Storyline and Drama

STORYLINE AND DRAMAMike Chu, Joey Blekicki, Stephen Kish

STORYLINE AND DRAMA History of Narrative Development Dialogue Management Gameplay and Story New Research in Storyline Development

HISTORY OF NARRATIVE DEVELOPMENT

PROPP AND THE FORMALIST Morphology of the folktale

Discovered stable structures in Russian folktales Transgression, deception, struggle, punishment,

wedding, etc. A sequence of 31 narrative functions

PROPP’S APPROACH Narrative functions are invariant elements

and are independent from the characters that execute them

There are a limited number of narrative functions

Functions always occur in the same order No “backtracking” allowed

LIMITS TO PROBB’S MODEL No “branching” to allow for alternate stories It is not interactive

GREIMAS Introduced the role-based analysis of

narratives Define characters “not for who they are, but for

what they do” Defined roles: subject vs. object Also identifies semantic fields: love, greed, etc

BARTHES AND THE INTERPRETATIVE CODES Defined scenes in terms of action

ramifications Actions have the dimension of semantic field Not restrained to a specific occurrence

BATHES CONT. Some actions do not require a specific

sequence The result of a murder and the reason for the

murder Theory based on five codes:

ACT (ACTion) REF (REFerence) SYM (SYMbolic) SEM (SEMantic) HER (HERmaneutic)

BREMOND His theory centered around the concept of

character roles Opposition between Agent and Patient

BREMOND CONT. Characters can alternate between the agent

and patient role A patient can be prompted into taking action

A narrative process affects a patient by: Influencing their awareness of a situation Altering the situation (improving or worsening)

Agents Voluntary-purposefully initiates a goal-oriented

process Involuntary-narrative impact derives from

unintended side-effects of actions

BREMOND CONT. …AGAIN Also introduces character psychology

Characters have beliefs, motivations, and goals

The Matrix

MORE Patient influencing process

Their actions can influence the outcome of the agent

Portal

DIALOGUE MANAGEMENT

DIALOGUE MANAGERS History and beginnings

Colossal Cave Adventure Progressions

Neverwinter Nights Dialogue Manager

Center of interactive language systems Speech or text based

Responsible for what is said and is talked about Based on historical information, goals, possible actions

DIALOGUE MANAGER (DM) Finite state machines, frames, stacks,

inference engines, planners Several definitions historically

Process decide what is said at time steps Similar to goal oriented control structures

DM favored system initiative vs. user initiative vs. mixed initiative System: system drives dialogue

Final Fantasy Tactics User: user drives dialogue, system responds Mixed: user chimes in, system responds and

redirects

FINITE STATE MACHINES Most popular Dialogue Management

Paradigm and implementation technique of commercially spoken dialogue systems

Neverwinter Nights

FINITE STATE MACHINES Distinctive advantage with spoken language

Use specially tuned acoustic language model for each dialogue state

Know what “utterances” to expect Makes auto speech recognition task easier

Suited for special situations System has dialogue initiative, dialogue states,

dependencies between are defined and not many

(FSM) EXAMPLE CONT.

Choose

Food

Choose

Coffee

SA

Br

It

Br Sweet

Br Not Sweet

Br Sweet

Latte

Br Latte

Br Sweet

Br PureBr Pure

Br Latt

e

Br Sweet

Br Sweet

Latte

Cake

Tea

South African

Italian

Brazilian

No Sugar

Sugar

Milk

No Milk

Milk

No Milk

FINITE STATE MACHINES Provides straightforward way from task

breakdown to DM implementation Easy to check uncovered conditions, shortest

paths, cycles etc… Difficult lies in growth

Problematic with interrupts to system directed dialogue

Standard: ignore -> steer user back to original

(FSM) SUMMARY Good for simple, informative characters Implementing task oriented subdialogues Familiar to game developers Good starting point when implementing

characters with dialogue capabilities Limitations

Need all data to adhere to ordering constraints Any new information not expected is discarded

Examples: Text based

FRAMES Second most popular dialogue modeling

technique Foundational paradigm of VoiceXML (Expand) Widely used in commercial dialogue systems Used to fill a form and populate and/or query

a database Typical applications

Transport timetable info, call routing (expand)

FRAME BASED DIALOGUE SYSTEM (FBDS) Gets its name from way info is gathered from

user Frame is viewed as object in object oriented

paradigm with no defined methods Frame keeps track of wanted info Algorithm determines what to do to fill

missing items Prompts initial question, fill as many slots as

possible with user’s current “utterances”, ask questions to clarify and fill remaining slots

FBDS Necessary to keep track of info and create

clarification questions Need to keep confirmation flag Need confidence values associated with slots

Similar to learning tables Stronger in case of spoken dialogue systems

Need to address ambiguity and interpretation problems

FBDS Allow more efficient and natural interaction

System able to use info not explicitly asked for but relevant to the frame

Ease burden to software engineers Allowed to specify dialogue rules for each frame Management algorithm generates appropriate dialogue moves dynamically

Limitations To use automatic speech recognition component must be robust Needs to be able to deal with “utterances” used to describe

everything in any given frame Goes for natural language understanding module and embedded

clarifications Unable to deal with info that falls out of current frame but is

still relevant and supported Not used in video games more so commercial products

Human and computer interactions

STACKS Provide natural way to change topic of

conversation and resume halted one Any new conversation is pushed over the old and

then the old is popped once the new is done Can be integrated or independent COMIC system

Uses stack and augmented FSM as basis of DM AFSMs called Dialogue Action Forms (DAFs)

Has ability to execute arbitrary action in state transitions

Wait for arbitrary and external info Indexing terms like keywords

STACKS DM changes topics through DAF creation,

indexing and selections DAFs created with properties such as verbs,

nouns, entities and restrictions on world properties

Combo of keys are put in index When system receives “utterance” makes

key from bits of info from sentence Then selects DAF most closely matched and

is put on top of current one When finished it is popped and pervious one

resumes

STACKS Limitations

Auto speech recognition needs to have one general layer capable of identifying all “utterances” that lead to a topic or task shift

Natural language understanding module needs to be able to spot keywords, dependencies, entities that signal topic shifts

Tuning the indexing and retrieval mechanisms is challenging task in itself

Needs more sophisticated language generation module capable of summarizing what was said before and introducing appropriate cues and intros to resume previous converstations

STACKS Implementation

Most games use this type of dialogue manager Tutorials Sequences for more information

Ex) Dragon Age, Brave Fencer Musashi

INFERENCE BASED SYSTEMS Has four components

Knowledge base, inference engine, working memory, facts selector

Knowledge base is composed of declarative rules in logical formulism Propositional logic, first order logic

Inference engine responsible for finding valid proof of a fact Supports unification, forward/backward chaining

Working memory is where facts of current interests are kept

Facts selector is an algorithm that chooses and combines facts of interest before put in the inference system

NICE game system – hybrid system

IBDS Limitations

Difficult to use tuned auto speech recognition model for different dialogue parts with an IBDS

Language understanding module needs to provide enough information to populate working memory with relevant facts

Having good knowledge base information to guide interpretation of “utterance”

Mass Effect Blue: Charm, Red: Intimidate

PLAN BASED SYSTEMS Integral part of research and cutting edge

commercial dialogue management systems Basic structure

Set of operators and procedures to find sequence of operations that achieve one or more goals

Operations usually specified in terms of preconditions and effects

Two basic common uses Encoding speech acts/DM output directly to

operators’ actions To select facts of interest to be fed into the

system

PLANNED BASED SYSTEMS Ordering exists between actions to

organize; need to know how many people and where will they sit

Offers same complications for auto speech recognition and natural language understanding as an interface based system

TRIPS Final Fantasy 9: Cooking (1:39)

GAMEPLAY AND STORY Brief history of story in games Modeling faction Interactions Applications to RPGs Faction modeling in action

STORY IN GAMES More of a recent development Earlier games relied heavily on gameplay

(Megaman, Pong, Super Mario Brothers) Still plenty of games with limited base story:

Super Mario Galaxy, Katamari, Call of Duty, Sports Games, Fighting Games.

MODERN GAMES Games are now expected to have at least

some type of story. RPG storylines have been growing in scale,

and just keep getting larger.

MODEL PARAMETERS Assuming two factions (X,Y), each

faction gets a parameter (x,y) where x or y is that faction’s level of cooperation towards the other.

Higher parameter value means more cooperative.

Factions may also have other parameters for belligerence or pacifism.

All the parameters are evaluated to decide the factions’ behavior towards each other.

EQUATIONS TO USE PARAMETERS (BASED ON MODELING AN ARMS RACE) Equations describing intended behavior:

x = ky – ax + g y = lx – by + h k and l are fear constants (mutual) a and b are restraint constants g and h are grievance terms

If ab > kl, there is equilibrium. If ab < kl there is unstable equilibrium.

REINTERPRETED FOR RPGS Same basic equations

X = Ky – Ax + GY = Lx – By + H

K and L are belligerence factors A and B are pacifism factors G and H are friendliness towards the other

faction If the result is above equilibrium, they are

in cooperation, if they are below, they are in competition, and on or around eqilibrium is neutrality.

BEHAVIOR IN ACTION Parameters may be applied toward random

encounters with other factions. More hostility = more difficult battles. Allied factions assist in battles If factions are cooperative, maybe a negotiation

encounter will occur. Also effect conversations as shown in dialog

trees.

EXAMPLES IN MODERN GAMES Fable Fallout (Good/Evil) Neverwinter Nights Series Oblivion

Almost every NPC belongs to some faction: Fighter’s guild, Mage’s Guild, Thief’s Guild, Dark Brotherhood, Arena.

Also minor guilds: Blackwood Company, Knights of the Nine, The Blades, Order of the Dragon, etc.

The faction’s standing towards the player effects dialogue and aggressiveness.

ENDINGS OF GAMES Some games use character or faction

relations to change the endings(Star Ocean Series, Heavy Rain).

Evaluate relationship values and present different scenarios based on those values, allowing different endings for multiple playthroughs.

STORY IN GAMEPLAY Normally, player expects story to effect

gameplay, newer games make it work so that gameplay also effects the story.

Example: http://www.youtube.com/watch?v=SQCBLsJhcDo#t=02m35s

RESEARCH IN STORYLINE DEVELOPMENT

INTERACTIVE STORYTELLING RESEARCH Experimenting how interfering with actions

can affect outcomes of a storyline

STORYLINE BASED ON AWARENESS AND FEELING

NARRATIVE GENERATION THROUGH POV