Handbook of Multimedia for Digital Entertainment and Arts- P22

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Handbook of Multimedia for Digital Entertainment and Arts- P22: The advances in computer entertainment, multi-player and online games, technology-enabled art, culture and performance have created a new form of entertainment and art, which attracts and absorbs their participants. The fantastic success of this new field has influenced the development of the new digital entertainment industry and related products and services, which has impacted every aspect of our lives.

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  1. 29 Storytelling on the Web 2.0 as a New Means of Creating Arts 633 Fig. 3 A comparison of the existing storytelling platforms to drive on it. The system can be interpreted as community of practice (drivers who have access to the Internet via mobile phones or PDA’s) and collaborative, since it is very important to get real time feedback from the users. Figure 3 presents a summary of all systems presented. Features presented in the table are very important for one storytelling system nowadays to meet all the re- quirements of the users. YouTell: A Web 2.0 Service for Community Based Storytelling How to apply storytelling for professional communities can be enabled by Web 2.0 and Social Software. We have designed and developed youTell using Web 2.0 ser- vice for community based storytelling. It is based on a social software architecture called Virtual Campfire. Virtual Campfire In order to make knowledge sharing a success for any kind of professional com- munity, independent of size or domain of interest, a generic community engine for Social Software is needed. After some years of experience, with the sup- port of professional communities two different products emerged: a new reflective research methodology called ATLAS (Architecture for Transcription, Localization,
  2. 634 R. Klamma et al. and Addressing Systems) [10] and a community engine called LAS (Lightweight Application Server) [23]. The research challenge in ATLAS was to incorporate the community members as stakeholders in the requirements and software engineering process as much as possible. In the end, all community design and engineering ac- tivities should be carried out by the community members themselves, regardless of their technical knowledge. While this ultimate goal of taking software engineers out of the loop is rather illusionary in the moment, we have targeted realizing a generic architecture based on the research methodology. It allows community members to understand their mediated actions in community information systems. In its reflec- tive conception the community information systems based on ATLAS are tightly interwoven with a set of media-centric self monitoring tools for the communities. Hence, communities can constantly measure, analyze and simulate their ongoing activities. Consequently, communities can better access and understand their com- munity need. This leads to a tighter collaboration between multimedia community information systems designers and communities. Within UMIC we have developed this complex scenario of a mobile community based on our real Bamiyan Develop- ment community, and the ATLAS/LAS approach. Virtual Campfire is an advanced scenario to create, search, and share multimedia artifacts with context awareness across communities. Hosted on the basic component the Community Engine, Virtual Campfire provides communities with a set of Context-Aware Services and Multi- media Processor Components to connect to heterogeneous data sources. Through standard protocols a large variety of (mobile) interfaces facilitate a rapid design and prototyping of context-aware multimedia community information systems. The suc- cessful realization of a couple of (mobile) applications listed as follows has proved the concept and demonstrated Virtual Campfire in practices: MIST as a multimedia based non-linear digital storytelling system; NMV as a MPEG-7 standard based multimedia tagging system; (Mobile) ACIS as a Geographic Information System (GIS) enabled multimedia information system hosting diverse user communities for the cultural heritage management in Afghanistan; and finally CAAS as a mobile application for context-aware search and retrieval of multimedia and community members based on a comprehensive context ontology modeling spatial, temporal, device and community contexts. All these applications employ the community en- gine and MPEG-7 Services within the Virtual Campfire framework. Other services and (mobile) interfaces are applied according to different communities require- ments. Virtual Campfire is running on Wireless Mesh Networks to apply high and stable network data transfer capability, and low cost, in developing countries. In order to use Web 2.0 feature, related community concepts for storytelling, a prototype called YouTell has been developed within the Virtual Campfire scenario. Figure 4 gives an overview of this new web service. Additionally to storytelling functionality an expert-finding service is integrated. Web 2.0 techniques as tagging and giving feedback contribute to a comprehensive role model for storytelling too. Tags can be analyzed for a dynamic classification of experts. This role model is also used to represent the behavior and influence of every user. In our previous research, we have focused on how to generate stories by applying the Movement Oriented Design (MOD) paradigm, which divides stories into Begin,
  3. 29 Storytelling on the Web 2.0 as a New Means of Creating Arts 635 Fig. 4 An overview on the YouTell concepts Middle, and End parts [21]. We have designed and deployed a so-called Multimedia Integrated Story-Telling system (MIST) to create, display, and export non-linear multimedia stories [26]. MIST proves to be applied in domains of cultural heritage management well, in order to organize a great amount of multimedia content re- lated to a monument or a historical site. MIST can also be used as an e-learning application to manage multimedia learning stuff or as an e-tourism support system to generate personalized tour guide. Drawbacks or missing features are also exposed during the deployment. First of all, MIST lacks the mechanism to support users’ collaborative storytelling explic- itly. That means, more than one users are able to work on the same story together, while their activities are not recorded for each user respectively but mixed. Second, MIST can be used to create and view stories. But users can not give any personal comments to the stories. Third, it is almost impossible to search stories in the large story repositories, since these multimedia stories do not possess proper metadata to describe it content. Finally, MIST lacks authority, if a story has a serious usage e.g. learning knowledge in a certain area. The question arises, who are the experts in the storytelling communities and have more potentials to create arts? YouTell enables communities to have joint enterprises (i.e. story creation), to build a shared repertoire (i.e. stories) and to engage mutually (i.e. expert contacts). Therefore, YouTell build a platform for a community-of-practice with a number of experts [29]. YouTell has also employed the most highlighted Web 2.0 features like tagging and feedback from amateur. Hence, the conflict between experts and amateur is dealt wish in YouTell.
  4. 636 R. Klamma et al. The main design concepts as well as algorithms of the YouTell system are an appropriate role model as well as user model for storytelling, Web 2.0 tagging fea- tures, the profile-based story search algorithm, and expert finding mechanism. The Role Model All roles which should be taken into account for storytelling is specified in YouTell (cf. Figure 5). A new YouTell user John Doe gets necessary rights to execute basic features like tagging, viewing, rating and searching for a story. Experts are users which have the knowledge to help the others. There exist three different sub roles. A YouTell technician can aid users with administrative questions. A Storyteller knows how to tell a thrilling story. And finally a Maven is characterized as possessing good expertise. A user has to give a minimum number of good advices to the communities in order to be upgraded to an expert. Administrators have extended rights which are necessary for maintenance issues. The system admin is allowed to change system and configuration properties. Story sheriffs can delete stories and media. Additionally, there exists the user admin. He manages YouTell users and is allowed to lock or delete them. A producer create, edit and manage stories. The producer role is divided into the production leader who is responsible for the story project, the author who is responsible for the story content, the media producer who is responsible for used media, the director who is responsible for the story, and finally the handyman who is a helper for the story project. Fig. 5 The YouTell role model
  5. 29 Storytelling on the Web 2.0 as a New Means of Creating Arts 637 The role called Bandits classifies users which want to damage the system. Ac- cording to their different behavior, they can be a troll, a smurf, a hustler or a munchkin. In contrast to bandits there exist two prestige roles: the connector and the domain lord. Whereas the connector knows many people and has a big contact network, the domain lord both has a great expertise and, at the same time, is an excellent storyteller. Web 2.0 for Storytelling: Tagging and Rating If a YouTell user wants to create a story, he first has to create a story project. With regard to his wishes he can invite other YouTell members to join his project. Ev- ery team member is assigned to at least one producer role. Every YouTell user can tag stories to describe the related content. Because the widely-in-use plain tagging approach has several disadvantages [14], a semantic tagging approach is used, too. Besides, users’ rating and viewing activities on stories are also recorded. As de- picted in Figure 6, A YouTell story are described with tags, rated by users. The popularity is also reflected by the viewing times. Profile-based Story Searching In comparison to MIST, YouTell has enhanced the story searching feature greatly. Additionally to a content based search by title or tags, a profile based search is offered to users. Figure 7 shows how the profile based story search works. In the following the corresponding algorithm is explained in detail. The set of all stories, which haven’t been seen and created by the user is described trough S D fS1 ; :::; Sn g. The function W S 7 ! W L assigns a set of tags to a story, R is the set of all ratings, RSi is set set of ratings of story Si ; Si 2 S . Fig. 6 Information board of a YouTell story
  6. 638 R. Klamma et al. Fig. 7 Profile based story search algorithm Input of the algorithm is a user made tag list W D fsw1 ; :::swk g. Additionally further information are necessary: the maximal result length n and the set of story ratings B of user with a similar profile. For computation of these users the Pearson r algorithm is taken(cf. [28]). Considered are user with similar or opposite ratings. If the ratings are similar the Pearson value is near to 1, if they are opposite the value is sear to -1. In the first case stories with similar ratings, in the second case stories with opposite ratings are recommended. The value has to be in a threshold L to be suitable. The Pearson value is computed with the following formula: Pm iD1 .ra;i ra / .rb;i rb / wa;b D qP P m i D1 .ra;i ra /2 m .rb;i rb /2 i D1 The Pearson value between user profile a and compared profile b is represented through wa;b . The variable m corresponds to the story count, i is a particular story and r its rating. The average ratings of profile a is displayed through ra . It holds B D fBS1 ; ; BSk g with Si 2 S; 1 Ä i Ä k. Furthermore BSi corresponds to the set of story ratings of user with a similar profile for story Si and finally it holds .RSi / D BSi . 1. step: Group the stories: The first group G1 corresponds to the story set S1 ; ; Sm , Si 2 S with W Â .Si / and .RSi / 2 B. The second group G2 contains the stories S1 ; ; Sl , Sj 2 S with .Sj / \ W ¤ ;; W ª .Sj / and .RSi / 2 B.
  7. 29 Storytelling on the Web 2.0 as a New Means of Creating Arts 639 2. step: 1. Take group G1 D fS1 ; ; Sm g. a. Compute the story ratings median BSi for every story Si . b. Build a ranking corresponding to the medians 2. If jG1 j < n, take group G2 D fS1 ; ; Sl g. a. Be P W S 7 ! R a function, which assigns a number of points to every story. b. For every j; 1 Ä j Ä l it holds P .Sj / D 0 c. For every tag swi For every storySj If swi 2 .Sj /: Compute the median mj of ratings BSj Map the result to the range [1,5]: m0 D mj C 3 j P .Sj /C D m0 j d. Sort the stories by their score. 3. Build an overall ranking with the rankings from group 1 and group 2. This rank- ing is the output of the algorithm. Expert Finding System Users who have questions can contact an expert. A special algorithm and useful user data are necessary to determine the users knowledge, in order for the users to find the best fitting expert. For every user there exists a user profile which contains the following information: Story data are generated when a user visits or edits a story. Expert data are created with given/ received expert advices Personal data represent the user knowledge the user has acquired in the real world. These data are typed in by the user itself. With these information three tag vectors are created. They will be weighted summed up and normalized. Such a vector has the following form: 2 3 t aga valuea 6t agb valueb 7 6 7 4t agc valuec 5 The final value of each tag represents the users knowledge assigned to the related tag. A value near to zero implies that the user only knows few, where as a value near 1 implies expertise at this topic. Now it will be described how the data vector is composed. First the story data vector will be created. For every story a user has visited and for every story for which
  8. 640 R. Klamma et al. the user is one of the producer, the corresponding story/media tags will be stored in a vector. The respective value is computed with the formula value D AV DV BF and – AV D count of appearances of a tag O – DF D date factor: The older a date, the more knowledge is lost. The value O lies between 0 and 1. A 1 stands for an actual date, a zero for a very old one. Four weeks correspond to a knowledge deficit of 5 percent. It holds: DF D 1 .b #weeks c 0:05/. 4 – BF D rating factor: This value is computed by the explicit and implicit feedback O which has been given. Then the story data vector d is computed: d D Story visit vector 0:35 C Story edit vector 0:65: After that a normalization to the range Œ0; 1 will be done: Let S D fs1 ; :::; sn g be the set of all tags, which occur within the set of data vectors and let v.s/ be the corresponding value. v.s/ vmin 8s 2 Sv.s/norm D vmax vmin and vmin D minfv.s1 /; :::; v.sn /g; vmax D maxfv.s1 /; :::; v.sn /g: In a second step the expert data vector is computed. For every expert advice a user has given/ obtained the corresponding tags are stored in a vector. The respective value will be calculated analogously to the above computation and it holds: expert data vector D advicegiven 0:8 C ad vi ceobtained 0:2: Third the personal data vector is computed. With the information the system got from the user tags and its corresponding values will be obtained. These will be taken for this vector. In a last step the final vector will be computed: data vector D 0:4 expert data vector C 0:4 story data vector C0:2 personal data vector: To find an expert first a vector v D fs1 ; w1 ; :::; sm ; wm g will be created with the tags the user has specified. Then this vector will be compared with all existing data vectors w1 ; ; wn . The user with the best fitting vector will be the recommended expert. The vectors have the following form: z D .s1 ; w1 ; s2 ; w2 ; ; sm ; wm /, whereas si is the i-th tag and wi the corre- sponding value. 1. Repeat for every vector wj ; 1 Ä j Ä n 2. diffj D 0
  9. 29 Storytelling on the Web 2.0 as a New Means of Creating Arts 641 3. Repeat for every tag si ; 1 Ä i Ä m of vector v 4. If si D sjk , sjk 2 wj : diffj D diffj C .wi wjk / 5. else diffj D diffj C 1 Output of this algorithm is the user for which data vector u holds: u D wj mit diffj D minfdiff1 ; ; diffn g. Web 2.0 for the Expert-finding Algorithm How does Web 2.0 features like tagging and esp. feedback influence on expert- finding? Users can give feedback to stories and for expert advices. Feedback is very important for YouTell, because it delivers fundamental knowledge for executing the profile based search and defining the user’s expert status. Furthermore the visu- alization of feedback results (i.e. average ratings, tag clouds) help user to get an impression of the experts/story’s quality. Explicit and implicit feedback techniques are used. After visiting a story respec- tively getting an expert advice the user has the possibility to fill out a questionnaire. This explicit form of giving feedback is fundamental for YouTell. But not every user likes filling out questionnaires [31]. Therefore, also implicit feedback is employed. Although this is not as accurate as explicit feedback, it can be an effective substitute [31]. In YouTell the following user behavior will be considered: The more one user visits one story the more interesting it is. The more a story is visited by all users, the more popular it is. In addition, the integrated mailbox service offers the possibility to handle all necessary user interaction of the YouTell community. Users need to send mes- sages when they want to ask an expert, give an expert advice, invite a new team member, etc. Implementation of the YouTell Prototype An overall architecture of YouTell is illustrated in Figure 8. YouTell is realized as client/server system and is integrated in the LAS system [25] implemented in Java. The client, implemented as a web service, communicates via the HTTP protocol with the las server by invoking service methods. The LAS server handles the user management and all database interactions. New services like the expert, mailbox, YouTell user and storytelling service extend the basic LAS features and fulfill all functionality needed by YouTell. The story service extends already existing MIST features and includes methods for the management of story projects and searching for stories. The expert ser- vice contains functions for computation and management of the expert data vectors. The mailbox service manages the mailbox system. The YouTell user service extends the LAS user service and offers the possibility to add and edit user specific data.
  10. 642 R. Klamma et al. Fig. 8 System architecture of YouTell In addition, YouTell needs several different servers to work properly. The client system communicates via the HTTP protocol with an Apache tomcat server. Their Servlets and JSPs are executed for the user interface of YouTell (cf. Figure 9). In YouTell the storytelling board is integrated with Java applets which run on the client. All media of the YouTell community are stored on a FTP server. The communication with the used databases (eXist and DB2) is realized by the LAS server.
  11. 29 Storytelling on the Web 2.0 as a New Means of Creating Arts 643 Fig. 9 YouTell screenshots and functionality description YouTell Evaluation The evaluation of YouTell consists of three parts. First users had the possibility to test the YouTell prototype. The results are described in Sub Section. After that the algorithms for profile based search (Sub Section 29) and for expert finding (section 29) are evaluated. The algorithms need a great amount of data to work properly. Because the available data were not sufficient, test data had to be generated for a convincing system evaluation result. Prototype Testing The YouTell prototype has been published on a web server and was available for all interested user. The users were requested to test YouTell for one month. At the end of that period they were asked to fill out a questionnaire. In parallel statistical data about the YouTell usage where collected by MobSOS. It is a mobile multimedia testbed and is able to test, evaluate and analyze web services [18]. Figure 10 shows how often LAS services have been called. From the statistics, it shows that the story service has been used most frequently and expert service calls took the longest time.
  12. 644 R. Klamma et al. Fig. 10 Calling statistics Fig. 11 Questionnaire results The questionnaire has been completed by 10 persons within a test session. Figure 11 shows some results. The worst rating was given for the loading times. Additionally, YouTell is not user friendly enough. YouTell as a whole has been rated with an average score of 2.1 and corresponds to the German school grade ”good”. To sum up the results, YouTell has been accepted by the test user but has to be improved. Profile Based Story Search To evaluate the profile based story search an approach analogously to the proceeding of Shardanand and Maes ([28]) has been performed. 1. Delete 20 percent of story visits of an arbitrary chosen user U 2. Run profile based story search without specifying tags. Compare results and re- moved stories. Store percentage of coverage.
  13. 29 Storytelling on the Web 2.0 as a New Means of Creating Arts 645 Table 3 Evaluation result: achieved hit rate (in pro cent) Minimum Maximum Mean Standard variance Profile based search with keywords 60 100 97.11 8.237 Profile based search without keywords 60 100 92.69 12.445 3. Run profile based story search with specifying tags that where assigned to the deleted story set. Compare results and removed stories. Store percentage of coverage. The evaluation results are shown in Table 3. The average hit rate of 97,11 indi- cates that not all removed stories are found. There exists a simple reason for this. Only stories visited by similar users can occur in the result set. Because all removed stories haven’t been visited by similar users, the algorithm delivers the exactly right results. Also in step 3 of the evaluation proceeding not all stories are found. But this was expected and the hit rate corresponds to the analysis results of Shardarnand and Maes [28]. Expert Finding Algorithm The expert finding algorithm delivers user/tag pairs with an expert value lying between one and zero. To evaluate the algorithm the value distribution has been analyzed. In Figure 12 the distribution of the expert values is depicted. For every number on the x- axis the frequency of user/tag pairs with an appropriate expert value is denoted. Figure 13 shows the same values separated by the singular tags. So both figures show that the expert knowledge distribution is approximately nor- mally distributed. Because the test data were predominantly normally distributed this result was expected. In Figure 12 the expert value 1 has a peak which seems to be unusual at first glance. This can be explained by the used normalization: Af- ter computing the data vectors they will be normalized in the range from 0 to 1 separated by the tags. Therefore, for every tag exists a user/tag pair with the value 1 resp. 0. This approach establishes the possibility to represent the knowledge as- signed to particular a tag within the YouTell community. Figure 13 shows that the distinct knowledge function differ. This implies that the knowledge about particular topics is differently pronounced within the YouTell test community. In addition to classification of the users’ knowledge, the expert finding algorithm delivers a measurement for analyzing the community knowledge.
  14. 646 R. Klamma et al. Fig. 12 Distribution of expert knowledge Fig. 13 Distribution of expert knowledge according to keywords Summary In this chapter, storytelling is discussed as a new means of creating arts based on Web 2.0 features and communities of practice. We illustrate a use scenario to demonstrate why and how storytelling is useful. The related work about the tagging approach and storytelling platforms etc. is discussed in Section 29. Section 29 per- tains to the design and main features of the community-based storytelling system
  15. 29 Storytelling on the Web 2.0 as a New Means of Creating Arts 647 YouTell. Section 29 introduces the implementation of prototype YouTell. Evalua- tion results based on hands-on experiences from the user communities are presented in Section. In summary, we combine Web 2.0 and communities of practice with expert find- ing in a storytelling platform YouTell to create arts. YouTell is featured with a role model for storytelling system, the tagging concepts, the profile based story search approach, and the expert finding mechanism. The YouTell architecture is discussed together with the prototype implementation issues. The prototype evaluation results show that the usefulness and performance in profile based story search as well as expert finding mechanism. Generated stories have been further applied to create educational games in order to train the professional communities [24]. Besides con- ceptual approaches and technical realization, the First International Workshop on Story-Telling and Educational Games (STEG’08) has been organized as an annual event to bring researcher communities on storytelling together. In the ongoing future research, new questions arise. How can diverse user com- munities work together seamlessly to create art through Web 2.0 based storytelling approach? How can amateur be upgraded into experts? The process of the idea sharing can also be carried out in YouTell. More application case studies can be explored. How can various Web 2.0 media organized via the YouTell storytelling platform? We can imagine that a number of Weblog entries or even bookmarks can be organized in a user generated sequence for the storytelling purpose. Stories will be exploited for entertainment with some speech bubbles, so that the expressiveness of the story narration could be enhanced or some art comments can also be given on the bubbles (see Fig. 14). Fig. 14 Narrating a story with speech bubbles
  16. 648 R. Klamma et al. Besides the use scenario for professional communities who are working on cultural heritage management conservation in Afghanistan. Another scenario is de- veloped to create stories for promotion and enrichment of the museum archives. We seek to share knowledge and train new researchers by research on advanced storytelling platforms and services for the Battleship “G. Averof” [27]. It provides communities more opportunities to create, access, share, and even reuse the large valuable multimedia collection about the Battleship “G. Averof” with the Web 2.0 storytelling technologies. Artworks can be shared in a better way across a larger community. Story templates will be also systematically surveyed. A story template engine and the corresponding story template editor will be provided to YouTell to enable user communities mash-up stories. Web 2.0 based storytelling contributes to advanced research on social software, storytelling, multimedia meta- data, GIS, and information technologies for arts, museums, architecture, cultural heritage management. References 1. S. Benford, B. B. Bederson, K.-P. Akesson, V. Bayon, A. Druin, P. Hansson, J. P. Hourcade, R. Ingram, H. Neale, C. O’Malley, K. T. Simsarian, D. Stanton, Y. Sundblad, and G. Tax´ n.e Designing storytelling technologies to encouraging collaboration between young children. In CHI ’00: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 556–563, New York, NY, USA, 2000. ACM. 2. J. Bughin and J. Manyika. How businesses are using web 2.0: A mckinsey global survey. In- ternet, http://www.mckinseyquarterly.com/article page.aspx?ar=1913&L2=13&L3=11 &srid =9&gp=1, 2007, [Last Access: April 2007]. 3. Y. Cao, R. Klamma, and A. Martini. Collaborative storytelling in the web 2.0. In R. Klamma, N. Sharda, B. Fern´ ndez-Manj´ n, H. Kosch, and M. Spaniol, editors, Proceedings of the First a o International Workshop on Story-Telling and Educational Games (STEG’08) at EC-TEL 08, Sep. 16, 2008, Maastricht, the Netherlands. 4. Y. Cao, M. Spaniol, R. Klamma, and D. Renzel. Virtual Campfire – A Mobile Social Software for Cross-Media Communities. pages 192–195, 2007. 5. T. H. Davenport. Thinking for a Living: How to Get Better Performance and Results from Knowledge Workers. Harvard Business School Press, 2005. 6. T. H. Davenport and L. Prusak. Working Knowledge: How Organizations manage what they know. Boston Business School Press, Boston, MA, 1998. 7. P. F. Drucker. Knowledge work productivity: The biggest challenge. California Management Review, 1(2):79–94, 1999. 8. P. EduTeCH. Internet, http://bsgconsulting.org/ini/edu par ger.html, 2008 [Letzer Zugriff am 01.05.2008]. 9. S. Gøbel, F. Becker, and A. Feix. Inscape: Storymodels for interactive storytelling and edu- tainment applications. In International Conference on Virtual Storytelling, pages 168–171, 2005. 10. M. Jarke and R. Klamma. Reflective community information systems. In Y. M. et al., editor, Proceedings of the International Conference on Enterprise Information Systems (ICEIS 2006), LNBIP, pages 17–28, 2008. 11. C. Kelleher, R. Pausch, and S. Kiesler. Storytelling alice motivates middle school girls to learn computer programming. In CHI ’07: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 1455–1464, New York, NY, USA, 2007. ACM.
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  20. Chapter 30 A Study of Interactive Narrative from User’s Perspective David Milam, Magy Seif El-Nasr, and Ron Wakkary Introduction The topic of interactive narrative has been under debate for several years. What does it mean to be engulfed in an interactive narrative? Can users engage in a meaningful interactive narrative experience? Who tells the story, the designer or the player? While answers to these questions have not been formulated, the community is split. Some regard the question of interactive narrative as an oxymoron, philosophically regarding narrative and play as two separate entities [1, 2]. Others regard narrative as an integral aspect of any interactive or media production [3], [4]. A reasonable approach to this dilemma is to explore these questions through the design, development, and evaluation of interactive narrative experiences. Many re- searchers have explored the design of interactive narratives integrating believable agents [15], drama managers [6], user modeling [7], [8], [9], and planning systems [10]. In our view, the design of a good interactive narrative requires the understand- ing of the participants and their experience. Even though research is ongoing in the development of interactive narratives, there is very little research exploring how users view their interactive narrative experience. This chapter focuses on a research study that attempts to understand the interactive narrative experience through the voices of the participants themselves, using a phenomenological method. For the study, we chose to use Facade as an interactive narrative experience; ¸ Facade was developed by Mateas and Stern and released to the public in 2005 [11]. ¸ While some may argue that video and computer games are rich with examples of interactive narrative, we believe Facade is a better choice to explore. Most video ¸ and computer games use puzzles, quests, destruction, or collection as their core mechanics, where narrative is often used for motivation or game aesthetics. Facade¸ focuses on social relationships, conflict, and drama as its core mechanics. In this paper, we report results from a qualitative study exploring the ques- tions: how do participants define interactive narrative before playing Facade ¸ D. Milam, M.S. El-Nasr ( ), and R. Wakkary School of Ineractive Arts and Technology, Simon Fraser University, Surrey, BC, Canada e-mail: fdma35, magy, rwakkaryg@sfu.ca B. Furht (ed.), Handbook of Multimedia for Digital Entertainment and Arts, 653 DOI 10.1007/978-0-387-89024-1 30, c Springer Science+Business Media, LLC 2009
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