evaluate the encoun-tered movements. This formal characterization of the processseems to capture important aspects of the activity. Impor-tantly, it highlights the role of the dancer in the evaluationof the AI’s output. Arguably, if this is a creative system,then the system contains both the dancer and the AI. This hy-brid of human and software can perhaps be understood as ahuman-computer creative partnership [Kantosalo et al., 2014;Lubart, 2005] or a case of “distributed cognition” [Hollan etal., 2000].

8.1 Evaluation and embodimentAs reported by the dancer, movement ideas generated by theAI are evaluated not only by observing the avatar but alsoby trying to perform the moves physically. By embodyingthe ideas, the dancer interprets and implements them with herown body. In Wiggins’ model, embodiment fits into the no-tion of concept evaluation. But it may also be understood interms of conceptual and material space, as part of an interac-tion between ideas and material [Dahlstedt, 2012] – an aspectwhich seems crucial for understanding the creative process indance.

8.2 Transformational and meta creativitySo far, the analysis has focused on the observing and eval-uating role of the dancer in relation to the AI. In the actualexperiments, the interaction was more interactive. As de-scribed above, the dancer is able to experiment with differentparameters governing novelty, extension, etc. By doing so,she effectively alters the exploration strategy of the system.In other words, we are dealing with a kind of “explorationof possible explorations” or “creativity at the meta-level” inWiggins’ terms.

During the earlier phases of the experiments, the team alsoexperimented with different dimensionality reductions: linearversus nonlinear, and different number of PCA components.The team thereby modified the conceptual movement spaceitself, a process which Boden calls “transformational creativ-ity” [Boden, 1998].4 Furthermore, different variants of al-gorithms for path generation were explored. In other words,most components of the creative system have been subject to

3“Possible trajectory” here means a path that may be created bythe trajectory generation algorithm.

4For Wiggins, modification of the exploration strategy is anothercase of transformational creativity.

meta exploration. To this end, one may perhaps conceive ofthe whole team as parts of the creative system.

The interaction between humans and machine in the cur-rent setup can be developed further in many different direc-tions. For example, the dancer’s assessment of the move-ments are currently not fed back to the AI. The inclusion ofsuch a feedback mechanism could enable positive and nega-tive reinforcements of exploration strategies, thereby furtherenhancing the transformational creativity of the system as awhole.

9 ConclusionsThe convergence of dance and AI poses several questions:What kind of interactions and exchanges between human andvirtual dancers can be conceived? And how can AI contributeto the creative process of dancers and choreographers? Thispaper has presented an attempt to approach these questions bydeveloping and experimenting with a kinetically creative AI.Some conclusions from the work can be drawn. First, the per-formed experiments provide some empirical support for AI asa potentially useful tool for enhanced improvisation and com-position in dance. The specific approach, based on automaticexploration of pose maps derived statistically from motioncapture data, has the ability to reach beyond the “input” intonovel territories of movement. Experiments performed by thedancer in the team show that the avatar’s movement are sur-prising, inspiring and carry choreographic potential. In fu-ture work, it would be interesting to perform more systematicevaluations with a larger user group and a standard proceduresuch as Creative Support Index [Carroll and Latulipe, 2009].

Second, the creative process that has emerged between thedancer and the AI has many nontrivial qualities which can-not be easily characterized in terms of creative capacity andagency. Here we have analyzed the algorithms of the AIand the interplay between researcher, dancer and software asa joint exploration of conceptual movement spaces. In thispicture, humans and machine are components of the samecreative system. Naturally, other conceptualizations can bemade. We see the presented results and the theoretical analy-sis as contributions to an ongoing discussion about how cre-ative collaborations between artists and AI can be described,enabled and analyzed.

References[Berman and James, 2014] Alexander Berman and Valencia

James. Towards a live dance improvisation betweenan avatar and a human dancer. In Proceedings of the2014 International Workshop on Movement and Comput-ing, MOCO ’14, pages 162:162–162:165, New York, NY,USA, 2014. ACM.

[Boden, 1998] Margaret A Boden. Creativity and artificialintelligence. Artificial Intelligence, 103(1):347–356, 1998.

[Boden, 2004] Margaret A Boden. The creative mind: Mythsand mechanisms. Psychology Press, 2004.

[Carlson et al., 2011] Kristin Carlson, Thecla Schiphorst,and Philippe Pasquier. Scuddle: Generating movement

2436

catalysts for computer-aided choreography. In Proceed-ings of the Second International Conference on Computa-tional Creativity, pages 123–128, 2011.

[Carroll and Latulipe, 2009] Erin A. Carroll and Celine Lat-ulipe. The creativity support index. In CHI ’09 ExtendedAbstracts on Human Factors in Computing Systems, CHIEA ’09, pages 4009–4014, New York, NY, USA, 2009.ACM.

[Dahlstedt, 2012] Palle Dahlstedt. Between material andideas: a process-based spatial model of artistic creativity.In Computers and Creativity, pages 205–233. Springer,2012.

[deLahunta, 2009] Scott deLahunta. The choreographic lan-guage agent. In Cheryl Stock, editor, Conference Proceed-ings of the 2008 World Dance Alliance Global Summit,2009.

[Hollan et al., 2000] James Hollan, Edwin Hutchins, andDavid Kirsh. Distributed cognition: toward a new foun-dation for human-computer interaction research. ACMTransactions on Computer-Human Interaction (TOCHI),7(2):174–196, 2000.

[Kantosalo et al., 2014] Anna Kantosalo, Jukka M Toivanen,Ping Xiao, and Hannu Toivonen. From isolation to in-volvement: Adapting machine creativity software to sup-port human-computer co-creation. In Proceedings of theFifth International Conference on Computational Creativ-ity, 2014.

[Kohonen, 2001] Teuvo Kohonen. Self-organizing maps,volume 30. Springer Science & Business Media, 2001.

[Laban, 1966] Rudolf Laban. The Language of Movement:A guidebook to choreutics. Plays, Inc, Boston, 1966.

[Lubart, 2005] Todd Lubart. How can computers be partnersin the creative process: Classification and commentary onthe special issue. International Journal of Man-MachineStudies, 63(4-5):365–369, 2005.

[McCormick et al., 2013] John McCormick, Kim Vincs,Saeid Nahavandi, and Douglas Creighton. Learning todance with a human. In Proceedings of the 19th Inter-national Symposium on Electronic Art, 2013.

[Preston-Dunlop, 1998] Valerie Monthland Preston-Dunlop.Rudolf Laban: an extraordinary life. Dance Books Lim-ited, 1998.

[Rothwell, 2014] Nick Rothwell. Programming languages,software thinking and creative process. In AnastasiosMaragiannis, editor, Book of Abstracts DRHA2014, 2014.

[Schiphorst, 1993] T. Schiphorst. A Case Study of MerceCunningham’s Use of the Lifeforms Computer Choreo-graphic System in the Making of Trackers. M.A. Thesis,Simon Fraser University, 1993.

[Scholkopf et al., 1998] Bernhard Scholkopf, AlexanderSmola, and Klaus-Robert Muller. Nonlinear componentanalysis as a kernel eigenvalue problem. Neural Comput.,10(5):1299–1319, July 1998.

[Wiggins, 2006] Geraint A Wiggins. A preliminary frame-work for description, analysis and comparison of cre-ative systems. Knowledge-Based Systems, 19(7):449–458,2006.

2437