1. 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.

2. Samer Hassan CAEPIA 2007 2 Contents Objective The initial MAS New context & BDI Storytelling: Content planning Example Future work

3. Samer Hassan CAEPIA 2007 3 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

4. Samer Hassan CAEPIA 2007 4 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

5. Samer Hassan CAEPIA 2007 5 Contents Objective The initial MAS New context & BDI Storytelling: Content planning Example Future work

6. Samer Hassan CAEPIA 2007 6 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

7. Samer Hassan CAEPIA 2007 7 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

8. Samer Hassan CAEPIA 2007 8 Contents Objective The initial MAS New context & BDI Storytelling Content planning Example Future work

9. Samer Hassan CAEPIA 2007 9 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

10. Samer Hassan CAEPIA 2007 10 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”

11. Samer Hassan CAEPIA 2007 11 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

12. Samer Hassan CAEPIA 2007 12 Contents Objective The initial MAS New context & BDI Storytelling: Content planning Example Future work

13. Samer Hassan CAEPIA 2007 13 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

14. Samer Hassan CAEPIA 2007 14 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

15. Samer Hassan CAEPIA 2007 15 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

16. Samer Hassan CAEPIA 2007 16 Contents Objective The initial MAS New context & BDI Storytelling: Content planning Example Future work

17. Samer Hassan CAEPIA 2007 17 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!

18. Samer Hassan CAEPIA 2007 18 Contents Objective The initial MAS New context & BDI Storytelling: Content planning Example Future work

19. Samer Hassan CAEPIA 2007 19 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

20. Samer Hassan CAEPIA 2007 20 Thanks for your attention! Carlos León, Samer Hassan, Pablo Gervás, Juan Pavón samer@fdi.ucm.es Dep. Ingenieria del Software e Inteligencia Artificial Universidad Complutense de Madrid

21. Samer Hassan CAEPIA 2007 21 Contents License This presentation is licensed under a Creative Commons Attribution 3.0 http://creativecommons.org/licenses/by/3.0/ You are free to copy, modify and distribute it as long as the original work and author are cited