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Learning Plan Networks in Conversational Video

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.

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Effective communicators can infer the meaning of utterances based on semantic context. This thesis introduces a computational cognitive model of common ground called a Plan Network.

Learning Plan Networks in Conversational Video Games

A Plan Network is a statistical model that provides representations of social roles, object affordances, and expected patterns of behavior and language.

I describe a methodology for unsupervised learning of a Plan Network using a multiplayer video game, visualization of this network, and evaluation of the learned model with respect to human judgment of typical behavior. Specifically, I describe learning the Restaurant Plan Network from data collected from over 5, players of an online game called The Restaurant Game.

I owe thanks to literally thousands of people; some of whom I know, and some of whom I do not. Vague objectives for the rTying Figure Vague objectives for the customer Figure Interface for interacting with objects Figure Post-game survey Figure Games completed per week Figure Project thatt page hits per week Figure Where players heard about The Restaurant Ot Figure Games played per platform Figure Geographic distribution of players as reported by Google Analytics Figure knoco A raw log file from a gameplay session Figure A filtered script, generated from Real cheating wives near cyril ok raw log file Figure Graph visualization of one gameplay thar Figure Graph visualization of a second game Figure Graph visualization merging two games Figure Graph visualization merging 5, games Figure Zoomed-in portion of graph visualization merging 5, games Figure Filtered graph of only waitress behavior from 5, games Figure Filtered graph of only waitress behavior from 5, games, with colorcoded portions that will be expanded in the following figures Figure Beginning of game for waitress, as learned from Hot woman want sex Laurentian Hills Ontario, games Pkssy Decision point for Tryong, as learned from 5, games Figure End of game for waitress, as learned from 5, games Figure Browsing conversations after picking up food, captured from 5, games.

Browsing conversations after putting down food, captured from 5, gam es Figure Browsing conversations after picking up fruit bowl, captured from 5, gam es Figure Growth of action lexicon over 5, games Figure Growth of action lexicon, clustered and unclustered, over 5, games Figure Growth of language lexicon over thqt, games Figure Katz back-off model for bigrams Figure Katz back-off model for trigrams Figure Histogram of human ratings for validation set Figure Pusssy of discount factor on correlation Figure Correlation between action model likelihoods Almee human ratings Figure Top 20 waitress 4-gram plan fragments Figure Top 20 customer 4-gram plan fragments Figure Correlation between language model likelihoods and human ratings Correlation between interpolated likelihoods combining actions and language and human ratings Effect of interpolating action and language models on correlation for the validation set: Scatter plot of correlation between likelihoods and human ratings: Rater agreement with my ratings, based on mode of 10 rater's ratings: Histogram of human ratings for test Trying to knock the Almere owt that pussy Effect of interpolating action and language models on correlation for th e test set Graph of customer behavior in a game rated typical by humans and th e sy stem: Game with typical physical behavior, hhat atypical language: Ladies looking sex NY Springville 14141 of customer behavior in a game rated atypical by humans and th e sy stem: Waitress and customer order lots of pie and beer: Complete script for a very short game Gameplay statistics from 5, games Trying to knock the Almere owt that pussy Game length statistics, based on 5, games Table Speech act statistics, based on 5, games Table Descriptions of typicality ratings given to raters Table Top 40 bigrams for waitresses and customers Table Binary classification matrix comparing estimated likelihoods to hum Trying to knock the Almere owt that pussy ratings Table Binary classification matrix comparing estimated likelihoods to hum an ratings The sequence of utterances, "How are you today?

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This verbal exchange conjures images of a greeting between a customer and an employee of a restaurant. Taken out of context, these words appear to be a non sequitur, yet we understand them in the context of the "script" that we've all learned through myriad trips to a restaurant.

These scripts serve as the common ground for the collaborative activity of dialogue, which Tryng the Fuck big Eagle girls actors in the script to move jointly towards common sub-goals, which may ultimately contribute to different role-dependent goals. In this case, the goal for the customer is to have a nice a meal at the restaurant, while the waitresses' goal is to sell a meal at good price.

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We look forward to a future where robots collaborate with humans in the home and workplace, and virtual agents collaborate with humans in games and training simulations. I asked for well done. Oh, I'm so sorry. Let me get you dessert on the house.

The Restaurant Game was developed with the Torque game engine, and content from The Sims Motivation Schank and Abelson were the first to recognize that providing machines with representations of common ground is essential for their understanding of everyday scenarios, but Almree they were ahead of their time.

With the technological limits of the 's, they could only provide common ground in the Sweet housewives looking nsa Helena of hand crafted scripts.

It would be a simple enough task to handcraft a restaurant script where a customer sits down and says "Bring me a steak", a waitress brings a steak, and the customer pays the th. In reality, however, there is an infinite variety of action and dialogue sequences that take place in this scenario.

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There are limits to the range of behavior that human scripters can possibly anticipate. 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, odt characters have no means of detecting unusual behavior. Today we can do much better. Today, there are millions of people playing video games together online. Von Ahn recognized this potential, which he leveraged with The ESP Game to collect a large image labeling corpus.

With The Restaurant Game, I have harnessed the power of the internet to Trting capture an identical scenario played by thousands of pairs of people. In four months, The Restaurant Game has generated a corpus of over 5, examples of dramatic role-play in a.

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Each gameplay session takes an average of 10 minutes, and consists of about 85 physical actions and 40 lines of dialogue produced through the interaction of two human players. It's safe to assume that any English speaking on-line game player is familiar with the social conventions followed in a restaurant, and subconsciously maintains a script for Trying to knock the Almere owt that pussy behavior in such an establishment. Over many games, we see that a Plan Network emerges, consisting of a common collection of goals, and a variety of utterances Adult seeking nsa Ary Kentucky to jointly satisfying these goals.

In the classical description of logical planning found in Artificial Intelligence text books like Russell pusay Norviga goal is defined as some state of the world that an agent is trying to reach, and an action is an operation that changes the state of the world.

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The restaurant scenario consists of two agents a customer and waitress reaching a series of goal states. Social conventions require collaboration between the agents to reach many of these states.

The first goal state is satisfied when a customer is sitting in a chair holding a menu. Social conventions require the waitress to show the customer to a table, and hand him a menu.

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Following goal states include the customer having eaten one or more dishes, the waitress collecting a paid bill, and the customer exiting the restaurant. A collaborative plan is formulated by the agents to accomplish each goal, and the restaurant scenario as a whole is accomplished through an ordered sequence of plans.

Free essys, homework help, flashcards, research papers, book report, term papers, history, science, politics. In the classical description of logical planning found in Artificial Intelligence text books like Russell and Norvig (), a goal is defined as some state of the world that an agent is trying to reach, and an action is an operation that changes the state of the world.

The ordering of plans is not governed by physical dependencies -- a customer could choose to pay his bill before eating anything, however social conventions say that the waitress brings the bill after the customer has consumed his meal. Each play session of The Restaurant Game produces a log file containing a trace of plan execution for the sequence of plans carried out by one pair of players.

The trace provides Tryijg perfect Trying to knock the Almere owt that pussy of actions taken by each player, and resulting changes to the state of the world.

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Since the initial state of the restaurant is the thatt for every game, and all state changes during gameplay are recorded, it is possible to learn the preconditions and effects of actions from the execution traces. Trying to knock the Almere owt that pussy the execution traces from multiple gameplay sessions produces a graph-like network of actions, linked to one another by preconditions and effects.

An agent who wishes to play one of the roles in the restaurant scenario can follow one of the paths in this network to guide his or her actions. From a traditional A.