Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Spatio-Temporal Reasoning Over Play-Scripts for Artificially
Intelligent Characters
Christine Talbot
Richard Burton in Ham...
Background
2
of 49
Virtual Character Positioning
3
Back-
ground
EMA: A process model of appraisal dynamics (Stacy C. Marsella,
Jonathan...
of 49
Mocap Files and Hand-Coding
4
Back-
ground
Discovery News – Avatar: Motion Capture Mirrors Emotions
http://news.disc...
of 49
BML and BML Realizers
5
Back-
ground
SmartBody Path Planning
http://smartbody.ict.usc.edu
Hamlet played by robots
Un...
of 49
<act><participant id="GRAVEDIGGER2" role="actor" /><bml><gesture lexeme="POINT" target="GRAVEDIGGER1"
/></bml></act>...
of 49
So How Do We Do It?
7
Back-
ground
A: Excuse me…
B: Can I help you?
A: Yes, where is the post office?
B: Go straight...
Play-Scripts
8
of 49
GRAVEDIGGER1
Give me leave!
(GRAVEDIGGER2 sits on the side steps)
(Pointing down into the grave)
Here lies the water...
of 49
The Baseline
Play-
Scripts
Hamlet
Act 5, Scene 3
(Graveyard Scene)
Richard Burton in Hamlet, directed by Sir Gielgud...
of 49
11
Play-
Scripts
Sentence
Subject NP Actor/Noun
VP
VP Action/Verb
NP Target/Noun
Example Nouns:
GRAVEDIGGER1
GRAVEDI...
of 49
What did it look like?
12
Play-
Scripts
of 49
How Did We Do?
13
Play-
Scripts
HamletGraveDigger1
Ground Truth Simple NLP Method
Character Traces Over Time for Ent...
Spatial Rules
14
of 49
What’s Next?
 Applying Spatial Rules
 Conversational Spatial Rules
 Grouping Spatial Rules
 Theatre Rules
 Gene...
of 49
Architecture
16
Spatial
Rules
BML
of 49
Architecture
17
Spatial
Rules
of 49
Rules Engine Logic
18
Spatial
Rules
of 49
Position Results
19
Spatial
Rules
C. Talbot and G. M. Youngblood. Shakespearean Spatial Rules. In Proceedings of the...
of 49
Position Results
20
Spatial
Rules
C. Talbot and G. M. Youngblood. Shakespearean Spatial Rules. In Proceedings of the...
Implied Movements
21
of 49
Grave Digger 1 Initiative
22
Implied
Mvmt
of 49
Implied Motion
23
Implied
Mvmt
To be, or not to be—
that is the question:
Whether 'tis nobler in
the mind to suffer ...
of 49
24
Implied
Mvmt
Information Captured
 For Each Line of Speech:
 Movement by Speaker or Other
Character
 Number of...
of 49
25
Implied
Mvmt
Machine Learning
 RTextTools in R
 Maximum Entropy
 Random Forests
 Boosting
 SVM
 Specific Mo...
of 49
26
Implied
Mvmt
Learning Combinations
 Movement Classifications
 Specific Movements
 Movement High-Level Categori...
of 49
27
Implied
Mvmt
Evaluation Criteria
 Overall Accuracy
 Recall
 Precision
 F1 score
 F0.5 score
 Matthews Corre...
of 49
28
Implied
Mvmt
Evaluation Criteria
 Overall Accuracy
 Recall
 Precision
 F1 score
 F0.5 score
 Matthews Corre...
of 49
29
Implied
Mvmt
Evaluation Criteria
 Overall Accuracy
 Recall
 Precision
 F1 score
 F0.5 score
 Matthews Corre...
of 49
Best Performing
30
Implied
Mvmt
Boosting
SVM
MaxEnt
RandForest
Any Mvmt, POS, Unigrams Any Mvmt, No Text, Unigrams
G...
of 49
Best Performing
31
Implied
Mvmt
Boosting
SVM
MaxEnt
RandForest
Random
Any Mvmt, POS, Unigrams Any Mvmt, No Text, Uni...
Incorporating Human
Characters
32
of 49
So Far…
33
Humans
Sentence
Subject NP Actor/Noun
VP
VP Action/Verb
NP Target/Noun
Speech Movement
Grouping Spatial R...
of 49
Adding a Human
 Move correctly, on-time
 Move correctly, wrong time
 Move incorrectly, on-time
 Move incorrectly...
of 49
35
Humans
 Equilibrium of Forces
 Aesthetically Balanced
 Easy to See Nodes
 Crossings-Free (some)
 Fixed Nodes...
of 49
Force-Directed Graph Structure
 Node Representations:
 Characters
 Human
 Target/Marks/Pawns
 Audience
 Centra...
of 49
Force-Directed Graph Structure
 Node Representations:
 Characters
 Human
 Target/Marks/Pawns
 Audience
 Centra...
of 49
Force-Directed Graph Functions
 Adding Characters
 Characters Leaving
 Moving Characters
 Human Moves
38
Humans
...
of 49
A
Force-Directed Graph Functions
 Adding Characters
 Characters Leaving
 Moving Characters
 Human Moves
39
Human...
of 49
Force-Directed Graph Functions
 Adding Characters
 Characters Leaving
 Moving Characters
 Human Moves
40
Humans
...
of 49
Force-Directed Graph Functions
 Adding Characters
 Characters Leaving
 Moving Characters
 Human Moves
41
Humans
...
of 49
42
Humans
Forces and Time
δ= distance between nodes
L = length of stage depth
α = constant
C. Talbot and G. M. Young...
of 49
Evaluation Approaches
 Optimal arrangement based on current relationships
 Time-based / sequential arrangement thr...
of 49
Humans
Arrangement Based Upon Relationships
44
of 49
Humans
Arrangement Based Upon Relationships
45
of 49
Humans
Arrangement Based Upon Relationships
46
of 49
47
Humans
Evaluation Criteria
Appropriate arrangement based on current relationships
 Even Vertex Distribution
 Me...
of 49
48
Humans
Results
100s of Random Relationship Scenarios
 Even Vertex Distribution
 3.14 feet (SD=1.54) between cha...
of 49
Incorporating Forces for Time-
Based Arrangements 49
Humans
of 49
Evaluation Criteria
 Occlusion
 Clustering
50
Humans
of 49
51
Humans
ResultsCase#
Case
Description
Avg
Occlusion
Average
Clustering
X
Average
Clustering
Y
0Baseline All AI 3.6...
of 49
52
Humans
ResultsCase#
Case
Description
Avg
Occlusion
Average
Clustering
X
Average
Clustering
Y
0Baseline All AI 3.6...
User Studies
53
of 49
Block World 3D Representation
54
User
Studies
of 49
Survey Questions
1. Characters showed evidence of
engaged listening
2. Characters appeared to perform
suitable movem...
of 49
Results
56
User
Studies
StronglyDisagreeNeutralStronglyAgree
Mean
of 49
Results
57
User
Studies
StronglyDisagreeNeutralStronglyAgree
Mean
Planned Future Work
58
of 49
Planned Future Work
 Additional User Studies (shortened)
 Random
 Baseline
 NLP
 NLP + Rules
 NLP + Rules + FD...
Summary
60
of 49
 Proposed Play-Scripts
 Applied NLP
 Added Rules Engine
 Evaluated Speech for Implied Movement
 Incorporated Hu...
of 49
Christine Talbot
ctalbot1@uncc.edu
Questions?
 C. Talbot. Creating an Artificially Intelligent Director (AID) for T...
Upcoming SlideShare
Loading in …5
×

Voices 2015 - Spatial Temporal Reasoning Over Play-Scripts for Artificially Intelligent Characters

629 views

Published on

Spatial Temporal Reasoning Over Play-Scripts for Artificially Intelligent Characters
Christine Talbot, University of North Carolina/Salesforce.com

www.globaltechwomen,com

Session Length: 1 hour

The objective of this session is to present current research in AI consisting of creating an Artificially Intelligent Director. The key here is the set of algorithms utilized to transform a standard play-script into spatial temporal directions for AI characters within a virtual environment. We review not only a fully AI scripted set of characters, but also the incorporation of one or more human characters in the scene, which affects the AI characters’ movements. We will review the results of experiments which show both the quantifiable evidence of the algorithms’ accuracy, as well as the qualitative results of user perception of these scenes.

Published in: Technology
  • Be the first to comment

  • Be the first to like this

Voices 2015 - Spatial Temporal Reasoning Over Play-Scripts for Artificially Intelligent Characters

  1. 1. Spatio-Temporal Reasoning Over Play-Scripts for Artificially Intelligent Characters Christine Talbot Richard Burton in Hamlet, directed by Sir Gielgud http://www.youtube.com/watch?v=XRU5yLgs0zw&feature=player_detailpage
  2. 2. Background 2
  3. 3. of 49 Virtual Character Positioning 3 Back- ground EMA: A process model of appraisal dynamics (Stacy C. Marsella, Jonathan Gratch), In Journal of Cognitive Systems Research, volume 10, 2009. Ada and Grace: Toward Realistic and Engaging Virtual Museum Guides (William Swartout, David Traum, Ron Artstein, Dan Noren, Paul Debevec, Kerry Bronnenkant, Josh Williams, Anton Leuski, Shrikanth Narayanan, Diane Piepol, H. Chad Lane, Jacquelyn Morie, Priti Aggarwal, Matt Liewer, Jen-Yuan Chiang, Jillian Gerten, Selina Chu, Kyle White), In Proceedings of the 10th International Conference on Intelligent Virtual Agents (IVA 2010), 2010.
  4. 4. of 49 Mocap Files and Hand-Coding 4 Back- ground Discovery News – Avatar: Motion Capture Mirrors Emotions http://news.discovery.com/videos/avatar-making-the-movie/
  5. 5. of 49 BML and BML Realizers 5 Back- ground SmartBody Path Planning http://smartbody.ict.usc.edu Hamlet played by robots Unity using SmartBody MindMakers Wiki http://www.mindmakers.org/ projects/bml-1-0/wiki/Wiki
  6. 6. of 49 <act><participant id="GRAVEDIGGER2" role="actor" /><bml><gesture lexeme="POINT" target="GRAVEDIGGER1" /></bml></act> <act><participant id="GRAVEDIGGER1" role="actor" /><bml><speech id="sp1" ref="“ type="application/ssml+xml">Give me leave!</speech></bml></act> <act><participant id="GRAVEDIGGER1" role="actor" /><bml><gesture lexeme="POINT" target="GRAVEDIGGER2" /></bml></act> <act><participant id="GRAVEDIGGER1" role="actor" /><bml><gesture lexeme="POINT" target="GRAVE" /></bml></act> <act><participant id="GRAVEDIGGER1" role="actor" /><bml><speech id="sp1" ref="" type="application/ssml+xml"> Here lies the water -- good? </speech></bml></act> <act><participant id="GRAVEDIGGER1" role="actor" /><bml><gesture lexeme="POINT" target="GRAVE" /></bml></act> <act><participant id="GRAVEDIGGER1" role="actor" /><bml><speech id="sp1" ref="" type="application/ssml+xml"> Here stands the man -- good! </speech></bml></act> <act><participant id="GRAVEDIGGER1" role="actor" /><bml><speech id="sp1" ref="" type="application/ssml+xml"> If the man go to this water and drown himself, it is willynilly he goes, mark you that! But, </speech></bml></act> <act><participant id="GRAVEDIGGER1" role="actor" /><bml><gesture lexeme="POINT" target="GRAVE" /></bml></act> <act><participant id="GRAVEDIGGER1" role="actor" /><bml><speech id="sp1" ref="" type="application/ssml+xml">if the water come to HIM and drown him, he drowns not him-self; Argal, he that is not guilty of his own death shortens not his own life!</speech></bml></act> <act><participant id="GRAVEDIGGER1" role="actor" /><bml><locomotion target="GRAVE" type="basic" manner="walk" /></bml></act> <act><participant id="GRAVEDIGGER2" role="actor" /><bml><speech id="sp1" ref="" type="application/ssml+xml">But is this LAW ?</speech></bml></act> Still a Lot of Work… 6 Back- ground 4 hours & 12 minutes for a 10 minute scene!! Point Speak Move
  7. 7. of 49 So How Do We Do It? 7 Back- ground A: Excuse me… B: Can I help you? A: Yes, where is the post office? B: Go straight and turn left. A: Where do I turn left? B: Turn left at the bus stop - you can’t miss it. A: Thank you very much! B: No problem.
  8. 8. Play-Scripts 8
  9. 9. of 49 GRAVEDIGGER1 Give me leave! (GRAVEDIGGER2 sits on the side steps) (Pointing down into the grave) Here lies the water--good? (Pointing to the table ledge) Here stands the man-good! (Illustrating each point literally with his hands) If the man go to this water and drown himself, it is willy-nilly he goes, mark you that! But, (Pointing first to the grave, then to the ledge) if the water come to HIM and drown him, he drowns not him-self; (Greatly pleased with his own logic) Argal, he that is not guilty of his own death shortens not his own life! (He goes behind the barricade down into the grave and prepares to dig) GRAVEDIGGER2 (Trying to disprove him) But is this LAW? Play-Scripts 9 Play- Scripts Character Directions Stage Directions Stage Directions Character Directions Character Directions
  10. 10. of 49 The Baseline Play- Scripts Hamlet Act 5, Scene 3 (Graveyard Scene) Richard Burton in Hamlet, directed by Sir Gielgud http://www.youtube.com/watch?v=XRU5yLgs0zw&feature=pla yer_detailpage 10 400 BML Commands 4 hours & 12 minutes 10 minute scene
  11. 11. of 49 11 Play- Scripts Sentence Subject NP Actor/Noun VP VP Action/Verb NP Target/Noun Example Nouns: GRAVEDIGGER1 GRAVEDIGGER2 HAMLET HORATIO Steps Grave Audience Center stage Stage left Example Verbs: Move to Follow Look at Pick up Put down Speak Point to Example: (Pointing down into the grave) Actor = current speaker Verb = point Target = grave Annotation Parsing
  12. 12. of 49 What did it look like? 12 Play- Scripts
  13. 13. of 49 How Did We Do? 13 Play- Scripts HamletGraveDigger1 Ground Truth Simple NLP Method Character Traces Over Time for Entire Graveyard Scene C. Talbot and G. M. Youngblood. Spatial Cues in Hamlet. In Proceedings of the 12th International Conference on Intelligent Virtual Agents, IVA '12, pages 252-259, Berlin, Heidelberg, 2012. Springer-Verlag.
  14. 14. Spatial Rules 14
  15. 15. of 49 What’s Next?  Applying Spatial Rules  Conversational Spatial Rules  Grouping Spatial Rules  Theatre Rules  General Rules 15 Spatial Rules E. Sundstrom and I. Altman. Interpersonal Relationships and Personal Space: Research Review and Theoretical Model. 1976 Counter-Crossing
  16. 16. of 49 Architecture 16 Spatial Rules BML
  17. 17. of 49 Architecture 17 Spatial Rules
  18. 18. of 49 Rules Engine Logic 18 Spatial Rules
  19. 19. of 49 Position Results 19 Spatial Rules C. Talbot and G. M. Youngblood. Shakespearean Spatial Rules. In Proceedings of the 2013 International Conference on Autonomous Agents and Multi-agent Systems, AAMAS '13, pages 587-594, Richland, SC, 2013.
  20. 20. of 49 Position Results 20 Spatial Rules C. Talbot and G. M. Youngblood. Shakespearean Spatial Rules. In Proceedings of the 2013 International Conference on Autonomous Agents and Multi-agent Systems, AAMAS '13, pages 587-594, Richland, SC, 2013.
  21. 21. Implied Movements 21
  22. 22. of 49 Grave Digger 1 Initiative 22 Implied Mvmt
  23. 23. of 49 Implied Motion 23 Implied Mvmt To be, or not to be— that is the question: Whether 'tis nobler in the mind to suffer …. I should move towards the audience for my monologue
  24. 24. of 49 24 Implied Mvmt Information Captured  For Each Line of Speech:  Movement by Speaker or Other Character  Number of Lines Spoken Before  Number of Lines Spoken After  Annotation Before  Annotation After  Number of Lines since Last Movement  Number of Repeated Words  Number of Upper Case Words  Punctuation Counts  Parts of Speech Counts  Type of Movements:  Fighting  Jumping  Gestures  Object Manipulations  Locomotion  Pointing  Posture  Gaze
  25. 25. of 49 25 Implied Mvmt Machine Learning  RTextTools in R  Maximum Entropy  Random Forests  Boosting  SVM  Specific Movements  General Movements  Any Movement By Speaker  Any Movement at All
  26. 26. of 49 26 Implied Mvmt Learning Combinations  Movement Classifications  Specific Movements  Movement High-Level Categories  Big Movements  Any Movements  N-Gram Sizes  Unigrams  Bigrams  Trigrams  4-grams  5-grams  Training Sizes  Even Split Training / Testing  Even Split of Positive Examples for Training / Testing  Feature Sets  Text Only  POS Counts Only  POS Counts & Text  POS Counts & Contextual Features  All Features
  27. 27. of 49 27 Implied Mvmt Evaluation Criteria  Overall Accuracy  Recall  Precision  F1 score  F0.5 score  Matthews Correlation Coefficient  ROC curves
  28. 28. of 49 28 Implied Mvmt Evaluation Criteria  Overall Accuracy  Recall  Precision  F1 score  F0.5 score  Matthews Correlation Coefficient  ROC curves
  29. 29. of 49 29 Implied Mvmt Evaluation Criteria  Overall Accuracy  Recall  Precision  F1 score  F0.5 score  Matthews Correlation Coefficient  ROC curves
  30. 30. of 49 Best Performing 30 Implied Mvmt Boosting SVM MaxEnt RandForest Any Mvmt, POS, Unigrams Any Mvmt, No Text, Unigrams Gestures, All, 4-grams Any Mvmt, Text, Unigrams C. Talbot and G. M. Youngblood. Lack of Spatial Indicators in Hamlet. In Florida Artificial Intelligence Research Society Conference, FLAIRS '13, pages 154-159. Association for the Advancement of Artificial Intelligence, 2013.
  31. 31. of 49 Best Performing 31 Implied Mvmt Boosting SVM MaxEnt RandForest Random Any Mvmt, POS, Unigrams Any Mvmt, No Text, Unigrams Gestures, All, 4-grams Any Mvmt, Text, Unigrams C. Talbot and G. M. Youngblood. Lack of Spatial Indicators in Hamlet. In Florida Artificial Intelligence Research Society Conference, FLAIRS '13, pages 154-159. Association for the Advancement of Artificial Intelligence, 2013.
  32. 32. Incorporating Human Characters 32
  33. 33. of 49 So Far… 33 Humans Sentence Subject NP Actor/Noun VP VP Action/Verb NP Target/Noun Speech Movement Grouping Spatial Rules Conversational Spatial Rules Theatre Rules General Rules MindMakers Wiki http://www.mindmakers.org/ projects/bml-1-0/wiki/Wiki SmartBody Path Planning http://smartbody.ict.usc.edu
  34. 34. of 49 Adding a Human  Move correctly, on-time  Move correctly, wrong time  Move incorrectly, on-time  Move incorrectly, wrong time  Don’t move at all 34 Humans
  35. 35. of 49 35 Humans  Equilibrium of Forces  Aesthetically Balanced  Easy to See Nodes  Crossings-Free (some)  Fixed Nodes  Varying Relationships Based on Data  Can be Arranged in Pre-defined Shapes (some) Force-Directed Graphs (FDGs) T. M. J. Fruchterman, Edward, and E. M. Reingold. Graph Drawing by Force- Directed Placement. Software: Practice and Experience, 21(11):1129{1164, 1991.
  36. 36. of 49 Force-Directed Graph Structure  Node Representations:  Characters  Human  Target/Marks/Pawns  Audience  Central Grouping Point 36 Humans A H T A H T
  37. 37. of 49 Force-Directed Graph Structure  Node Representations:  Characters  Human  Target/Marks/Pawns  Audience  Central Grouping Point  Linkages  Characters – Humans/Characters  Characters – Targets/Marks/Pawns  Characters – Audience  Characters – Central Grouping Point  Humans – Central Grouping Point  Central Grouping Point - Audience  Humans - Audience 37 Humans A H T
  38. 38. of 49 Force-Directed Graph Functions  Adding Characters  Characters Leaving  Moving Characters  Human Moves 38 Humans B H T T A
  39. 39. of 49 A Force-Directed Graph Functions  Adding Characters  Characters Leaving  Moving Characters  Human Moves 39 Humans B H T T
  40. 40. of 49 Force-Directed Graph Functions  Adding Characters  Characters Leaving  Moving Characters  Human Moves 40 Humans B H T T A
  41. 41. of 49 Force-Directed Graph Functions  Adding Characters  Characters Leaving  Moving Characters  Human Moves 41 Humans B H T
  42. 42. of 49 42 Humans Forces and Time δ= distance between nodes L = length of stage depth α = constant C. Talbot and G. M. Youngblood. Positioning Characters Using Forces. In Proceedings of the Cognitive Agents for Virtual Environments Workshop (CAVE 2013) collocated with AAMAS (W08). IFAMAAS (International Foundation for Autonomous Agents and Multi-agent Systems), 2013.
  43. 43. of 49 Evaluation Approaches  Optimal arrangement based on current relationships  Time-based / sequential arrangement through entire scene  User evaluation of appropriate positioning 43 Humans
  44. 44. of 49 Humans Arrangement Based Upon Relationships 44
  45. 45. of 49 Humans Arrangement Based Upon Relationships 45
  46. 46. of 49 Humans Arrangement Based Upon Relationships 46
  47. 47. of 49 47 Humans Evaluation Criteria Appropriate arrangement based on current relationships  Even Vertex Distribution  Measure character distances  Small Number of Vertices  Count number of vertices  Fixed Vertices  Measure distance from targets/marks  Centering and Encircling of Groups  Comparison to semi-circular shape
  48. 48. of 49 48 Humans Results 100s of Random Relationship Scenarios  Even Vertex Distribution  3.14 feet (SD=1.54) between characters  Small Number of Vertices  At most 40 vertices in graph, with 12 characters  Fixed Vertices  3.30 feet (SD=1.52) from target  Centering and Encircling of Groups  Characters formed nice semi-circles C. Talbot and G. M. Youngblood. Application of Force-Directed Graphs on Character Positioning. In Proceedings of the Spatial Computing Workshop (SCW 2013) collocated with AAMAS (W09), pages 53-58. IFAMAAS (International Foundation for Autonomous Agents and Multi-agent Systems), 2013.
  49. 49. of 49 Incorporating Forces for Time- Based Arrangements 49 Humans
  50. 50. of 49 Evaluation Criteria  Occlusion  Clustering 50 Humans
  51. 51. of 49 51 Humans ResultsCase# Case Description Avg Occlusion Average Clustering X Average Clustering Y 0Baseline All AI 3.60% 19.50% 14.60% 1Baseline Human 90% 3.60% 19.10% 15.40% 2Baseline Human 50% 2.90% 20.00% 14.70% 3Baseline Human 10% 4.40% 30.90% 28.70% 4Forces All AI 2.40% 16.80% 14.60% 5Forces Human 90% 2.40% 16.80% 14.60% 6Forces Human 50% 1.60% 20.40% 13.80% 7Forces Human 10% 2.40% 20.80% 14.00% C. Talbot and G. M. Youngblood. Scene Blocking Utilizing Forces. In Florida Artificial Intelligence Research Society Conference, FLAIRS '14, pages 91-96. Association for the Advancement of Artificial Intelligence, 2014.
  52. 52. of 49 52 Humans ResultsCase# Case Description Avg Occlusion Average Clustering X Average Clustering Y 0Baseline All AI 3.60% 19.50% 14.60% 1Baseline Human 90% 3.60% 19.10% 15.40% 2Baseline Human 50% 2.90% 20.00% 14.70% 3Baseline Human 10% 4.40% 30.90% 28.70% 4Forces All AI 2.40% 16.80% 14.60% 5Forces Human 90% 2.40% 16.80% 14.60% 6Forces Human 50% 1.60% 20.40% 13.80% 7Forces Human 10% 2.40% 20.80% 14.00% C. Talbot and G. M. Youngblood. Scene Blocking Utilizing Forces. In Florida Artificial Intelligence Research Society Conference, FLAIRS '14, pages 91-96. Association for the Advancement of Artificial Intelligence, 2014.
  53. 53. User Studies 53
  54. 54. of 49 Block World 3D Representation 54 User Studies
  55. 55. of 49 Survey Questions 1. Characters showed evidence of engaged listening 2. Characters appeared to perform suitable movements on cue 3. The pace of the performance was too fast 4. The pace of the performance was too slow 5. The use of the space on stage was appropriate 6. The blocking (positioning and timing of the characters) was appropriate 7. There was adequate variety in the staging positions of the characters 8. The characters’ movement onstage during the performance was believable in the context of the performance 9. The performance is free from distracting behavior that does not contribute to the scene 10. The arrangement of the performers appropriately conveys the mood of the scene 11. The character movements provide appropriate dramatic emphasis 12. There is adequate variety and balance in the use of the performance space 13. All visible behaviors appear to be motivated and coordinated within the scene 14. The characters were grouped to give proper emphasis to the right characters at the right time 15. The characters frequently covered or blocked each other from your point of view 16. The movements of the characters were consistent with the play 17. There was a great deal of random movement 18. The characters’ reactions to other characters were believable 19. Characters showed a lack of engagement when listening 20. The arrangement of the performers contradicts the mood of the scene 21. The more prominent characters in the scene were hidden or masked from your view 22. The characters were too close together 23. The characters were too far apart 24. The stage space was not utilized to its full potential 25. All characters were visible from your point of view throughout the scene 55 User Studies
  56. 56. of 49 Results 56 User Studies StronglyDisagreeNeutralStronglyAgree Mean
  57. 57. of 49 Results 57 User Studies StronglyDisagreeNeutralStronglyAgree Mean
  58. 58. Planned Future Work 58
  59. 59. of 49 Planned Future Work  Additional User Studies (shortened)  Random  Baseline  NLP  NLP + Rules  NLP + Rules + FDGs  Human Interaction User Studies  Baseline  NLP + Rules + FDGs  Generalization  Identify play-types based on organization  Apply & evaluate techniques for up to 10 of these 59 Planned
  60. 60. Summary 60
  61. 61. of 49  Proposed Play-Scripts  Applied NLP  Added Rules Engine  Evaluated Speech for Implied Movement  Incorporated Human-Controlled Characters  Added FDGs and Algorithms  Created Spatial Performance Evaluation  Initial User Study Summary 61 Summary
  62. 62. of 49 Christine Talbot ctalbot1@uncc.edu Questions?  C. Talbot. Creating an Artificially Intelligent Director (AID) for Theatre and Virtual Environments. In Proceedings of the 2013 International Conference on Autonomous Agents and Multi-agent Systems, AAMAS '13, pages 1457-1458, Richland, SC, 2013. International Foundation for Autonomous Agents and Multi-agent Systems.  C. Talbot and G. M. Youngblood. Spatial Cues in Hamlet. In Proceedings of the 12th International Conference on Intelligent Virtual Agents, IVA '12, pages 252-259, Berlin, Heidelberg, 2012. Springer-Verlag.  C. Talbot and G. M. Youngblood. Application of Force-Directed Graphs on Character Positioning. In Proceedings of the Spatial Computing Workshop (SCW 2013) collocated with AAMAS (W09), pages 53-58. IFAMAAS (International Foundation for Autonomous Agents and Multi-agent Systems), 2013.  C. Talbot and G. M. Youngblood. Lack of Spatial Indicators in Hamlet. In Florida Artificial Intelligence Research Society Conference, FLAIRS '13, pages 154-159. Association for the Advancement of Artificial Intelligence, 2013.  C. Talbot and G. M. Youngblood. Positioning Characters Using Forces. In Proceedings of the Cognitive Agents for Virtual Environments Workshop (CAVE 2013) collocated with AAMAS (W08). IFAMAAS (International Foundation for Autonomous Agents and Multi-agent Systems), 2013.  C. Talbot and G. M. Youngblood. Shakespearean Spatial Rules. In Proceedings of the 2013 International Conference on Autonomous Agents and Multi-agent Systems, AAMAS '13, pages 587-594, Richland, SC, 2013. International Foundation for Autonomous Agents and Multi-agent Systems.  C. Talbot and G. M. Youngblood. Scene Blocking Utilizing Forces. In Florida Artificial Intelligence Research Society Conference, FLAIRS '14, pages 91-96. Association for the Advancement of Artificial Intelligence, 2014. 62 Questions Selected Bibliography Highlighting This Work

×