1. © American Association of Artificial Intelligence 2002 Sex, Lies and Video Games: An Interactive Storytelling Prototype Marc Cavazza, Fred Charles, Steve Mead University of Teesside Middlesbrough, UK

2. Other References Marc Cavazza, Fred Charles and Steven Mead “Characters in Search of An Author” (2001) Marc Cavazza, Fred Charles and Steven Mead “AI-Based Animation for Interactive Storytelling” (2001) Marc Cavazza, Fred Charles and Steven Mead “Agents’ Interaction in Virtual Storytelling” (2001) Michael Mateas “An Oz-Centric Review of Interactive Drama and Believable Agents” (1997) http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/oz/web/papers/CMU-CS-97-156.html Jonathan Gratch “Émile: Marshalling Passions in Training and Education” (2000) John Gratch, Jeff Rickel, Stacy Marsella, William Swartout and Randall Hill “Steve Goes to Bosnia: Towards a New Generation of Virtual Humans for Interactive Experiences” (2001)

3. © American Association of Artificial Intelligence 2002 Motivation Extend audience interaction Military training Educational Purposes

4. © American Association of Artificial Intelligence 2002 Architecture Multi-Agent System Unreal ™ game engine DLL interfaces with the game engine System fully implemented as template C++ classes

5. © American Association of Artificial Intelligence 2002 Types of Agents Agents: actors or “characters” Can be of two kinds Primary: Usually goal driven Secondary: Purely reactive

6. © American Association of Artificial Intelligence 2002 Environment Continuous Non-deterministic Episodic Inaccessible Dynamic

7. © American Association of Artificial Intelligence 2002 Goals Vary from time to time No ultimate drive Programmed into agent by “author” Absence of goals: Purely reactive agent

8. © American Association of Artificial Intelligence 2002 Sensory Input Auditory: Can hear “nearby” sounds Visual: Conical field

9. © American Association of Artificial Intelligence 2002 Actions (Plans) Can be primitive or complex Complex actions built upon primitives Agents use Planning Plans: Ordered sequence of Steps Steps: Preconditions, Actions and Effects Planning: Top-Down or Bottom-Up Action Selection: Based upon agent plan.

10. © American Association of Artificial Intelligence 2002 Action Selection Actor can only react to sensed changes in environment Unless actor has a goal Actors with goals: Use real-time planning All actors compete for “resources” Resources: Time, physical objects Unavailability of a resource necessitates re-planning capabilities

11. © American Association of Artificial Intelligence 2002 Action Selection from HTN plans Solution derived by searching through plan Top-down left-to-right search with backtracking Implemented using real-time variant of AO * Hendler, Tsunato et al. “Plan-Refinement Strategies and Search-Space Size”, Proceedings of the European Conference on Planning, 1997, pp. 414-426.

12. © American Association of Artificial Intelligence 2002 Primary characters usually have a definite goal Create a plan towards achieving it Plan represented as a Hierarchical Task Network (HTN). HTN is an AND/OR Graph Tasks from HTN are usually executed from top- down and left to right Backtracking if actions fail Agent Goals and Planning

13. © American Association of Artificial Intelligence 2002 Hierarchical Task Network (HTN)

14. © American Association of Artificial Intelligence 2002 Dramatic Purpose Dynamic Interaction of characters’ plans (or no plans) leads to humorous situations Illustrated by enactment of sitcom ”Friends™” User can follow story from any perspective (of characters or her own) User can also navigate the virtual set unseen by characters

15. © American Association of Artificial Intelligence 2002 Actors and “Characters” in “Friends™” Jennifer Anniston (“Rachel”) Courtney Cox (“Monica”) Lisa Kudrow (“Phoebe”) Matthew Perry (“Chandler”) Matt LeBlanc (“Joey”) David Schwimmer (“Ross”) Prototype restricts itself to: Ross and Rachel (primary actors) Phoebe (secondary actor)

16. © American Association of Artificial Intelligence 2002 Episode Details Ross’ Goal: To ask Rachel out to dinner Rachel’s Goal: None

17. © American Association of Artificial Intelligence 2002 Ross’ Plan To ask Rachel out: Ross must find out Rachel’s preferences Consult her PDA Ask Phoebe Gain Rachel’s affection Buy her gifts Isolate Rachel from the others…..

18. © American Association of Artificial Intelligence 2002 Ross’ Preferences among Actions Influenced by personality profile Maybe influenced by “moods” or emotions Personality profile can be built-in Can be changed Moods (emotions): Not implemented in prototype but subject of future

19. © American Association of Artificial Intelligence 2002 Hierarchical Task Network (HTN)

20. © American Association of Artificial Intelligence 2002 User Intervention Act upon physical objects on screen that bear narrative influence Influence actors’ actions by directly “speaking to them” Consequence for actors: Re-planning Re-planning uses bottom-up search of HTN

21. © American Association of Artificial Intelligence 2002 Re-planning scenarios for actors Emergent situations that cannot be ignored Actors use “situated reasoning” Situated reasoning tries to avoid undesirable future outcomes with respect to actor’s goals Actions of actors in emergent situations impacts future scenario Unavailability of resources User intervention

22. © American Association of Artificial Intelligence 2002 Friends™: An interactive episode Ross enters Rachel’s bedroom

23. © American Association of Artificial Intelligence 2002 Friends™: An interactive episode Unseen by Phoebe who’s preparing coffee

24. © American Association of Artificial Intelligence 2002 Friends™: An interactive episode User intervenes and removes “narrative object” (PDA) from Rachel’s Room

25. © American Association of Artificial Intelligence 2002 Friends™: An interactive episode Ross gets to Rachel’s room and discovers PDA missing

26. © American Association of Artificial Intelligence 2002 Friends™: An interactive episode Ross makes a new decision to ask Phoebe about Rachel’s preferences (Re-planning)

27. © American Association of Artificial Intelligence 2002 Friends™: An interactive episode Ross interrupts Phoebe to ask her about Rachel

28. © American Association of Artificial Intelligence 2002 Friends™: An interactive episode Ross interrupts Phoebe to ask her about Rachel

29. © American Association of Artificial Intelligence 2002 Friends™: An interactive episode Ross asks Rachel out

30. © American Association of Artificial Intelligence 2002 Emotions in Agents Emotions: Related to Agent Plans (Gratch 2000) Outcome of relation of events to agent’s plans and goals (Ex: Fear, Frustration) Outcome of interaction between events and agents’ plans and goals (Ex: Anger, Jealousy)

31. © American Association of Artificial Intelligence 2002 Back to Emotional Friends™ Rachel sees Ross and Phoebe conversing animatedly Rachel “feels” jealous Actors can’t really emote (!!) Alternative: “Mood” T-shirts Emotions affect Action Selection Rachel in a jealous mood would refuse Ross outright

32. © American Association of Artificial Intelligence 2002 User advising Ross

33. © American Association of Artificial Intelligence 2002 Conclusions and Future Directions Interactive Storytelling is at an early developmental stage Better co-ordination of actors required Emotive aspects of actors need to be worked upon Character-based plot generation cannot really “surprise” the user Plot-based narration and emergent plot generation can lead to more entertaining packages