Download Sex, Lies and Video Games: An Interactive Storytelling

Sex, Lies and Video Games:
An Interactive Storytelling
Prototype
Marc Cavazza, Fred Charles, Steve Mead
University of Teesside
Middlesbrough, UK
© American Association of
Artificial Intelligence 2002
Other References
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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
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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)
Motivation
 Extend
audience interaction
 Military training
 Educational Purposes
© American Association of
Artificial Intelligence 2002
Architecture
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Multi-Agent System
Unreal™ game engine
DLL interfaces with the game engine
System fully implemented as template
C++ classes
© American Association of
Artificial Intelligence 2002
Types of Agents
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Agents: actors or “characters”
Can be of two kinds
Primary: Usually goal driven
Secondary: Purely reactive
© American Association of
Artificial Intelligence 2002
Environment
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Continuous
Non-deterministic
Episodic
Inaccessible
Dynamic
© 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
© American Association of
Artificial Intelligence 2002
Sensory Input
 Auditory:
Can hear “nearby”
sounds
 Visual: Conical field
© American Association of
Artificial Intelligence 2002
Actions (Plans)
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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.
© American Association of
Artificial Intelligence 2002
Action Selection
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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
© American Association of
Artificial Intelligence 2002
Action Selection from HTN plans
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Solution derived by searching through plan
Top-down left-to-right search with backtracking
Implemented using real-time variant of AO*
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Hendler, Tsunato et al. “Plan-Refinement Strategies and
Search-Space Size”, Proceedings of the European
Conference on Planning, 1997, pp. 414-426.
© American Association of
Artificial Intelligence 2002
Agent Goals and Planning
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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 topdown and left to right
Backtracking if actions fail
© American Association of
Artificial Intelligence 2002
Hierarchical Task Network (HTN)
© American Association of
Artificial Intelligence 2002
Dramatic Purpose
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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
© American Association of
Artificial Intelligence 2002
Actors and “Characters” in
“Friends™”
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Jennifer Anniston (“Rachel”)
Courtney Cox (“Monica”)
Lisa Kudrow (“Phoebe”)
Matthew Perry (“Chandler”)
Matt LeBlanc (“Joey”)
David Schwimmer (“Ross”)
Prototype restricts itself to:
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Ross and Rachel (primary actors)
Phoebe (secondary actor)
© American Association of
Artificial Intelligence 2002
Episode Details
 Ross’
Goal: To ask Rachel out to
dinner
 Rachel’s Goal: None
© American Association of
Artificial Intelligence 2002
Ross’ Plan
To ask Rachel out:
 Ross must find out Rachel’s preferences
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Gain Rachel’s affection
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Consult her PDA
Ask Phoebe
Buy her gifts
Isolate Rachel from the others…..
© American Association of
Artificial Intelligence 2002
Ross’ Preferences among Actions
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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
© American Association of
Artificial Intelligence 2002
Hierarchical Task Network (HTN)
© American Association of
Artificial Intelligence 2002
User Intervention
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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
© American Association of
Artificial Intelligence 2002
Re-planning scenarios for actors
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Emergent situations that cannot be ignored
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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
© American Association of
Artificial Intelligence 2002
Friends™: An interactive episode
Ross enters Rachel’s bedroom
© American Association of
Artificial Intelligence 2002
Friends™: An interactive episode
Unseen by Phoebe who’s preparing coffee
© American Association of
Artificial Intelligence 2002
Friends™: An interactive episode
User intervenes and removes “narrative object” (PDA) from Rachel’s
Room
© American Association of
Artificial Intelligence 2002
Friends™: An interactive episode
Ross gets to Rachel’s room and discovers PDA missing
© American Association of
Artificial Intelligence 2002
Friends™: An interactive episode
Ross makes a new decision to ask Phoebe about Rachel’s preferences
(Re-planning)
© American Association of
Artificial Intelligence 2002
Friends™: An interactive episode
Ross interrupts Phoebe to ask her about Rachel
© American Association of
Artificial Intelligence 2002
Friends™: An interactive episode
Ross interrupts Phoebe to ask her about Rachel
© American Association of
Artificial Intelligence 2002
Friends™: An interactive episode
Ross asks Rachel out
© American Association of
Artificial Intelligence 2002
Emotions in Agents
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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)
© American Association of
Artificial Intelligence 2002
Back to Emotional Friends™
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Rachel sees Ross and Phoebe conversing
animatedly
Rachel “feels” jealous
Actors can’t really emote (!!)
Alternative: “Mood” T-shirts
Emotions affect Action Selection
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Rachel in a jealous mood would refuse Ross
outright
© American Association of
Artificial Intelligence 2002
User advising Ross
© American Association of
Artificial Intelligence 2002
Conclusions and Future
Directions
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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
© American Association of
Artificial Intelligence 2002