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Hand crafted scripts are brittle in the face of unanticipated behavior, and are unlikely to cover appropriate responses for the wide range of behaviors exhibited when players are given minimal instructions to play roles in an open ended environment.

Furthermore, scripted characters have no means of detecting unusual behavior. Today, there are millions of people playing video games together online.

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84 List of Figures Figure 1-1: The Restaurant Game was developed with the Torque game engine, and content from The Sims Figure 3-1: Vague objectives for the waitress Figure 3-2: Vague objectives for the customer Figure 3-3: Interface for interacting with objects Figure 3-4: Post-game survey Figure 3-5: Games completed per week Figure 3-6: Project web page hits per week Figure 3-7: Where players heard about The Restaurant Game Figure 3-8: Games played per platform Figure 3-9: Geographic distribution of players as reported by Google Analytics Figure 4-1: A raw log file from a gameplay session Figure 4-2: A filtered script, generated from a raw log file Figure 4-3: Graph visualization of one gameplay session Figure 4-4: Graph visualization of a second game Figure 4-5: Graph visualization merging two games Figure 4-6: Graph visualization merging 5,000 games Figure 4-7: Zoomed-in portion of graph visualization merging 5,000 games Figure 4-8: Filtered graph of only waitress behavior from 5,000 games Figure 4-9: Filtered graph of only waitress behavior from 5,000 games, with colorcoded portions that will be expanded in the following figures Figure 4-10: Beginning of game for waitress, as learned from 5,000 games Figure 4-11: Decision point for waitress, as learned from 5,000 games Figure 4-12: End of game for waitress, as learned from 5,000 games Figure 4-13: Browsing conversations after picking up food, captured from 5,000 games.

61 Figure 4-14: Browsing conversations after putting down food, captured from 5,000 gam es Figure 4-15: Browsing conversations after picking up fruit bowl, captured from 5,000 gam es Figure 4-16: Growth of action lexicon over 5,000 games Figure 4-17: Growth of action lexicon, clustered and unclustered, over 5,000 games Figure 4-18: Growth of language lexicon over 5,000 games Figure 4-19: Katz back-off model for bigrams Figure 4-20: Katz back-off model for trigrams Figure 5-1: Histogram of human ratings for validation set Figure 5-2: Effect of discount factor on correlation Figure 5-3: Correlation between action model likelihoods and human ratings Figure 5-4: Top 20 waitress 4-gram plan fragments Figure 5-5: Top 20 customer 4-gram plan fragments Figure 5-6: Correlation between language model likelihoods and human ratings...

Deb Roy Associate Professor of Media Arts and Sciences MIT Media Lab Learning Plan Networks in Conversational Video Games by Jeffrey David Orkin Submitted to the Program in Media Arts and Sciences in partial fulfillment of the requirements for the Master of Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY August 2007 Thesis Reader.

Cynthia Breazeal Associate Professor of Media Arts and Sciences MIT Media Lab Learning Plan Networks in Conversational Video Games by Jeffrey David Orkin Submitted to the Program in Media Arts and Sciences in partial fulfillment of the requirements for the Master of Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY August 2007 Thesis Reader Henry Lieberman Research Scientist MIT Media Lab Learning Plan Networks in Conversational Video Games by Jeffrey David Orkin Submitted to the Program in Media Arts and Sciences in partial fulfillment of the requirements for the Master of Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY August 2007 Thesis R eader Will Wright Chief Game Designer Maxis, Electronic Arts Acknowledgements A large scale data collection effort like this cannot be accomplished alone.

A representation of common ground for everyday scenarios is essential for these agents if they are to be effective collaborators and communicators.

Effective collaborators can infer a partner's goals and predict future actions.

Specifically, I describe learning the Restaurant Plan Network from data collected from over 5,000 players of an online game called The Restaurant Game. In reality, however, there is an infinite variety of action and dialogue sequences that take place in this scenario.

There are limits to the range of behavior that human scripters can possibly anticipate.

Learning Plan Networks in Conversational Video Games by Jeffrey David Orkin B. S., University of Washington (2003) Submitted-to the Program in Media Arts and Sciences in partial fulfillment of the requirements for the degree of Master of Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY August 2007 Massachusetts Institute of Technology All rights reserved.

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