Damian Hills
email: damski@assimilate.net

Developing Emergent Play In Collaborative Online Experiences

Keywords: Interaction Design, Creativity Support Tool, Interactive Digital Storytelling, Narrative Intelligence, Conversational Information System, Cybernetics

This paper will discuss common features of emergent play in the context of developing an online collaborative practice-based research project, assimilate. Emergent play features, such as development of fictional worlds will be identified, followed by a discussion of player experience of emergent play. The paper proposes an system framework that invites narrative emergent play facilitated by a set of clearly defined and simplified affordances that provide recognisable metaphors for collaboration.

Introduction

Recent developments [START_REF] Marrin | Webgl specification[END_REF] in web application technology have developed appropriate research areas for experimentation in emergent play. The technology has allowed for server architectures to asynchronously support rich interface to develop new and interesting entertainment platforms. This is of special interest to developers and researchers interested in novel experiments for emergent play and collaboration. This paper will discuss common features of emergent play in the context of developing an online collaborative practice-based research project, assimilate [START_REF] Hills | Collaborative Systems for Narrative Construction[END_REF]. (Figure 1). The project is a software design for collaborative narrative construction adapted from previous designs for online collaboration. Emergent play features, such as development of fictional worlds will be discussed, followed by a discussion of player experience of emergent play. Finally, an outline of the project with appropriate analysis of how these features are applicable.

Interface

Emergence in games is considered a fundamental game feature where collections of simple rules gives rise to new variations on behavior or outcomes. [START_REF] Juul | The Open and the Closed: Games of Emergence and Games of Progression[END_REF] In the context of play within games and other interactive media including playable games [START_REF] Ryan | From Narrative Games to Playable Stories: Toward a Poetics of Interactive Narrative[END_REF], emergence is considered the most appealing of designs. As it allows for such narrative possibilities as re-playability and player generated creativity with full agency and immersive qualities.

Given the established problems with narrative and player agency [START_REF] Louchart | Emergent narrative, requirements and high-level architecture[END_REF] researchers are identifying common design features that generate emergent play. These can be considered applicable to both games and play, as the notable differences in how a narrative coherence is generated, either directed by top down processes, such as a drama management [START_REF] Swartjes | A Fabula Model for Emergent Narrative[END_REF] or a bottom-up driven experience with the latter as the design goal of most desirability.

Two notable features will be discussed here, firstly the identification of boundaries that develop narrative coherence including fictional worlds, story worlds or contextual frames that place limits on emergence. Following this, the player experience of narrative emergence and possible solutions to maintaining collaborative engagement through an improvisational play.

Fictional World Coherence

Researchers of emergent narrative and play for digital experiences identify the development of the fictional world as basis for developing player or character based generated narratives. The experience of narrative differs greatly to the perception of narrative within films. For example, temporal events in games connects the play time, or discourse of the narrative (the sequence of events) directly with fictional time, or how the events are told [START_REF] Juul | Half-Real: Video Games Between Real Rules and Fictional Worlds[END_REF]. Game narrative becomes problematic when presenting fictional time in any achronological order commonly seen in films or fiction. Narrative is rather a direct consequence of player action and not perceived as the action evolves. This paradox of narrative comprehension in games has shifted research into understanding how narrative emergence may develop directly in fictional worlds. Games excel at the development of fictional worlds providing player agency and emergent play [START_REF] Jenkins | Game design as narrative architecture[END_REF] with rich story-building features constructed by player activities. They contain spatial metaphors that develop narrative possibilities suggesting interfaces may play a prominent role in player developed narratives. Fictional world metaphors are linked with aspects of the real world that exhibit recognisable, familiar activities and also places boundaries on context. Defining contextual boundaries [START_REF] Suttie | Theoretical Considerations towards Authoring Emergent Narrative[END_REF] contains the possibility space for emergence and maintains coherence through limitation. For a consistent and coherent world this is necessity as the link between emergent play, story and social world is deeply intertwined [START_REF] Pearce | A study of emergent behaviour in online games and virtual worlds[END_REF]. A play boundary in this case may just be an implicit or innate agreement on how to act, such as a 'magic circle' [START_REF] Salen | Rules of Play: Game Design Fundamentals[END_REF] or player attitude [START_REF] Suits | The grasshopper: Games, life and utopia[END_REF] or by placing specific design limitations on behavior to contain emergence [START_REF] Eladhari | AI-Based Game Design: Enabling New Playable Experiences[END_REF].

Fictional worlds that aim for coherence should be designed to develop implicit events [START_REF] Spierling | Adding Aspects of "Implicit Creation" to the Authoring Process in Interactive Storytelling[END_REF] rather than explicitly authoring sequences that will potentially exhaust the system of interesting outcomes. A good example of this can be seen in Minecraft [START_REF] Persson | Minecraft[END_REF], a successful player driven fictional world where activities mirror aspects of real world construction. However the true success of the Minecraft experience is largely dependant on social aspects of co-creativity and community engagement. For this reason, emergent fictional worlds need the drivers of collaboration and community to develop creative potential.

Player Experience and Participation

The narrative paradox as outlined by [START_REF] Louchart | Authoring emergent narrative-based games[END_REF] and others suggests emergent play and narrative would benefit from a character based model where specific behaviors may be interpreted by the player and a narrative induced from these collective environmental cues. This presents further issues in maintaining narrative comprehension as it requires the active involvement of the player in the experience. Players of emergent experiences are heavily dependant on the act of co-creation and collaboration for emergent narrative to be recognised at a macro level. Such an experience would require the player to act in similar ways to an improvisational actor with some understanding of narrative consequences based on actions of themselves and others. This has been discussed [START_REF] Louchart | Narrative theory and emergent interactive narrative[END_REF] [18] [START_REF] Fuller | Shared mental models in improvisational performance[END_REF] and further evaluated as a possible scenario [START_REF] Magerko | An empirical study of cognition and theatrical improvisation[END_REF] of emergent development of interactive digital narrative. The findings conclude that actors develop shared mental models and cognitive converge over specific dramatic performances that drive narrative forward.

Cognitive patterns such as these could be useful for understanding design for emergent play with actors who have the appropriate concern for the mutual progression of the narrative scenario. However the issue is that players are generally not trained as improvisational actors nor are they necessarily open to cues for collaboration. For design of emergent play, providing these cues can equip players with the tools they need to perform more like actors sensitive to the narrative context at hand. As such, developing interface metaphors that describe or explain the simple actions possible in the fictional environment in contrast with the familiar actions in the real world, can demonstrate how to act accordingly. This type of metaphor can also be described as an affordance [START_REF] Gibson | The concept of affordances. Perceiving, acting, and knowing[END_REF] and provides an innate understanding of how to perform within the environment.

Players perceive affordances they see in the fictional world and understand them through the affordances they commonly associate with the real world [START_REF] Gee | Video Games and Embodiment[END_REF]. In other words, they import the knowledge from their interaction of the real environment to understand how to act in the fictional world. This is akin to a conversation where embodiment, or innate knowledge of how the body acts within the world, can be transferred or hybridised with the game world. This notion of an innate affordance stems from the theory of situated and embodied cognition [START_REF] Hutchins | Cognition in the Wild[END_REF] [START_REF] Clark | The Extended Mind[END_REF], that states our bodies share in part responsibility for the way we comprehend the environment. This theory has already had some impact in the fields of human-computer interface (HCI) [START_REF] Dourish | Where the action is: the foundations of embodied interaction[END_REF] and is further discussed for its applicability to game play and player experience [START_REF] Gee | Video Games and Embodiment[END_REF] [26] [START_REF] Rambusch | The embodied and situated nature of computer game play[END_REF]. Situated cognition also has interesting implications for emergent game design and especially relates to aspects of interaction design and online player experiences. Researchers are evaluating the possibility of developing appropriate interface mechanics to enhance emergent play and narrative [START_REF] Freeman | Tweetris: A Study of Whole-body Interaction During a Public Art Event[END_REF] [START_REF] Alofs | A Tabletop Board Game Interface for Multi-User Interaction with a Storytelling System[END_REF]. Designing appropriate affordances will encourage players to perform more like actors and develop a level of collaborative engagement that produces more variations of behavior and novel outcomes. Further studies into motivations and aspects of playstyle with emergent games [START_REF] Canossa | Give me a Reason to Dig: Qualitative Associations Between Player Behavior in Minecraft and Life Motives[END_REF] would also further techniques on how to develop such affordances.

Emergent Model -assimilate

The following section presents the application of emergent play features for the assimilate system. The description includes an understanding of boundaries in narrative context and fictional world construction, followed by a holistic view of player experience with emergence and how interface mechanics support this collaboration for online deployment.

Developing Coherence With Simple Rules

The model [START_REF] Hills | A Conversational Framework for Emergent Collaborative Storytelling[END_REF] is based on Conversation Theory (CT) [START_REF] Pask | Conversation, cognition and learning : a cybernetic theory and methodology[END_REF], a cybernetic theory of learning and social interaction, that self-organises a set thematic relationships based on group conversation with eventual agreement on the context and meanings. As is applicable to an emergent system, relationships are arranged from simple rules or formalisms that scale into larger networks of meaning and may be combined or pruned through player participation. These networks, known as entailment meshes [START_REF] Pask | Conversation theory : applications in education and epistemology[END_REF] (Fig 2 .), are based on a simple formalism such that each concept is interdependent on at least two others.

Fig. 2. Cyclic entailment relationship

The most fundamental mesh would consists of three concepts, with every concept drawing its meaning based on its relationship to the other two. With all three exhibiting this relationship, this is said to achieve coherence within the CT theory. Coherence in this case meaning a participant can begin with any concept in the mesh and shift procedurally through the relationships in any arbitrary direction while developing an understanding of the assembled themes. This freedom to shift within an assembled concept network provides numerous entry points or cyclicity, such that there are no 'dead ends' in the entailment structure. When entailment networks are scaled or merged through collaborative action this provides the flexibility in the narrative structure and allows each participant to formulate their own point of view while developing a mental model that others may share based on their own actions within the network.

Interface Metaphors For Emergent Conversation

Emergent play experiences can directly benefit from collaborative experiences and the development of schemes for co-creation of artifacts within the fictional world that build community and social development.

For online play experiences the boundaries of context in the fictional space are embedded in the metaphors of the interface. This extends the conversational process and shows aspects of intention surrounding the collective narrative construction [START_REF] Hills | Collaborative Systems for Narrative Construction[END_REF]. Interface metaphors can support narrative comprehension and fictional world coherence through reflecting actions that are recognisable by other players and through revealing features of the system's inner processes.

Each player begins the experience by opening a 'ring' space and inputting a keyword that performs an internet search request. Retrieved video content circles the ring along with an associated annotated word tag. Salient or 'fit' tags (Fig 3 .) are placed inside the ring and associated video content plays out within the ringed space in a sequence.

Tags that are outside the ring may be selected and dragged inside forming new sequences. The ring exhibits a physics model that responds to gestural movement, that may be expanded or reduced to narrow the amount of sequenced content. Furthermore, each player's ring may be merged with others to form new combined sequences.

These interface mechanics are considered metaphors for narrative construction and maintain common recognizable aspects of conversation associated with storytelling, as such scope, exchanging views and merging ideas. (Fig. 4) These are either performed intentionally or inadvertently through the mechanics or player actions to produce dramatic or unexpected outcomes, a common feature of narrative emergence. The player rings are considered as agents in the experience that attempt to incorporate participants' creative actions and also show the self-organising conversational process. This is an attempt to visualise the system process, a form of expressive processing [START_REF] Wardrip-Fruin | Expressive Processing: On Process-Intensive Literature and Digital Media[END_REF] that develops the relationship between system processes and the surface level interaction of the players. This relationship is central to idea of developing emergent play as it provides affordances to the player for enacting a conversation. In similar ways to enabling a group of improvisational actors, it exhibits clues on how actions are taking place in the fictional world and invites collaboration. This allows the player to enact a conversation with the fictional world and assume a dynamic role of narrator, player or observer.

The general approach to the interface and its incorporation with the system processes, looks to holistic paradigm for how mental models and cognitive convergence can take place. While he system formalises some narrative structure through self-organisation, the interface metaphors allow for creative possibilites. The combination of the fictional world, the interface affordances and the understanding of the player's mental model in relationship to the other players creates the possibility for an emergence to take place.

Discussion

This paper has presented a view of emergent play that considers players experience of real world knowledge to develop appropriate fictional world affordances. Our comprehension of the real world is a continual sense-making process that accumulates knowledge from the world based on our bodily relationship to the world. This takes into account a holistic view of how emergence occurs in the real world and its possible application in game worlds to drive narrative coherence.

A study of situated and embodied cognition can provide design clues for how players may perceive certain affordances. One such view explains how our tendency to side with simple narrative explanations for complex emergent behavior. However there is an inherent disconnect between the causal relationships at the emergent level, and the eventual narrative that is extracted [START_REF] Walsh | Emergent Narrative in Interactive Media[END_REF]. From this view the possibility is that layering top down approaches, such as drama management over the emergent behavioral layer (agent behaviors) only serves to replace or simplify a chaotic system of possible micro-stories rendering the system sparse of creative possibilities.

One possible solution suggested in the paper is an interface that invites play as an improvisational actor with shared goals that are developed by visible and real embodied action of the players. This is facilitated by a set of clearly defined and simplified affordances that provide recognisable metaphors for collaboration.

In terms of narrative generation, this system model is clearly hit and miss and may not be applicable for a game market that requires player expectations to be met in most, if not all circumstances. However with online web applications this is perhaps not such a great requirement as players attitudes to gaming may very well shift with respect to browser based and casual games. For this reason emergence in online web applications may provide an interesting area for further research.

Fig. 1 .

 1 Fig. 1. assimilate interface

Fig. 3 .

 3 Fig. 3. Fitness measurement

Fig. 4 .

 4 Fig. 4. Narrative Metaphors


Peta Wyeth
email: peta.wyeth@qut.edu.au

Joshua Hall
email: joshua.hall@qut.edu.au

Daniel Johnson
email: dm.johnson@qut.edu.au

Designing a Digital Experience for Young Children with Developmental Disabilities

Keywords: Young children, developmental disability, interactive experience, design guidelines, evaluation, play and games

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Increasingly, innovative technology is being utilized in early childhood settings and there is clear evidence that well designed systems have the potential to engage young children, respond to their developmental needs and stimulate learning through play [START_REF] Edwards | A Research Paper to Inform the Development of an Early Years Learning Framework for Australia[END_REF]. However there is limited research focused on the design of new technology for children with developmental disabilities, especially within the early childhood years. Effective integration of playful technology into settings that include children with developmental disabilities is an ongoing challenge and our research is focused on addressing this issue. The research was guided by two research aims. Firstly, our objective is to identify the design requirements necessary to create playful interactive experiences that are meaningful and appropriate for young children with developmental disabilities. Our second aim involves understanding the extent to which the experience developed supports cognitive, social and physical play.

Background

Developmental disabilities can be defined as "a set of abilities and characteristics that vary from the norm in the limitations they impose on independent participation and acceptance in society" [START_REF] Odom | Handbook of developmental disabilities[END_REF].There are a number of studies that specifically examine the use of educational games for children within developmental disabilities [START_REF] Battocchi | Collaborative puzzle game: a tabletop interface for fostering collaborative skills in children with autism spectrum disorders[END_REF][START_REF] Durkin | Videogames and young people with developmental disorders[END_REF][START_REF] Piper | A Cooperative Tabletop Computer Game for Social Skills Development[END_REF]. Research has demonstrated the effectiveness of computer games in supporting engagement [START_REF] Pares | Achieving Dialogue with Children with Severe Autism in an Adaptive Multisensory Interaction: The "MEDIATE" Project[END_REF], facilitating social skills [START_REF] Battocchi | Collaborative puzzle game: a tabletop interface for fostering collaborative skills in children with autism spectrum disorders[END_REF] and providing opportunities to role play behaviors that are challenging in real social contexts [START_REF] Durkin | Videogames and young people with developmental disorders[END_REF]. However, a majority of the research in the field is focused on children aged between 7 and 14.

Our own previous research focused on the development of game-based interactions for adults with intellectual disability resulted in the development of the Stomp platform [START_REF] Wyeth | Stomp : An Interactive Platform for People with Intellectual Disabilities[END_REF]. The floor-based system allows users to interact with digital environments by triggering pressure sensors embedded within a 2 × 3 meter floor mat. Interactive applications are projected onto the mat using a short throw projector. The platform effectively turns the floor into a large pressure sensitive computer screen. The system is designed so that actions such as stomping, stepping and sliding in Stomp are like stomping, stepping and sliding in the real world [START_REF] Wyeth | Stomp : An Interactive Platform for People with Intellectual Disabilities[END_REF]. We use Stomp in our research as a means through which young children may engage in inclusive digital learning experiences.

Designing a Stomp Application for Children with Developmental Disabilities

In order to understand the requirements of an interactive experience for young children with developmental disabilities we involved teachers and support staff from an early childhood center that caters specifically for children with developmental disabilities. Initially, four informal meetings were undertaken to gain information on the types and nature of disabilities of the children who attended the school and to explore attitudes to technology designed to support children with developmental disabilities.

From the initial meetings we established that the teachers felt it was appropriate to use the Stomp platform to create a playful experience for children aged between four and six years. Two individual semi-structured interviews and one focus group involving three teaching staff followed these initial meetings. These discussions lasted between 45 minutes and one hour and were designed to illicit specific information with respect to the design of an interactive experience for the children attending the centre. These discussions resulted in the development of the interactivity design goals that were used to guide the design of the new application. General Interaction Goals: Interviews with teachers established that the interactive experience must not rely too heavily on a child's social skills, as this is often one of the most impaired abilities of children attending the school. As concepts such as teamwork are relatively new to this age group, any collaboration must be simple. For the most part, interaction is between the teacher and student. Therefore methods that enable greater communication between these two parties are just as important as im-proving student-to-student social skills. Four specific design guidelines were developed as a result:

• G1: Interaction should be clearly defined and simple.

• G2: The experience should facilitate supervisor / teacher interaction with the student.

• G3: The experience should facilitate interactions between students.

• G4: Interaction should not rely on successful collaboration or teamwork from students.

Input: Motor skills of the children vary, and the range encompasses those mildly impaired to children who are heavily impaired. System input requiring gross motor skills must take into consideration the physical build of a child. Actions need to be constrained to the reach, jump and step distance of a five-six year old child. Often developmentally delayed children's hand eye co-ordination is limited, therefore input actions requiring reflexes or accuracy will need ample room and time. Reaction time and spatial awareness is often impaired and events requiring actions must allow for this and occur at a slower speed. Object motion should be slow and relatively predictable. We developed three design guidelines based on this information:

• I1: System input should only rely on simple fine and gross motor skills.

• I2: Design actions that allow ample room and time for participants to react.

• I3: Input rules and boundaries should be clear and intuitive.

Feedback: Directions need to be clear, discreet and repetitive to ensure the message is conveyed. Once an action or task is complete audio/visual feedback should be provided to ensure children comprehend that a goal has been achieved. When conveying messages or events visually, content must be modest in color and intensity. Over stimulation and confusion may result from an excessive range and intensity of colors, shapes and objects being visible on the screen space. Excessive noise or loud sudden sounds can disturb some children. Three specific design guidelines stem from this information:

• F1: Feedback should be clear and discreet.

• F2: All forms of feedback should not be excessive or loud.

• F3: Feedback should occur immediately after input from a child.

The Anim-Action Experience

Anim-action is a collaborative art experience playable on the Stomp interactive surface to maximize the advantages of using a system that allows for whole body interaction. Participants can work together or individually to create, color and draw animal characters that come to life. Three stages were included in the experience; these were designed to progress the participant from simple to more complex learning goals (see Fig. 1). Each mode has been designed to incorporate the interactivity and goals identified in section 3. Stage one, draw mode, requires participants to start drawing the outline shape of their created animal character. The participant walks along the dashed outline of the shape, activating solid line pieces until the shape is a continuous solid outline. In color mode the participant may choose colors from a selection available from the side bar. Colors may be selected by simply stepping or jumping on them. Painting will occur when the child stands inside the animal outline area. Detail mode keeps the color bar on the side of the mat, but also introduces additional animal details as objects on the opposite side of the mat. Details, such as body parts and facial features, can be added by jumping or stepping on an object (e.g. eye) and then placing the body part on the animal. Correct positioning is indicated by a highlighted outline. Selecting from the color pallet either before or after object selection changes the color of these body parts. The Anim-action experience was designed to meet the interactivity design guidelines and educational goals in the following ways.

General Interaction: The space for social interaction is clearly defined and children are able to step anywhere on the mat without penalty (G1). Teachers are readily able to observe interactions and guide/instruct children as required (e.g., guiding a task, suggesting some cooperative activity) (G2). Stage 2 and 3 provide opportunities for children to engage in simple social interactions (e.g., in stage 2 one participant might select a color and the other paints, or both children might be involved in painting different areas of the shape (G3). While there are varied levels of social interaction from observing, through to advising and interacting (G3), interaction does not rely on any collaboration or teamwork (G4).

Input: The space for interaction takes up a large portion of the 3m x 2m floor mat and each interactive piece (e.g., line in draw mode, paint blob in color mode) provides a generous activation zone that is larger than a child's foot (I1, I2). The main objectives of the activities be can achieved through simple stepping motions (I1). The visual properties of the application ensure that input rules are simplified. For example, the dashed outline, as the only element on the mat initially, invites the participant step on that line to see what happens (I3). There is continuity from Stage 1 to Stage 2 as the outline shape remains the same. This allows the participant to work within the same area of the floor and follow similar rules of interaction (I3). A color in the color palette is already active when color mode starts. Consequently, a child can start painting without actively selecting a color (I3). Similarly, a body part choice is activated when detail mode becomes available (I3). No time constraints are enforced by the system (I2) and primary interactions occur within the boundary of the animal (I3).

Feedback: When in draw mode, the system responds to a child stepping on the outline of the shape by changing the dashed line to a solid line section and emitting a "click" sound effect (F1). During paint and detail mode, only one selection can be chosen at a time (e.g., a color blob from the paint palette, or a body part from the details menu) (F1). Upon selection of the color or body part in the system responds with visual and aural feedback and the chosen object remains highlighted when active (F3). A subtle glow effect is used to indicate the selection that is currently active (F2). During color mode, one foot step on the mat is represented as one individual paint splat (F1). Once a participant steps on any area within the shape outline, a paint splat effect is created (F3). To guide children towards shape selection in detail mode, an initial shape outline (e.g. eye) is highlighted on the animal's body (F1). When a shape piece has been correctly placed, feedback is given via a visual and audio cue (F3). Once this has occurred a new missing shape piece will be highlighted (F1). All visual and aural effects are designed to clearly depict an outcome, while at the same time being subtle in color and format so as not to over stimulate or distract children (F2). All system feedback is immediate (F3).

Anim-action Evaluation

Thirteen children, aged 5 and 6, participated in the evaluation of the Anim-action. Twelve boys and one girl from four different classes were involved. The study took place on six days during a two week period and study findings are based on observations of participants' engagement in the Anim-action experience. Each session lasted approximately 45 minutes and the observation method was based on existing teacher assessment schemes. The use of teachers to make assessments has been found to be highly effective in assessing the behavior of children with developmental disabilities [START_REF]Comprehensive Assessment and Evaluation of Students with Learning Disabilities[END_REF]. Teaching staff observed each child and recorded the child's displayed behaviors on an observational checklist sheet. Observation items were divided into four categories: cognitive (five items); social divided into two sub-categories: communication (four items) and social interaction (four items); and gross motor physical skills (three items). Observed behaviors were scored based on the level of assistance required during interaction for each checklist item. For a particular skill, such as color matching (cognitive), a child would be given a score between zero and five. The scoring system is as follows: 0 -no behavior exhibited; 1 -child did not initiate activity and at least two types of prompts were provided (e.g., verbal and physical); 2 -child did not initiate activity and one type of prompt was provided; 3 -child initiated activity and at least two types of prompts were provided; 4 -child initiated activity and one type of prompt was provided; 5 -child completed activity independent of support.

Findings

Analysis of data was performed to determine what kinds of interactions children were engaged in while playing Anim-action. We examined independent behavior, as de-fined as a child receiving a score of 5 (independent) or 4 (limited prompting) for a particular item. Similarly, dependent behavior is defined as a child receiving a score of 1 (dependent) or 2 (partially dependent). Results indicate that most children were able to engage independently in the cognitive activities of color matching (85%), shape matching (85%), problem solving (77%), task focus (85%) and body awareness (85%). Fig. 2 details the results for cognitive engagement across the five items measured. Similarly, most children were able to independently engage in physical activities while playing Anim-action (Fig. 3). They independently exhibited concentration on movement (85%), balance (100%) and motor planning (92%).

Social Skills

Cooperation Regulated Behavior

Waiting for a Turn Listening Skills Scores for independent activity were considerably lower in the communication and social interaction categories. In both areas a more bi-modal distribution is evident, such that children were either relatively independent in their behavior, or no behavior was exhibited at all (see Fig. 3). Investigation of communication skills engaged in while playing Anim-action shows that requests for help were limited, with 11 of the 13 children never requesting help. There was slightly more evidence of independent labeling of objects (54%), turn-taking communication (62%) and general communication (46%). While results show that cooperation was limited, with 10 of the 13 children exhibiting no cooperative behavior, children were observed engaging in other independent social activity: 62% were able to independently regulate their behavior and 62% were able to independently wait for a turn and all children listened for direction (either independently or with assistance).

Discussion and Conclusion

Results indicate that Anim-action was effective in promoting physical and cognitive engagement for young children with developmental disabilities. Of particular note was the evidence of independent problem solving and motor planning that occurs during the Anim-action experience. The staged approach to introducing complexity in both physical and cognitive activity through a carefully considered design may explain this result. Children were introduced to simple, structured gross motor activity (i.e. line following), before being required to move from one part of the play space to another in order to achieve their goals. The experience implicitly enforced simple interaction rules and while there was no negative feedback, a lack of responsiveness for incorrect activity may have encouraged children to focus on moving their bodies to achieve the desired outcome. Similarly, problem solving was introduced gradually and visual cues (e.g. the outline of a body part) provided scaffolding for the problem solving process. Immediate visual feedback allowed children to assess the effectiveness of their actions. Children are immersed in planning and problem solving activity. These results suggest that children were less engaged in social interaction. The system was designed so that children could work effectively autonomously and independently. There was no explicit requirement for social interaction, with G4 explicitly aimed at ensuring that interactions did not rely on collaboration. In light of the findings it may be necessary to rethink criterion G4 and how such a requirement is implemented. For example, while cooperation might not be necessary, more encouragement of, and reward for, engagement in shared experiences could be included. These findings also demonstrate that design decisions related to criteria G2 and G3 need to be reconsidered. It is perhaps necessary to consider an implementation that includes more systematic and overt mechanisms for teacher-student and student-student interactions.

The automated within Anim-Action experience may also be partly responsible for this finding, with the system providing prompt cues to guide a child's interaction. This automation may result in children not needing to ask for help. It also appears that the immersive nature of the digital experience results in a reduction in cooperative interaction. At the same time, it may be these automated and immersive qualities that have led to an increase in independent problem solving and motor planning. This tension between autonomy and immersion, on one hand, and social connectedness on the other, needs to be acknowledged in the development of new digital experiences for learning.

The results indicate that Anim-action is effective in supporting cognitive and physical engagement. Children's ability to work autonomously and not ask for help can be seen as positive. However, it needs to be acknowledged that the current study indicates that the experience was not as effective in facilitating social behavior. For children with developmental disability, where interaction is primarily with teachers and support staff, social interaction is a key aspect of early intervention programs. It would appear that the design of a digital system needs to be carefully considered in light of our findings to ensure that mechanisms are built in that better support collaborative behavior. It may also be that such digital experiences are most effective when they focus on one aspect of children's development and not another with experiences that attempt to simultaneously achieve all possible development goals ultimately being less successful. Future research will also explore the extent to which this pattern of results extends to other Stomp experiences and comparable non-digital activities.

Fig. 1 .

 1 Fig. 1. Anim-Action Interactions -(from left to right) draw mode, color mode and detail mode

Fig. 2 .

 2 Fig. 2. Children's engagement in cognitive and physical activity.

Fig. 3 .

 3 Fig. 3. Children's engagement in social activity.

Acknowledgements

We would like to thank all of the staff and children at Nursery Road State Special School, Queensland who participated in this study.


Bhojan Anand
email: banand@comp.nus.edu.sg

Hong Wong

Wei
email: wei@comp.nus.edu.sg

ARENA -Dynamic Run-time Map Generation for Multiplayer Shooters

Keywords: games, procedural content generation, game map generation

In this paper, we present simple and novel method to procedurally generate the game maps for multiplayer shooter games faster (in order of seconds) without compromising the expected features of a good multiplayer shooter environment.

Introduction 1.Procedural Content Generation

Procedural Content Generation (PCG) is the use of algorithmic means to create content [START_REF] Togelius | Search-based procedural content generation[END_REF] [START_REF] Watson | Procedural urban modeling in practice[END_REF] dynamically during run-time. Instead of trekking the same grounds which gets stale with time, PCG promises a more novel experience every playthrough. PCG was utilised in games as early as 1978. A notable example is Rogue [START_REF] Loguidice | Vintage Games: An Insider Look at the History of Grand Theft Auto, Super Mario, and the Most Influential Games of All Time[END_REF] [1] which spawns a new genre known as Roguelikes. Core features include randomly generated levels, item locations and so on. PCG's influence extends to racing games like Gran Turismo 5, which procedurally generates its tracks [START_REF]PolyphonyDigital: Gran turismo 5 course maker[END_REF]. First Person Shooter (FPS) Borderlands series procedurally generates weapons [START_REF]Gearbox: Borderlands 2 information[END_REF].

Multiplayer Shooter

We believe that an exception to PCG lies with environments/maps for multiplayer shooters such as Battlefield [START_REF]DICE: Battlefield 4 multiplayer[END_REF], which remains one of the most popular genres to date [START_REF]Global yearly chart[END_REF].

The reader should note the difference between a multiplayer and a standard shooter, which often follows an approximately linear path. The competitive nature of multiplayer shooters brings additional challenge of ensuring fairness through the positioning of strategic points. Moreover, players typically have more freedom to move around the map. It is for these reasons the designers often take a different approach to craft multiplayer maps.

We limit our scope to the game mode Capture and Hold, popularised in games like Killzone [START_REF]Guerrilla: Killzone home[END_REF] . Every player belongs to one of 2 teams. Littered around the map are flags that are captured by placing players in close proximity. A captured flag (can be recaptured) lowers the team points for the opposing team continuously. A team point of 0 signals the victory of the opposing team. To win, teams have to actively defend and pursue flags.

In this paper, we present a novel method that can generate dynamic maps almost as soon as the player press the 'Play' button, while ensuring the quality. The method is simple, practically feasible and we have implemented and evaluated it with a test game. In the rest of the paper, we first discuss the motivations for this work in Section 2 and then related works in Section 3. In Section 4 we describe the design goals of map generation method. Section 5 explains the design of the map generator. Implementation and evaluations are described in Section 6. Finally we conclude the paper with the summary at Section 7.

Motivation

In this work, we are attempting to automatically create playable, balanced (fairness) and interesting maps for multiplayer shooters, with a novel approach built using Searchbased PCG [START_REF] Togelius | Search-based procedural content generation[END_REF]. While PCG is used by some multiplayer shooter game designers, who would procedurally generate maps and then manually tweak them to ship with the final product, our goal is to remove the human intervention for manual tweaking completely. This means that the generation should be completed within a span of seconds, or else the patience of the player could wear thin. If we are successful, the development time and cost needed to create maps for similar games could be drastically reduced. The result would be increased longevity that stems from the near limitless amount of maps for players to play in.

Related Work

Güttler et al. [START_REF] Güttler | Spatial principles of level-design in multi-player first-person shooters[END_REF] identified some basic spatial properties of multiplayer FPS games and proposes several heuristics for better level design. In addition, the insights provided by several industry leaders of leading game companies on design of a good multiplayer game ( [START_REF] Scimeca | How to build the best multiplayer fps maps[END_REF], [START_REF] Holloway | Deathmatch map design: The architecture of flow[END_REF] and [START_REF] Hill | Game design theory: Multiplayer level design[END_REF]) are incorporated in formulating our design goals. Search-based PCG (SBPCG), an approach to PCG, was introduced by Togelius et al. [START_REF] Togelius | Search-based procedural content generation[END_REF]. We will be employing a similar approach in our solution. Togelius et al. also managed to procedurally generate tracks for a racing game [START_REF] Togelius | Towards automatic personalised content creation for racing games[END_REF] and maps for strategy game Starcraft [START_REF] Togelius | Multiobjective exploration of the starcraft map space[END_REF]. In both cases, SBPCG was used with a simulation based fitness function. Kerssemakers et al. [START_REF] Kerssemakers | A procedural procedural level generator generator[END_REF] introduced a procedural PCG generator to generalise the creation of PCG to games again with the use of a simulation-based fitness function. However, the use of simulation-based fitness function is not suitable for our goals, due to the time it takes to create a map is long and it is not suitable for practical implementations due to the strict real time requirements imposed by the games and game players. Work done by Cardamone et al. [START_REF] Cardamone | Evolving interesting maps for a first person shooter[END_REF] to evolve interesting maps for a FPS leveraging on SBPCG is a great starting point for our research. However, a great amount of work have to be done to ensure that the map can be generated in a span of seconds. Moreover, the maps that are generated are seemingly low on navigability and aesthetics, which are basic features of any good multiplayer game. In contrast, navigability and aesthetics are part of our design goals.

Map Design Goals

We describe what we want to achieve by identifying elements of interesting maps (of multiplayer shooters) so that they can be incorporated into our design. 1) Fast Generation -We wish to generate the maps in real-time. In other words, the final map has to be generated within a span of seconds to minimise player frustration. 2) Collision Points -Güttler et al. [START_REF] Güttler | Spatial principles of level-design in multi-player first-person shooters[END_REF] defines collision points as areas that see the most clashes and where most tactical choices are made. Tactical choices begin with preparations (route to take and so on), and ends with a confrontation. The designer should be aware of them to give more opportunity for tactical choices [START_REF] Hill | Game design theory: Multiplayer level design[END_REF]. In contrast, a map with no clear or too many such points are likely to see players stumping onto opponents unexpectedly. 3) Flow -The designers in [START_REF] Holloway | Deathmatch map design: The architecture of flow[END_REF] emphasise on flow, an "invisible flow (that is) continually impelling the player onwards". As this is too abstract, we deconstruct it into measurable components (Navigability and Pacing). Navigability -players should be able to recognize where they are and where they should go. Pacing -confrontation should last enough duration to be fun. It should be accompanied by some respite, but not to the extent of inducing boredom [START_REF] Güttler | Spatial principles of level-design in multi-player first-person shooters[END_REF]. Also, the map should not have disruptive dead ends. Even though they may not fully encompass it, we have observed that they provide reasonably good flow and serve as good starting point for future research. 4) Fairness -Each team should have same chance of victory [START_REF] Güttler | Spatial principles of level-design in multi-player first-person shooters[END_REF], which is related to flags. A team with flags closer to its spawn point has a higher chance of capturing them. 5) Aesthetics -The design must have the potential to meet aesthetics demand of players. There is a rich set of taxonomy that collectively define aesthetic [START_REF] Hunicke | MDA: A formal approach to game design and game research[END_REF]. For instance, a map cannot constitute entirely of blocks. Instead, it should contain trees, vehicles and so on to create more natural challenges. In addition, having diverse items improve navigability, as players can use them to get their bearings.

Map Generator Design

In this section we describe our algorithm to generate interesting maps for multiplayer shooters, which makes use of the popular Search-Based Procedural Content Generation (SBPCG) [START_REF] Togelius | Search-based procedural content generation[END_REF] method. We employ generate-and-test approach as illustrated in Figure 1.

We first present how a map blueprint is created by our initialisation algorithm in Section 5.1. Then, we detail how it evolves with a fitness function to produce the final map blueprint and how it is mapped to the actual game environment in Section 5.2.

Initialisation Algorithm

Our initialisation algorithm consists of several phases. We first populate the blueprint piece by piece before determining where to place strategic points and computing its fitness. PHASE I: Populating Game Tiles. Each game tile comprises of four smaller cells, which belong to either an indoor area (inside a building), outdoor area or inaccessible area as shown in Figure 2a. A game tile can be placed in a location adjacent to another game tile only if the neighbouring cells match (Figure 2b). We call this adjacency requirement as Rule of Adjacent Game Tiles.

The map uses a grid layout, with each 'square' occupied by a game tile. The grid is first enclosed by a layer of fully inaccessible game tiles. This ensures players cannot move outside the map. Next, game tiles of random types are placed in unoccupied 'squares', constrained by the rule of adjacency (Figure 2b). This is repeated until it is fully filled. An example of the generated map can be seen in Figure 3. PHASE II: Cleaning Up. We notice several artifacts like overly small buildings or protruding parts of buildings' remains. We scan the map blueprint both vertically and horizontally, removing single cells which are surrounded by cells of different types as depicted in Figure 2c. This is necessary to give the map a 'cleaner' look with more regularly shaped buildings which would otherwise hurt its navigability. PHASE III: Identifying Regions. To analyse the map, the algorithm requires an understanding of its layout. We first identify regions of the map, which are either indoor or outdoor. Region detection is done by applying a Flood Fill algorithm to the cells, stopping when it reaches its maximum size or when no cells are left. This is repeated until all accessible cells belong to a region. The maximum size is proportional to the map's size and can be tweaked. Note that cells of different types cannot share the same region; Indoor region contains only indoor cells and outdoor region contains only outdoor cells. PHASE IV: Connecting Regions. By identifying regions, we can construct an undirected graph with each node located approximately at the region's centre. Nodes are connected by an edge if players can move directly from one to the other without passing through a third one. At this stage, no edges exist between indoor and outdoor regions. For every indoor region, edges are created to connect to an neighbouring outdoor region. This adds a door between them and improves the connectivity of the map. The same A straight line is first 'drawn' between spawn points. Each side of the line will contain a neutral flag. They bound to a node with the minimum difference in travelling distance (using the Dijkstra algorithm) from both spawn points to preserve fairness. Collision Points are identified by the degree of the node. A high degree node most likely belongs to a region that players are prone to meet as many different routes will lead to it. We observed that a degree of five and above makes a good condition for a collision point. Covers are placed approximately at the meeting point of regions that have an edge to the collision points. This helps with promoting tactical choices as it provides more options to players who are more likely to meet at collision points. Covers are also placed at nodes which have not been assigned anything. Since these nodes are usually at region centres, the covers act as good places to hide if a firefight is to break out at that region. Without it, there may be too many open areas.

Evolution

Fitness Computation A fitness is assigned for the evolutionary algorithm to tell how good the map is. There are 3 approaches [START_REF] Togelius | Search-based procedural content generation[END_REF]. Interactive Fitness Function grades it based on interaction with a player. As this is physically impossible, this approach is not considered. Simulation-Based Fitness Function uses AI (Artificial Intelligence) agents to play through a portion of the game for evaluation. Many research works ( [START_REF] Togelius | Towards automatic personalised content creation for racing games[END_REF], [START_REF] Kerssemakers | A procedural procedural level generator generator[END_REF] and [START_REF] Cardamone | Evolving interesting maps for a first person shooter[END_REF]) used simulation-based fitness functions. However, its weakness lies in the time required to compute it.

Therefore, we will be employing Direct Fitness Function, where a content is judged by retrieving a list of features. In our solution, the fitness is computed by simply summing up the values for all of the following features which are derived based on the design goals presented at the beginning of this Section. 1) Connectivity -Returns 1 if the graph is connected (as detected in Phase IV). Otherwise, returns 0. All regions are reachable in a connected map. 2) Forced Collision Points -Returns 1 if there are one or two collision points. Returns 0 if there are zero or more than two collision points (no clear collision points). Ideal number of collision points depends on the map size. 3)Flag Fairness -Difference in the distance travelled to own team flag. This is measured by finding the travelling distance from the spawn points to the corresponding team flag. The returned value is normalised to 0 to 1, with 0 being maximum distance apart and 1 being approximately the same distance apart. 4) Overall Flag Fairness -Similar to Flag Fairness, but returns the difference in distance travelled to all flags from the spawn points instead.

Evolution and Mapping

The first population consists of three map blueprints generated with the initialisation algorithm. They are then mutated three times each, producing nine more blueprints. Each mutation removes part of the map and repopulate it with the initialisation algorithm. With that, the population is complete with twelve map blueprints. Three of the best maps (based on their fitness values) are picked and mutated three times to form the second population. This continues until enough populations are processed. The final map blueprint obtained after the evolution is then used to create the actual environment. Doing so is a direct mapping of each abstract game tile (in blueprint) to one of the many variations of concrete and matching game tiles seen by the player.

Evaluation

Evaluation Methodology

We developed a FPS game to implement our solution. Video demonstration of our algorithm and playable version of our game are available at our project homepage [START_REF] Anand | Arena-procedural map and music generation for multiplayer shooters[END_REF]. We evaluated the effectiveness of the evolutionary algorithm on producing good maps and its compliance with the design goals.

Effectiveness of Evolution

We created 3 different maps using our solution. We run the evolution algorithm for 55 populations (approximately 10 seconds in a Intel Core i7 laptop) each time to generate the map. The maximum average summed fitness is 4.0, as the fitness for each of the 4 features is normalised from 0.0 to 1.0. The average summed fitness of the processed population against the number of populations processed so far is plotted in Figure 6. The positive gradient shows that the evolutionary methods does improve the quality of the map as more population are generated and mutated. It also shows that the fitness stabilises after 26 populations (which takes about 5 seconds).

The fitness for each feature (with unnormalised values) of generated maps are shown in Table 1. Based on the fitness function that we have defined, the maps are all connected (with Connectivity of value 1.0), meaning that no regions are blocked from the rest. The algorithm is also effective in constraining the number of collision points (with Forced Collision Points of value 1.0, indicating that there are either 1 or 2 collision points.). For both Flag Fairness and Overall Flag Fairness, we show the unnormalised values. These values indicates the absolute difference in moving from the spawn points to own team flags for Flag Fairness, and the difference in moving to all flags for Overall Flag Fairness. A value of 1.0 means their distance are exactly one cell apart. Given our result, the values are very low, with the highest being 1.03510, which is barely one cell apart. To give a clearer perspective, one cell will take only approximately a second to travel. Henceforth, we can conclude that the flags are placed in positions that are largely fair for both teams.

Meeting Design Goals

A user study was conducted to test if the map generated using our method meets the design goals. The game was ported to run in a browser environment. The URL of the game with a starting page containing the introduction to the game, game rules and mechanics were sent to all participants. The users were allowed to play the game multiple times before taking the survey. A brand new map is generated with every playthrough. Table 2 shows the demography. The survey had 6 questions with Likert scale of 1 to 5 for each question. The questions: Q1) How fast is the loading process? The results shown in Figure 7 are discussed below. 1) Fast Generation -As discussed above it took about 10 seconds (10.1s, 9.9s and 11.5s respectively for Map1,Map2 and Map3) to generate the maps with 55 populations while 26 are sufficient. We feel that this loading time is reasonable for a commercial game. The mean user score of 3.19 with a standard deviation of 1.08 for Q1 implies that the users are generally acceptable of the time taken to generate a map. In contrast to algorithms in previous works [START_REF] Cardamone | Evolving interesting maps for a first person shooter[END_REF], [START_REF] Togelius | Towards automatic personalised content creation for racing games[END_REF], [START_REF] Togelius | Multiobjective exploration of the starcraft map space[END_REF] which takes hours, our algorithm is relatively successful in real-time generation. Previous works typically take a highly random approach in initialising the candidate, and relies too heavily on evolution. The key difference lies in our relatively complicated initialisation process, which takes additional steps to improve fitness of base maps. 2) Collision Points -The sample mean is 3.60 with standard deviation of 1.25 for Q2 implies the design of collision points are effective as we have successfully limited the points of confrontation. The covers surrounding it provides ample opportunities for tactical choices. 3) Flow -From the results of Q3, Q4 and Q5, we can say that our maps have fairly good navigability and pacing. Even with measures to prevent dead ends, they remain in some areas that are hard to detect. The buildings on the left and right are large but do not have doors other than the ones that leads to the centre. Players may find themselves trying to find flags deeper into them only to discover a dead end. This can be improved by tweaking the maximum region size. Pacing is susceptible to many confounding factors, such as the speed of movement, rate of fire, speed of reloading and so on, and is therefore not exclusively dependent on the map design. 4) Fairness -We measure the time to navigate from both spawn points to all four flags by playing through the map. It takes 58.88s to move from the blue base and 60:21s to move from the other base. The small difference between these values implies high fairness. The sample mean of 3.36, standard deviation of 0.98 for Q6 implies that the placement of flags are largely fair for our users. 5) Aesthetics -The look of the game depends on the artist. In general, However, what we can observe from the map is that the buildings have decent shapes and the objects such as trees and wagons are placed naturally as shown in Figure 5.

Conclusion

We first presented our findings of what constitutes a good game environment for multiplayer shooters, before designing and implementing an algorithm for the procedural generation of a map that can satisfy all the criteria. We then evaluated the maps by measuring the effectiveness of the evolution to produce better maps and to check whether the result satisfies what we have set out to do. Our evaluations show that we can generate game map procedurally for multiplayer shooters in less than ten seconds without compromising the common design requirements (flow, positioning of collision points, fairness and aesthetics) of commercial multiplayer shooters. Hence, in contrast to previous works, our method is practically feasible for run-time dynamic map generation and can be immediately used by game industry.

Fig. 1 :

 1 Fig. 1: Illustration of a SBPCG process. Only accepted maps are used in the next population.

Fig. 2 :

 2 Fig. 2: a. Types of Game Tiles (not complete), b. Rule of Adjacent Game Tiles, c. Removing artifacts horizontally

Fig. 4 :

 4 Fig. 4: Map blueprint after Phase V. The shields are the spawn points. The flag icons show the position of the flags. The shield and sword icon near the centre represents a collision point. The thicker lines are covers.

Fig. 5 :

 5 Fig. 5: Visual Appearance of the Game Environment

Fig. 6 :

 6 Fig. 6: Change in fitness when evolving

  [1 being unacceptably slow and 5 being very fast] Q2) Did the combats took place all over the map or in few key locations? [1 being only sparsely located and 5 being focused on a few areas] Q3) Was it easy to navigate your way to your opponents and flags? [1 indicating very easy and 5 being very hard] Q4) Did you run into many dead ends? [1 indicating none and 5 indicating many] Q5) Please rate the pacing of the game. [1 being too fast/slow paced and 5 being very well-paced] Q6) Did the placement of the flags give fair chances for both teams? [1 being very unfair and 5 being very fair]

Table 1 :

 1 Fitness values of generated maps (after 26 populations)

Table 2 :

 2 User Study -Demography Fig. 7: User Study Results


Christoffer Holmgård

Antonios Liapis

Julian Togelius
email: julian@togelius.com

Georgios N Yannakakis
email: georgios.yannakakis@um.edu.mt

Personas versus Clones for Player Decision Modeling

Keywords: Decision making, player modeling, evolutionary computation. 1

The current paper investigates how to model human play styles. Building on decision and persona theory we evolve game playing agents representing human decision making styles. Two methods are developed, applied, and compared: procedural personas, based on utilities designed with expert knowledge, and clones, trained to reproduce playtraces. Additionally, two metrics for comparing agent and human decision making styles are proposed and compared. Results indicate that personas evolved from designer intuitions can capture human decision making styles equally well as clones evolved from human playtraces.

Introduction

The current paper investigates how to create generative models of human player behavior or playing style in games. This can be seen as a method for understanding game-playing behavior. Generative models of playing behavior are also potentially useful in procedural play testing for procedural content generation, for simulation based testing, and within mixed-initiative game design tools for instant feedback during the design process. In other words, agents that play like humans can help understand content as it is being created, by playing it. This paper assumes that game players exhibit bounded rationality, i.e. they play to optimize some objective or set of objectives, but that they might not be very good at it. Playing style could then be characterized by how the players' in-game decisions differ from those of an agent that played rationally (given some set of objectives). We investigate this by using AI methods to train agents that behave rationally, and see to what extent they can predict human players' behaviors.

In previous work we have designed a simple turn-based, tile-based roguelike game which features monsters, treasures and potions in mazes [START_REF] Holmgård | Generative Agents for Player Decision Modeling in Games[END_REF]. 38 players played 10 levels of this game, and we recorded their every action. Next, we analyzed the design of the game to extract a number of possible affordances which we translated into partially conflicting objectives that a player might seek to fulfill (e.g. kill all monsters, avoid danger or get to the exit quickly). Using these affordances we trained agents to play the game rationally for each objective. We call these agents procedural personas. Both Q-learning [START_REF] Holmgård | Generative Agents for Player Decision Modeling in Games[END_REF] and evolutionary algorithms [START_REF] Holmgård | Evolving Personas for Player Decision Modeling[END_REF] were used to train highperforming agents; the evolved agents have the benefit that they generalize to levels they were not trained on. The agents' behavior was compared to playtraces of the human players through a metric we call the action agreement ratio (AAR) which compares agents and humans at the action level. But is this really the right level of analysis for comparing players to agents? It could be argued that the microscopic level of comparing actions gives a biased view of how well an agent's behavior reproduces player behavior, and that it is more interesting to look at behavior on the level of conscious decisions. Further, are we right to assume boundedly rational behavior given some set of objectives? It might be that with the same agent representation, we could train agents that reproduce player behavior better by using the actual playtraces as training data.

The current paper tries to answer these two questions. We propose a new playtrace comparison method (tactical agreement ratio) that instead of asking whether an agent would perform the same action as the player in a given state asks whether it would choose to pursue the same affordance in that state. We also train agents to behave as similarly as possible to human players using playtraces as objectives; we call such agents clones. Clones are compared to personas on both seen and unseen levels, using both action-level and affordance-level comparison. In the following we briefly outline the relations between persona theory, decision theory, player modeling, and the resulting concept of procedural personas. We briefly describe our testbed game, MiniDungeons, and the methods we used to create game playing personas and clones, before we present the results from comparing the resulting agents to the human players.

Related Work

In this section we outline our concept of procedural personas, relating it to its roots in decision theory and the use of personas for game design. Decision Theory and Games. The personas used for expressing designer notions of archetypical player behavior in MiniDungeons are structured around the central concepts of decision theory. Decision theory states that whenever a human makes a rational decision in a given situation, the decision is a result of an attempt to optimize the expected utility [START_REF] Kahneman | Prospect theory: An Analysis of Decision under Risk[END_REF]. Utility describes any positive outcome for the decision maker and is fundamentally assumed to be idiosyncratic. This means that in principle no definite assumptions can be made about what can provide utility to the decision maker. The problem is further complicated by the fact that the effort a decision maker directs toward attaining maximum utility from a decision can be contingent on the expected utility itself. For problems that are expected to provide low utility even in the best case, humans are prone to rely more heavily on heuristics and biases for the decision making process [START_REF] Rubinstein | Modeling Bounded Rationality[END_REF][START_REF] Gigerenzer | Heuristic Decision Making[END_REF]. In practice, however, for structured, well-defined problems, insights from e.g. psychology or contextual information about the decision maker or the decision problem may provide us with opportunities for assuming which decisions are important and which outcomes may be of utility to the decision maker.

As decision spaces, most games are special cases since the available decisions and their consequences are highly structured by the game's mechanics and evaluation mechanisms. Games, through their design, often provide specific affordances [START_REF] Gibson | The Concept of Affordances[END_REF] to the player, and suggest utility for various outcomes. This perspective forms the basis for our understanding of player behavior in our testbed game, as we assume that players are interacting with the game in accordance with the rules, understanding and responding to the affordances of our game. This, in turn, motivates our use of utility for attaining game rule based affordances as the defining characteristics of the personas we develop. Similar theoretical perspectives have been described by other authors, notably Dave Mark in [START_REF] Mark | Behavioral Mathematics for Game AI[END_REF].

When attempting to characterize player decision making styles in games using utilities, it is important to consider the level of decision making relevant for the game, as described in [START_REF] Canossa | Between Intention and Improvisation: Limits of Gameplay Metrics Analysis and Phenomenological Debugging[END_REF]. Here, we model players at both the individual action level as well as at the more tactical level of game affordances. Below we describe how we apply simple utility based agents by using linear combinations of utilities to define personas that represent archetypical decision making styles in our testbed game at two levels of abstraction.

Player Modeling. The concept of personas was first adapted to the domain of (digital) games under the headline of play-personas by Canossa and Drachen who define play-personas as "clusters of preferential interaction (what) and navigation (where) attitudes, temporally expressed (when), that coalesce around different kinds of inscribed affordances in the artefacts provided by game designers" [START_REF] Canossa | Play-Personas: Behaviours and Belief Systems in User-Centred Game Design[END_REF]. Our long term research agenda is to operationalize the play-persona concept into actual game playing procedural personas, by building generative models of player behavior from designer metaphors, actual play data, or combinations of the two.

Generative models of player behavior can be learned using a number of different methods. A key dichotomy in any player modeling approach lies in the influence of theory (vs. data) for the construction of the player model [START_REF] Yannakakis | Player Modeling[END_REF]. On one end, modelbased approaches rely on a theoretical framework (in our case persona theory or expert domain knowledge) and on the other hand, computational models are built in a model-free, data-driven fashion. In this paper, personas represent the model-based approach while what we term clones represent the data-driven approach. Model-free player modeling can be done by imitating the player directly, using supervised learning methods on the playtraces, or indirectly using some form of reinforcement learning to train agents to behave in a way that agrees with high-level features extracted from the playtraces [START_REF] Togelius | Towards Automatic Personalised Content Creation for Racing Games[END_REF]. Evolutionary computation can be used to optimize an agent to behave similarly to a playtrace or optimize it to exhibit the same macro-properties as said playtrace [START_REF] Ortega | Imitating Human Playing Styles in Super Mario Bros[END_REF][START_REF] Togelius | Towards Automatic Personalised Content Creation for Racing Games[END_REF][START_REF] Van Hoorn | Robust Player Imitation using Multiobjective Evolution[END_REF]. Direct imitation is prone to a form of overfitting where the agent only learns to cope with situations which exist in the playtraces, and might behave erratically when faced with new situations. Indirect imitation to a large extent solves this problem by learning a more robust, general strategy, which could be termed a decision making style. In the following section we give a brief introduction to our testbed game.

MiniDungeons

The testbed game, MiniDungeons, implements the fundamental mechanics of a roguelike dungeon exploration game where the player navigates an avatar through a dungeon containing enemies, powerups, and rewards. The turnbased game puts the player in a top-down viewed tilebased 12 by 12 dungeon containing monsters, potions, and treasures. Impassable tiles constitute the walls of the dungeon, while passable tiles contain enemies or items for the player. All of the level is visible to the player who can move freely between passable tiles. When the player moves to a tile occupied by a monster or item, immediately the monster is fought or the item is collected and applied. The player has a 40 hit point (HP) health counter and dies if this drops to zero. Monsters randomly deal between 5 and 14 HP of damage while potions heal 10 HP up to the maximum value of 40 HP. Treasures have no game mechanical effect other than adding to a counter of collected treasure. The game contains one tutorial level and 10 "real" levels. For further details on the test-bed game and discussion of its properties, we refer to [START_REF] Holmgård | Generative Agents for Player Decision Modeling in Games[END_REF]. The necessary data for developing and evaluating the agents was collected from 38 anonymous users who played MiniDungeons online; this resulted in 380 individual playtraces on the 10 MiniDungeons levels provided. The data was subsequently used to evolve clones and baseline agents as described below. Fig. 1 shows a selected level from the game, along with human playtraces from the level, exemplifying the diversity of human decision making styles expressed in even a simple game like this.

Method

This section describes the two agreement ratio metrics used to evaluate persona and clone likeness to humans, the fitness functions for personas and clones, and the evolutionary approach used. The metrics address the problem of decision characterization at two different levels of abstraction. Action Agreement Ratio. The first metric used to evaluate agent to human likeness is the action agreement ratio (AAR). AAR considers each step of a human playtrace a distinct decision. To produce the AAR between an agent and a human player, all distinct game states of the human playtraces are reconstructed. For each game state, the agent being tested is inserted into the game state and queried for the next preferred action, essentially asking: "What would you do?". If the action is the same as the actual next human action, the agent is awarded one point. Finally the AAR is computed by dividing the points with the number of decisions in the human playtrace.

Tactical Agreement Ratio. The second metric used for evaluating the likeness between agents and humans is the tactical agreement ratio (TAR). TAR only considers reaching each distinct affordance in the level a significant decision, ignoring the individual actions in between. For MiniDungeons the affordances considered relevant are: fighting a monster, drinking a potion, collecting a treasure, or exiting a level. For each affordance reached in the human playtrace, the resulting game state is reconstructed and the agent being tested is inserted into the game state. The agent is then allowed as many actions as necessary to reach the next affordance, again asking the question "What would you do?", but at the tactical affordance level. If the next encountered affordance matches the actual next human one, the agent is awarded a point. Finally the TAR is computed by dividing the points with the number of affordances reached in the human playtrace.

Evolved Agent Controllers. The controllers of the game agents are represented as seven linear perceptrons. Each perceptron takes 8 inputs describing safe and risky path distances to the nearest affordances in the map. Further details of the controller representation is given in [START_REF] Holmgård | Evolving Personas for Player Decision Modeling[END_REF]. Controllers are evolved using a (µ+α) evolutionary strategy without self-adaptation. For each generation the top 2% performing elite individuals remain unchanged, the lowest performing half of the remaining population is removed, and single-parent offspring from the remaining individuals are produced to maintain the population size. Finally all individuals not existent in the elite are mutated. Mutation is accomplished by changing each connection weight in the network with a random number drawn from a Gaussian distribution centered around zero with a standard variation of 0.3, a value confirmed as useful for this game by informal experimentation. All experiments are done using a population size of 100 individuals, trained for 100 generations. Controllers are initialized with random connection weights for all connections in the linear perceptrons.

Personas. For the purpose of the experiments 5 individual personas with different utility configurations were defined, based on designer interpretations of likely gameplay in MiniDungeons. The personas were intended to represent five hypothetical extreme decision making styles in interacting with the game: an Exit (E) persona who simply tries to escape the level, a Runner (R) persona who tries to escape the level in as few steps as possible, a Survivalist (S) persona who tries to avoid risk, a Monster Killer (MK) persona who tries to kill all monsters and escape the level, and a Treasure Collector (TC) persona who attempts to collect all treasures and escape the level. The decision making styles are defined by the utility weights presented in Table 1 and serve as a metaphor for the relative importance of the affordances to the archetypical player represented by the persona. When assigned to personas, utility points from a level are normalized by the maximally attainable utility for the same level. Personas are evolved by, for each generation, exposing them to 9 of the 10 levels of MiniDungeons, yielding 50 agents in total. For each generation, their fitness is computed as the average of the normalized utility scores from the seen levels. All subsequent evaluations presented in this paper are done using 10-fold cross validation, i.e., a persona is evaluated on the level which it was not exposed to during evolution.

Clones. Clones, like personas, are evolved by exposing them to 9 of the 10 levels of MiniDungeons. Their fitness value is computed as the average normalized AAR across all 9 seen levels. One clone per player per map is evolved, yielding 380 agents in total. All subsequent tests are done using 10-fold cross validation, evaluating the clones on unseen levels.

Baseline Agents. In order to evaluate the limits of the perceptron-based representation, a set of baseline agents is evolved, one agent for each human playtrace, 380 total. These are exposed to a single level of MiniDungeons. Their fitness scores are computed directly from AAR in an attempt to establish the closest fit to each human player that the representation can achieve.

Results

This section compares the two presented evaluation metrics, and compares the ability of personas, clones, and baseline agents to represent human decision making styles in MiniDungeons. Table 2 shows the mean of the agreement ratios for each kind of agent evolved, using both the AAR and TAR metrics. The ratios indicate that all agents achieve higher agreement with human playtraces when evaluated with the AAR metric than with the TAR metric. Additionally, they indicate that when using AAR clones perform only slightly better than personas (t = -3.23, df = 753.00, p < 0.001) while when using TAR the clones perform substantially better than the personas (t = -39.26, df = 721.51, p < 0.001), as tested using Welch's t test. Using AAR, the baseline agents perform significantly better than both personas and clones (df = 2, F = 62.59, p < 0.001), but when using TAR they perform significantly worse than the clones (df = 2, F = 59.1, p < 0.001), as tested using ANOVA. Table 3 shows which personas exhibited the best ability to represent human playtraces, for each MiniDungeons level and in total. For each human playtrace, the personas with the highest AAR and TAR, respectively, are identified. Both metrics generally favor the Treasure Collector persona as the best match for most playtraces, although there is some discrepancy between the two measures in terms of which personas represent the human playtraces best.

Discussion

It seems that the AAR metric achieves higher agreement ratios than the TAR metric. The two metrics aren't directly comparable, however, as the level's specific layout has a higher influence on the AAR value than the TAR value. Additionally, clones and baseline agents were evolved toward AAR, rather than TAR, for these experiments. Evolving toward TAR might have yielded different results. Other external playtrace comparison metrics could advantageously be used for calibration such as aggregated statistics of in-game event occurrences or other action/edit-distance based methods such as the Gamalyzer metric [START_REF] Osborn | A Game-Independent Play Trace Dissimilarity Metric[END_REF]. The fact that personas and clones perform roughly equally well, when measured by AAR, suggests that the persona method is a viable approach to modeling player decision making styles from expert knowledge. The method is less playtrace-dependent and computationally expensive than the cloning method, but needs an expert game designer. Still, some players may exhibit decision making styles that cannot be captured by the designer's intuition, and would be captured better by the cloning approach, as suggested by the higher agreement ratios obtained from the clones. In order to address this issue, we would propose using observed deviation from initial persona behavior to guide the evolution of new utility configurations for subsequently derived personas, combining the persona and cloning approaches.

Conclusion

This paper presented two methods of modeling player decision making styles. One was based on personas, evolved from designer expert knowledge, the other was based on clones, based on human playtraces. Two metrics were used to evaluate the agents' abilities to represent human decision making styles. The methods were shown to perform almost equally well when compared at the action level, while clones performed better than personas when compared at the affordance level.

Fig. 1 .

 1 Fig. 1. Heatmaps of six selected human playtraces in Level 2 of MiniDungeons, showing a diversity of player decision making styles

Table 1 .

 1 Utility weights for the five designed personas

Table 2 .

 2 Agreement ratios for personas, clones, and baseline agents

	Affordance E	R	S	MK TC	Agent	Metric Mean SD
	Move	-0.01 -0.02 -0.01 -0.01 -0.01	Personas	AAR 0.75 0.08
	Monster				1	Clones	AAR 0.77 0.08
	Treasure				1	Baseline Agents AAR 0.81 0.09
	Death			-1		Personas	TAR 0.62 0.11
	Exit	0.5 0.5 0.5 0.5 0.5	Clones	TAR 0.66 0.13
						Baseline Agents TAR 0.61 0.13

Table 3 .

 3 Best persona matches based on Action Agreement Ratio (AAR) and Tactical Agreement Ratio (TAR), respectively

Acknowledgements. We thank the players of the game. The research was supported, in part, by the FP7 ICT project C2Learn (project no: 318480) and by the FP7 Marie Curie CIG project AutoGameDesign (project no: 630665).