ARCHITECTING RUBE WORLDS:
A METHODOLOGY FOR CREATING VIRTUAL ANALOG DEVICES AS
METAPHORICAL REPRESENTATIONS OF FORMAL SYSTEMS
KRISTIAN LINN DAMIKJER
A THESIS PRESENTED TO THlE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THlE REQUIREMENTS FOR THlE DEGREE OF
MASTER OF SCIENCE
UNIVERSITY OF FLORIDA
Kristian Linn Damkjer
Dedicated to my wife Bryony,
my parents Keith and Deborah Damkjer,
and my sister April Derato,
for their constant support and encouragement
I would like to sincerely thank my advisor and committee chairman, Dr. Paul A. Fishwick, for his constant instruction, guidance, and support throughout my graduate studies at the University of Florida. I would also like to extend my gratitude to Dr. Abdelsalam Ali Helal and Dr. Joachim Hammer for their time and interest in serving on my thesis committee.
I sincerely appreciate the time and devotion of my companions on the University of Florida RUBE Project, Mr. Minho Park, Mr. Jinho Lee, and Ms. Hyunju Shim, who made this research truly an enjoyable experience.
Many thanks go to my fellow graduate students in the digital arts and sciences program, especially Mr. John Hays and Mrs. Joella Walz, for their companionship and support through the highs and lows of graduate studies. I will never forget their friendship.
I am deeply grateful to my parents, Keith and Deborah Damkjer, for their constant prayer, support and encouragement, and to my wife, Bryony, whose love, support, and willingness to share these last two wonderful, though often trying, years with me will be a treasured memory for years to come.
Finally, I owe an extreme debt of gratitude to everyone who has prayed for me
throughout my academic endeavors and to God for His many blessings; may He continue to guide me and my family throughout our lives.
TABLE OF CONTENTS
A CK N OW LED GM EN TS . iv
LIST OF TABLE S . ix
LIST OF FIGU RE S . x
A B S T R A C T . x iii
I IN TR OD U CTION . I
A Brief H istory of RUBE . I
C h a lle n g e s . 3 O rganization of Thesis . 4
2 BA CK GR O U ND . 5
M athem atics as a Language . 5
M athem atics in M odel D esign and Sim ulation . 6
S c h e m a s . 7 Causation and A nim ism . 10
M anipulative Objects: Play and the Em otional Factor . I I
Im m ersion and Engagem ent . 12
M ulti-Sensory M odel R epresentation . 13
S u m m a ry . 1 3
3 CON CRETIZIN G CON TEN T D EFIN ITION . 15
O v e rv ie w . 1 5
R efining the M X L Schem a . 15
M X L D ata Types . 16 M X L R oot and Top-Level Elem ents . 17
The M XL root elem ent . 17
The m odels group . 18
The sim ulation elem ent . 18
R em oval of the com m ent elem ent . 19
M odel D efinition Elem ents and G roupings . 19
H andles: input and output . 20
M o d el categ o rie s . 2 1
Generalizing N ode Definition Structure . 21
4 EXTENDING RUBE TO SUPPORT THE MULTI-MODELING PARADIGM . 24
M XL External M odel File Referencing . 24
M XL M assive M erge . 26
M o d e l L ib ra rie s . 2 7
5 M ODEL DEFINITION . 28
O v e rv ie w . 2 8
Identification of M odel Subcom ponents . 28
Self-Sim ilarity of M odel Functionality and Interface . 28
Logical and/or Functional Groupings . 29 Object-Oriented Approach . 30
Optimizing M odel Definition . 30
6 M ETAPH ORS TO M ODEL BY . 32
M o d e l S tru c tu re . 3 3
M o d el M etap h o rs . 3 3
M odels are environm ents . 33
M odels are containers . 33
M odels are buildings . 34 M odels are m achines . 34
Handle M etaphors . 34
Handles are thresholds . 34 H an d le s are v alv e s . 3 5
Handles are ports or outlets . 35
Handles are radio towers . 35
Handles are puzzle pieces . 35
Handles are fasteners . 36
N o d e M etap h o rs . 3 6
N odes are containers . 36
N o d e s are ro o m s . 3 7
N odes are m achine parts . 37
E d g e M etap h o rs . 3 7
E d g e s are co n d u its . 3 8
Edges are a line-of-sight . 38
M o d e l E x e cu tio n . 3 8
In p u t M etap h o rs . 3 8
In p u t is fu e l . 3 9
In p u t is a so u rc e . 3 9
Input is an agent entering . 39
In p u t is a m e ssag e . 3 9
In p u t is an a ctio n . 4 0
D ata Flow M etaphors . 40
D ata flow is a transfer of energy . 40
D ata flow is flow ing w ater . 40
D ata flow is a m oving agent . 41
D ata flow is a transm ission . 41
A ctive N ode M etaphors . 41
A ctive node is anim ation . 41
A ctive state is agent location . 42
A ctive state is a color or texture . 42
A ctive node is a sound . 42
Output M etaphors . 42
Output is a m anufactured product . 42
Output is a sound . 43
Output is an agent exiting . 43
O u tp u t is a re p ly . 4 3
Output is a reaction . 43
Output is a piece of art . 43
7 MODEL PRESENTATION DESIGN METHODOLOGY . 44
M o d e l P ro to ty p e s . 4 4
U sing Groups and Transform s to M im ic Content Structure . 46
L o g ic al G ro u p s . 4 6
C o n ta in e rs . 4 7
M odel H ierarchy and Geom etry . 49
8 COM M EN T PAR SER . 52
D e sc rip tio n . 5 2
S tru c tu re . 5 2
M a p p in g . 5 6
9 BARN SLEY FERN IF S . 63
D escription and D efinition . 63
S tru c tu re . 6 6
IF S F B M . 6 6
Transform ation Selection FSM . 67
Augmented Affine Transformation Matrix Calculation FBM . 68
Sine, Cosine, and N egative Sine Calculation FBM . 69
Three-By-Three By Three-By-Three Matrix Multiplication FBM . 69
Row -Colum n Product (V ector M ultiplication) FBM . 70
N ew Point Calculation FBM . 71
M a p p in g . 7 2
10 C O N C L U SIO N . . . 75
Summary of Results and Future Work . 75 A uxiliary O bjectiv es . . . 76
A u to m atio n . . . 7 6 Extension to Representations other than VRML . 77
A M X L SCH EM A D EFIN ITIO N . 78
B MODEL PRESENTATION EXAMPLE STRUCTURE . 85
C MODEL DEFINITION FOR THE C-STYLE COMMENT SCANNER . 89
D MODEL DEFINITION FOR THE BARNSLEY FERN IFS . 97
L IST O F R E F E R E N C E S . 122
B IO G R A P H IC A L SK E T C H . 124
LIST OF TABLES
3-1. List of original M XL elem ents w ith descriptions . 16
3-2. M XL data types . 17
3-3. M XL m odel elem ents . 22
8- 1. Scanner transitions sum m arized . 55
9-1. A ffine transform ations in R 3 . 65
LIST OF FIGURES
1-1 . R U B E arch ite ctu re . 2 3-1. M XL top-level elem ents . 18
3-2. The m odels group . 20
3-3. The handles group . 20
4-1. Parent m odel external reference . 25
4-2. Externally defined sub-m odel (m ySubM odel.xm l) . 25
7-1. Sim ple FBM block diagram . 44
7-2. Sim ple FBM M XL definition . 45
7-3. A VRM L presentation file show ing m odel prototypes . 46
7-4. M odel com ponent grouping in presentation files . 47
7-5. Containing transform s . 48
7-6. Prototype instances and atom ic structures . 50
8-1. Com m ent scanner FSM . 53
8-2. Com m ent scanner FBM . 53
8-3. Com m ent scanner virtual w orld . 56
8 -4 . B lo c k : F ile . 5 8
8 -5 . B lo c k : S c a n n e r . 5 8
8 -6 . B lo c k : P a rse r . 5 9
8 -7 . S tate : P ro g ra m . 5 9
8 -8 . S tate : S la sh . 6 0
8-9. State: CPP . 60 8-10. State: C. 61 8-11. State: Star . 61 8-12. Transitions . 62 8-13. Signage indicating transition directionality . 62 9-1. The generalIF S FflM. 66 9-2. Transformation states FSM. 67 9-3. Augmented affine transformation matrix calculation. 68 9-4. Sine, cosine, and negative sine calculation FBM . 69 9-5. M atrix m ultiplication FBM . .70 9-6. Row-column product calculation FBM. 71 9-7. New point calculation FBM . 72 9-8. Bamnsley Conservatory. 72 A-i1. MXL schema definition . 78 B-i. Model presentation example file . 85 C-i1. comment.xml . 89 C-2. comment sub.xml. 90 C-3. comment~js. 91 D-i1. Bamnsley.xml. 97 D-2. States.xml. 100 D-3. Affine.xml . 102 D-4. SinCosCalc.xml. 108 D-5. MatMult.xml . 109 D-6. RCCalc.xml . 114 D-7. PointCalc.xml. 115
D-8. Bamsley.js. 117 D-9. math.j s. 121
Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science ARCHITECTING RUBE WORLDS:
A METHODOLOGY FOR CREATING VIRTUAL ANALOG DEVICES AS
METAPHORICAL REPRESENTATIONS OF FORMAL SYSTEMS By
Kristian Linn Damkjer
Chair: Paul A. Fishwick
Major Department: Computer and Information Science and Engineering
My research attempts to establish a methodology for creating presentation worlds that ensures proper correlations between the model content and presentation. My approach uses virtual analog devices and architectural constructs as metaphorical representations of model structure, though I also provide a generalized methodology for
using metaphorical mappings and grouping structures to ensure strong correlations. This research has also resulted in the restructuring of the Multi-model Exchange Language (MXL)-one of several XML applications employed in RUBE-for better model content definition, as well as extended support for multi-modeling in RUBE.
A Brief History of RUBE
As the names suggest, the focus of SimPack and its successors is model simulation, not model development. To augment the simulation toolkits, specifically SimPack and OOSIM, with multi-model development capabilities, the Object-Oriented Physical Multimodeling (OOPM) toolkit (1996) was developed . This toolkit provided a graphical interface for developing multi-models with support for several model types and is regarded as the predecessor to the RUBE Project .
The RUBE Project began in 2000 as a continuation of research in multi-modeling and web-based modeling and simulation with the initial purpose of facilitating dynamic multi-model construction and component reuse in three-dimensional distributed
immersive environments. To this end the Virtual Reality Modeling Language (VRML) was employed as the primary language for specifying scene geometry and presentation dynamics. Initial work in this arena was done using the VRML "Prototype" extensibility mechanism ; however, that approach has recently been replaced with the Extensible Markup Language (XML) based framework that now serves as the basis for RUBE . This approach more strongly decouples the model content from the model presentation which in turn allows for better component reuse and customization of model representation.
The initial framework designed by Kim has been modified slightly to accommodate recent developments in our research and the current structure is outlined in Figure 1-1.
Scene File (VRML) (.Model File (MXL) Nj Sinaok.1 jj MXL o. XL Transla or NI STVRMLtoX3D DXL DXL o . STransla or
Model Fusion Engine (XSLT) X3D
X3D toVRML (XSLT)
Simulation File (VRML) O3 User Generated Files rl rube Generated Files
0 rube Generated Files Requiring User Edits El Translation Engines
Figure 1-1. RUBE architecture
In the RUBE pipeline, the developer provides as input scene files describing how the model should be presented, model files defining the model topology, and semantic files defining how the model should execute (simulation rules). These files are then merged using XML Style-sheet Language Transformation (XSLT), Document Object Model (DOM), and custom-developed translation mechanisms to produce a single presentation file with an interface for initializing simulation execution and hooks for simulation output. The developer modifies this file to use the simulation output data to drive the presentation dynamics while the simulation executes.
With more robust modeling and simulation toolkits-affording greater possibilities in model definition, presentation, simulation, and distribution-and the fact that model definition is ideally decoupled from its presentation, the need for a formalized methodology for developing models and their corresponding presentations increases.
To this end the primary objectives of this research are to refine the Multi-model Exchange Language (MXL) to remove ambiguities in model definitions and to facilitate language extensions in the form of adding new model types; to extend the RUBE framework to allow for model definition and semantic component reuse; to explore model definition development to take advantage of the multi-model paradigm; to explore metaphorical mappings for model representations; and to establish a methodology for creating presentation worlds for models developed with the RUBE framework. Auxiliary objectives of this research are to explore the possibility of partially automating the established methodology and to investigate extending the methodology to presentation vehicles other than VRA/1L.
Organization of Thesis
A philosophical background of the RUBE approach to modeling and simulation is discussed in Chapter 2. Chapter 3 details the revisions made to MXL and the model semantics definition structure. Chapter 4 details the extensions to the RUBE framework to fully support multi-modeling. Chapter 5 explores model definition strategies. Chapter 6 outlines metaphorical mappings to use in model representations. Chapter 7 discusses the methodology to be used in presentation development. Chapters 8 and 9 are summaries of case-study implementations. Conclusions, future work, and auxiliary objectives are discussed in Chapter 10.
Mathematics as a Language
Mathematics is often thought of as an eloquent language, highly formalized and structured, able to capture endless representations of phenomena in the world around us succinctly and efficiently. It has its own syntax, grammar, and an alphabet that is the product of centuries of abstraction and stylization due to economics and efficiency . As higher mathematics has evolved, new symbols, syntax, and grammar have been created to extend the mathematical language, albeit non-uniformly and non-universally, resulting in "dialects" of mathematics. In general, however, mathematics is still regarded as a fairly universal language, allowing for the exchange of formalized ideas and concepts in spite of socio-economic as well as natural language barriers.
The power of mathematics to represent formal structures is inherent in its abstract nature. However, with this power of generalization comes a loss of inherent comprehension. Gennady Uzilevsky's hierarchy of languages, which organizes languages and codes based on their inherent comprehensibility among humans, places mathematics into categories that are among the least inherently comprehensible: iconic languages, symbols, and meta-languages. Hughes observes the following when commenting on this hierarchy:
Only the top five languages are what we think of as "languages": these are the least
powerful of the 12 . Most human communication is done by the lower seven,
of which the most easily named (in decreasing order of power) are gesture, images,
movements, emotional conditions, music and color. [13:180]
Thus, mathematics should be augmented with those languages that are more universally comprehensible, a feat we accomplish or observe readily in literature, oration, and fine art.
Mathematics in Model Design and Simulation To model is to abstract from reality a description of a dynamic system. Modeling
serves as a language for describing systems at some level of abstraction or,
additionally, at multiple levels of abstraction. Models are used for the purpose of communicating with each other-the alternative being that two people wishing
to discuss dynamics would be forced to work with the real system under
Mathematics, because of its powerful ability to generalize formal structures, is a natural choice for the language of modeling. It is very simple, in mathematical terms, to describe well defined dynamic systems as sets of quantitative, stochastic, and logical relations: The resulting deterministic system is defined as the following 7-tuple [8:4647]:
(T, U, Y, Q, Q, (5, A)
" T: the time set. For continuous systems T = R (the real numbers), and for discrete
systems T = Z (the integers).
" U: the input set. Contains the possible values of the input to the system.
" Y: the output set.
Q : the state set.
Q: the set of admissible (or acceptable) input functions. T -> U
5: the transition function. 5:QxTxTxU->Q
A: the output function. A:Q->Y
Through mathematics we are able to not only formalize the structure of dynamic systems, but also to virtually execute and analyze the systems. We can easily and safely
manipulate aspects of the system to observe how the system responds and behaves as a whole. We can apply these observations back to the real-world phenomena being modeled, provided that the model is accurate.
Despite this power, the primary purpose for modeling is not simply for description and analysis of a system, but for communication with others. Unfortunately, the traditional way of representing mathematical concepts with calligraphic notation often fails in this endeavor . We should attempt then to extend our representation of mathematical models. After all, the exchange of ideas is the raison dWre of modeling. This extension of the mathematical model is the basis for the University of Florida's RUBE Project [5, 8, 12]. Mathematics, in the traditional sense, has been used to represent both the content and presentation of model information; RUBE [5, 12] attempts to separate one from the other and allow users to interpret models using their own presentation schemes .
RUBE [5, 12] is perhaps unique in its approach to modeling and simulation in that it takes a narrative approach to representing models. By doing so it attempts to draw on schemes not only to improve comprehension of models, but also to create a synergistic representation that addresses both of Bruner's "two modes of thought".
There are two modes of cognitive functioning . each producing distinctive ways of ordering experience, of constructing reality. The two (though complementary)
are irreducible to one another. Efforts to reduce one mode to the other or to ignore
one at the expense of the other inevitably fail to capture the rich diversity of
thought. [2: 11 ]
The two modes of cognitive functioning are the paradigmatic, which attempts to achieve an ideal formalization or mathematical explanation through categorization and conceptualization of a system, and the narrative, which instead deals with the "human or
human-like intention and action and the vicissitudes and consequences that mark their course." [2:13]
By incorporating schema and narrative in our model representation we are attempting to improve comprehension of a model by providing an immediately recognizable and familiar framework for the user. To successfully incorporate schemes, however, we must first make some observations about how they are defined and operate, then establish a means of merging the narrative structures with the paradigmatic ones.
There are several parallels between Bruner's schema definition and our formalized model definition. Schemas are composed of states, crises, and redresses with the possibility for cycles and branches in the narrative structure. The result of realizing this structure is story . Similarly, recall that dynamic systems are composed of states, transitions, and next-states, and the result of simulating the system is output. The narrative steady state is breached by some external force thus instigating the steady-statecrisis-redress cycle . In much the same way, systems transfer between states based on input. Finally, both establish a chronological framework (even if narrative is not presented in a strictly linear fashion). The mapping between the two is fairly direct: schema-states map to system-states, crises map to transitions, redresses map to nextstates, steady-state breach forces map to system input, and story maps to system output.
Note also that we can extend this kind of schema mapping to system multimodeling in which we can have any combination of multiplicity, heterogeneity, and hierarchy of models. Multiplicity implies several model instances, heterogeneity implies several model types, and hierarchy implies several model layers in a nested structure . Black and Bower's observations on Episodes as Chunks in Narrative Memory
conform nicely to the multi-model structure. They observe that stories consist of narrative elements that are interconnected to produce a coherent whole. They also observe that the resulting network of interconnected elements can be represented in one of two ways, either as consisting of several clustered sub-groups, or a resulting "macro"structure and "micro" -structure . Both views parallel the multi-model paradigm and I would further argue that neither is explicitly the normative structure.
To combine the paradigmatic and narrative modes of model representation in RUBE we employ yet another narrative device: metaphor. We could have similarly chosen to use the mathematical device function. Both provide a direct mapping from one domain to another; however, metaphor is explicitly a one-to-one mapping, a trait that ensures consistency between the narrative schema and the formal system definition. Note that this merge will result in not only a representation of the model output, but also of the model structure and functionality. This representation goes far beyond that of the simple graph or diagrammatic presentation . Once the narrative world and the paradigmatic model have been merged we can proceed to simulate the model. Assuming that the model requires no direct input from the user to execute, what the user will observe are elements of the narrative domain interacting with each other as a representation of the model execution. Users will likely unconsciously compose a story based on the narrative schema to describe and explain the interactions that they observe. This is a result of causation and perceived intention as well as drawing on relevant scripts within the schema to shape our perception, navigation, and interaction within a given scenario [6, 13].
Causation and Animism
Bruner recounts a discourse by Baron Michotte in which he demonstrates that when subjects observed objects moving with respect to each other within highly limited confines, the subjects impose causality. They saw the objects' movements as affecting and being caused by the movements of other obj ects in the scene. It was found that the sp ati al -temporal relationships of the objects could be manipulated to invoke various types of behavior interpretation such as "launching," "dragging," and "deflecting" . To visualize this behavior, consider the classic arcade games of Pong and Arkanoid in which a ball is "launched" across the screen at a target and is repeatedly "deflected" by other objects in the scene.
Bruner further relates the work of Heider and Simmel who used methods similar to Michotte's to demonstrate the irresistibility of "perceived intention". Subjects again observed a short animated film and again bestowed behavior to the objects, yet this time the behaviors were perceived as being intention-driven. Stewart demonstrated that again, by manipulating the spatial -temporal relationships of the objects, other apparent intention-driven behaviors could be invoked, for example: "searching," "pursuit," and "persistence" .
Why are these results so interesting to RUBE model applications? The answer is simple, they imply that users will create their own narrative to explain the model presentation in terms that they already understand and will thus be able to achieve an initial understanding of the model concept or perhaps a deeper comprehension. This understanding will be made further concrete if the user already has a deep understanding of the schema in which the model is being presented.
Recall, however, that one of the greatest strengths of the mathematical model is that it allows us to safely and efficiently manipulate aspects of the system to observe how the system as a whole responds . To achieve this we must have some form of interaction with the schema world and this is achieved through manipulative objects.
Manipulative Objects: Play and the Emotional Factor
By extending our representation of models to include schemes, we have extended the ways in which users can understand our model. By introducing manipulative objects in our schemes, we can allow the user to interact with and affect model execution. The use of manipulative objects as a learning aid has been shown to be a positive influence on learning and comprehension of mathematical concepts in general. This benefit has been shown to hold across grade level, ability level, and topic. The use of manipulative objects alone, however, does not guarantee successful comprehension of concepts . In RUBE, manipulative objects are incorporated in such a way that allows the user to play within the confines of the model world in much the same way that a child might role-play with toys . This act of playing can contribute immensely to our comprehension of systems and their complexity. Through this kind of hands-on exploration of "possibility spaces" we glean terrific amounts of information about a system .
By incorporating interaction, the user also becomes an integral part of the narrative schema and as a result tends to respond emotionally to events in the model world. Being tightly coupled with the model world and having a high level of perceived control over its outcome is amazingly parallel to computer gaming, which simultaneously engages emotions of anticipation, joy, and acceptance. Should the model produce unexpected results or behave in an unexpected manner (or even crash) even more emotions are engaged resulting in surprise, anger, and frustration . Coupling these emotional
responses with the presentation of the information has the added benefit of improving comprehension and retention of concepts .
Hughes observes that, "Play, in the pure sense of simply controlling and
manipulating things, in a safe environment, for no particular purpose, is crucially important for humans."[ 13:170] He notes that it is often easier to give users this "fun of control" if there is an underlying serious didactic purpose and is surprised to find that educational software is amazingly void of such "virtual toys" that allow for this kind of playfulness and emotional reward . Thus the potential for a system like RUBE is great since it engages the user at so many levels and can also create an immersive experience.
Immersion and Engagement
The great potential of a system like RUBE for representing mathematical models is that-beyond merely representing systems for communication and analytic purposesthere exists potential for the experience of working with the models to be quite pleasurable. Because RUBE is designed to operate in much the same way as interactive narratives and computer games, this pleasure can be both immersive and engaging. The immersive nature of RUBE is derived from the way that any given model presentation is derived with a single schema in mind. While interacting with the single model presentation the schema remains constant allowing the user to draw on the scripts commonly associated with that schema to drive perception of the model and the various ways to interact with it . Of course, much of this is dependent on the model presentation design. Interaction with the model should be well guided and should conform to the schema world. Failure to do so will likely have the same result as in other media: frustration . RUBE invites users to have several immersive encounters with the models by decoupling model content and presentation. This allows the user to apply
several presentations to the same model, much like a sort of "schema style-sheet". It is also hoped that as users gain understanding of models they will seek out secondary sources to augment their schemes for understanding the models and will then revisit the RUBE models to gain greater insight through engaging experiences.
Multi-Sensory Model Representation
Finally, to enhance the immersive experience and provide further language cues, the worlds that are developed for RUBE are usually multimedia applications. Humans are quite obviously multimodal beings. Our senses are the mechanisms through which we perceive our world and are integral to our understanding of it. Mixing several types of media has the potential to be "sensory dynamite" . Adding one form of media to another can dramatically change our perception of the original media. Hollywood has used this for years in movies. By laying a soundtrack under the visuals, directors are able to subtly manipulate the mood of a scene and drive audience expectations. Incorporating audio with graphics not only improves the perceived quality of the graphics, but also provides a means of implying information about the graphics and their interactions [I I]. In short, the more cues our model representation can give, the more modes of interpretation we can potentially employ; however, Hughes cautions that the use of nonverbal cues should be used subtly. We are highly sensitive to them and misuse (intentional or not) results in being highly intrusive and can very easily have the opposite of the desired effect [I I].
We understand that traditional methods of conceptualizing and representing dynamic systems models abstract the systems to such a level that they are no longer readily comprehendible without formal training in simulation and model design. While
this level of abstraction and formalism is useful for the conceptualization and simulation of the model, we attempt to counter the effects of abstraction on the presentation of the model by using multi-media applications and schema frameworks in our worlds. This approach has a high potential for creating immersive and emotionally engaging experiences when working with the models. This in turn has benefits of improved comprehension and retention of the information presented in the model.
CONCRETIZING CONTENT DEFINITION Overview
MXL was developed to allow for the formal definition of model topologies and the establishment of simulation frames. The initial version of MXL displayed several weaknesses. Prominent among these were tendencies of allowing ambiguity in model definition and of being unintuitive due to its abstract nature as well as not truly allowing component reuse. These weaknesses necessitated a restructuring of MXL. Since MXL is an XML application, its syntax and grammar are defined using the XML Schema Definition Language (XSD) also an XML application. Thus, a restructuring of MXL meant refining the MXL schema definition.
Refining the MLXL Schema
The current version of MXL was created to address the following issues that were surfaced in the original version:
* Model content and presentation were too tightly coupled.
* Model component granularity was too abstract.
* Subtleties of unique model types were impossible to implement.
* Multi-model nesting rules were over-generalized.
* Model definitions included redundant or superfluous information.
* Simulation frame definition was both incomplete and contained superfluous
Table 3-1 provides a summary of the original version of MXL, since that was the
starting point used in refining the MXL schema.
Table 3-1. List of original MXL elements with descriptions Element Attributes Children Description
MXL root elementa
type="xsd:string" defines topology and behavior of a model
type="xsd:string" defines objects and the connectivity of each
object in the model