Mixed Narrative and Dialog Content Planning Based on BDI Agents Carlos León Aznar Samer Hassan Collado Pablo Gervás Juan Pavón Mestras CAEPIA 2007 Universidad

Mixed Narrative and Dialog Content Planning Based on BDI Agents

Carlos León Aznar

Samer Hassan Collado

Pablo Gervás

Juan Pavón Mestras

CAEPIA 2007

Universidad Complutense de Madrid

Acknowledgments. This work has been developed with support of the projects TIN2006-14433-C02-01 and TIN2005-08501-C03-01, funded by the Spanish Council for Science and Technology.

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Samer Hassan CAEPIA 2007 2

Contents

Objective The initial MAS New context & BDI Storytelling: Content planning Example Future work


Objective

Storytelling narrative systems try to automatically generate a creative story in natural language

Dialogs carry much information not present in simple narrative text

The system proposed: creates stories with focus on character interactions,

based on communication between the characters addresses content planning for dialogs together with

narrative text


Objective

For achieving this aim, the work is divided in two modules:

MAS of BDI agents that simulate social interaction, generating the contents for the story

An automatic story generation module, that receives the set of facts happened in the simulation, and creates a textual representation of the main events


Contents



The initial MAS

Agent Based Social Simulation system Each agent is an individual with attributes and relations The original system has a sociological context in

postmodern Spain



The initial MAS

Agent/Individual: Agent attributes:

ideology, religiosity, economic class, age, sex…

Different behaviour while life cycle: youth, adult, old

Demographic micro-evolution: couples, reproduction, inheritance

World: Demographic model

Network relationships:

•Friends groups•Relatives


Contents

Objective The initial MAS New context & BDI Storytelling Content planning Example Future work


Adapting the system for a new context

Modern social systems can be boring for storytelling Fantasy Medieval World is more interesting Personification of the characters: name, race, inheritable

last name Deron Cairnbreaker, the Elf

New semantic of the facts Death Betrayed, accident, poisoned… Relation Enemy, friend, love…

Introduction of life events Killing dragons Suffer several spells Finding treasures in dangerous dungeons


Deep changes in agent architecture

The idea is to make the agents evolve in time internally

Agents’ characteristics will now change depending on the events: treasure economy increasing

From simple cellular automata to BDI agent: Believes: represent the knowledge of the agent about his

world - “What I know and believe” Desires (objectives): represent the state that the agent is

trying to reach - “What I want” Intentions (plans): the means that the agent choose to

accomplish its objectives - “What I am going to do”


BDI model

With the BDI model, each agent is “more intelligent”, taking its own decisions, and building a real story

D Ask for info success? Dialog new B enough? generation I’s associated try to execute those events if D satisfied, delete D

There are several D’s in each agent, so it’s not linear

Agents’ planning is quite simple, but enough for the prototype to generate coherent and linked content


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Text generation

This process takes place in several stages: Content planning: concepts that will appear in the final

content are decided and organised into a specific order and structure

Sentence planning: each message resulting from the previous stage is progressively enriched with all the linguistic information required to realize it

Surface realization: assembles all the relevant pieces into linguistically and typographically correct text


Content Planning

The module is mainly centred around content planning

Imports XML log from ABSS

While importing, the facts are related by time or cause relations

Dialogs are handled as other facts, so both can be mixed

The discourse is created based on a state space search: backtracking algorithm that explores the solution space, by creating different stories, using relations between statements as operators



Content Planning

Many possible stories are generated

For selecting one, objectives are defined: Linearity of the text: level of sequentiality Theatricality: porcentage of dialog parts Causality: importante of cause-effect relations

The most similar story to the objective will be the chosen one


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Example

It was an elf. And His name was Deron. And His last name was Cairnbreaker. And Deron Cairnbreaker desired to become a great wizard. After that, the spell of memory was cast upon Deron Cairnbreaker. Because of that, its education decreased. After that, Deron Cairnbreaker and Parbagar Greatcutter talked:

- Do you know who has the one ring?

- Yes, I can tell you who has the one ring - said Deron Cairnbreaker, and he told him where.

- Are you sure? Then I’ll go and talk with him. - said Parbagar Greatcutter

- Farewell.

Before that, Deron Cairnbreaker and Georgia Houston talked:

- Do you know where can I find another wizard?

- Yes, I do. I will tell you. - said Deron Cairnbreaker. Then, Deron Cairnbreaker showed the place.

- Ok, now I have this useful information. - said Georgia Houston

- Thank you!


Contents



Future work

Next logical step: introducing agents negotiation

Improving BDI complexity: complex rules certainty, intensity, success

Connecting NLP module with proper sentence planning and surface realization modules


Thanks for your attention!

Carlos León, Samer Hassan, Pablo Gervás, Juan Pavón

[email protected]

Dep. Ingenieria del Software e Inteligencia Artificial

Universidad Complutense de Madrid



Contents License

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Mixed Narrative and Dialog Content Planning Based on BDI Agents Carlos León Aznar Samer Hassan Collado Pablo Gervás Juan Pavón Mestras CAEPIA 2007 Universidad