Ontanon, Santiago


A User Study on Learning from Human Demonstration

AAAI Conferences

A significant amount of work has advocated that Learning from Demonstration (LfD) is a promising approach to allow end-users to create behaviors for in-game characters without requiring programming. However, one major problem with this approach is that many LfD algorithms require large amounts of training data, and thus are not practical for learning from human demonstrators. In this paper, we focus on LfD with limited training data, and specifically on the problem of active LfD where the demonstrators are human. We present the results of a user study in comparing SALT, a new active LfD approach, versus a previous state-of-the-art Active LfD algorithm, showing that SALT significantly outperforms it when learning from a limited amount of data in the context of learning to play a puzzle video game.


SHRDLU: A Game Prototype Inspired by Winograd’s Natural Language Understanding Work

AAAI Conferences

This paper describes a game prototype called "SHRDLU" that explores the concept of designing a game around the ideas behind Winograd's original SHRDLU system. We briefly describe the main gameplay, as well as the natural language and inference architecture used by game NPCs.


Tracing Player Knowledge in a Parallel Programming Educational Game

AAAI Conferences

This paper focuses on tracing player knowledge in educational games. Specifically, given a set of concepts or skills required to master a game, the goal is to estimate the likelihood with which the current player has mastery of each of those concepts or skills. The main contribution of the paper is an approach that integrates machine learning and domain knowledge rules to find when the player applied a certain skill and either succeeded or failed. This is then given as input to a standard knowledge tracing module (such as those from Intelligent Tutoring Systems) to perform knowledge tracing. We evaluate our approach in the context of an educational game called Parallel to teach parallel and concurrent programming with data collected from real users, showing our approach can predict students skills with a low mean-squared error.


Ontanon

AAAI Conferences

This paper describes a game prototype called "SHRDLU" that explores the concept of designing a game around the ideas behind Winograd's original SHRDLU system. We briefly describe the main gameplay, as well as the natural language and inference architecture used by game NPCs.


Packard

AAAI Conferences

A significant amount of work has advocated that Learning from Demonstration (LfD) is a promising approach to allow end-users to create behaviors for in-game characters without requiring programming. However, one major problem with this approach is that many LfD algorithms require large amounts of training data, and thus are not practical for learning from human demonstrators. In this paper, we focus on LfD with limited training data, and specifically on the problem of active LfD where the demonstrators are human. We present the results of a user study in comparing SALT, a new active LfD approach, versus a previous state-of-the-art Active LfD algorithm, showing that SALT significantly outperforms it when learning from a limited amount of data in the context of learning to play a puzzle video game.


Kantharaju

AAAI Conferences

This paper focuses on tracing player knowledge in educational games. Specifically, given a set of concepts or skills required to master a game, the goal is to estimate the likelihood with which the current player has mastery of each of those concepts or skills. The main contribution of the paper is an approach that integrates machine learning and domain knowledge rules to find when the player applied a certain skill and either succeeded or failed. This is then given as input to a standard knowledge tracing module (such as those from Intelligent Tutoring Systems) to perform knowledge tracing. We evaluate our approach in the context of an educational game called Parallel to teach parallel and concurrent programming with data collected from real users, showing our approach can predict students skills with a low mean-squared error.


Packard

AAAI Conferences

A significant amount of work has advocated that Learning from Demonstration (LfD) is a promising approach to allow end-users to create behaviors for in-game characters without requiring programming. However, one major problem with this approach is that many LfD algorithms require large amounts of training data, and thus are not practical. In this paper, we focus on LfD with limited training data, and specifically on the problem of Active Learning from Demonstration in settings where the amount of data that can be queried from the demonstrator is limited by a predefined budget. We extend our novel Active Learning from Demonstration approach, SALT, and compare it to related LfD algorithms in both task performance (reward) and similarity to the demonstrator's behavior, when used with relatively small amounts of training data. We use Super Mario Bros. and two variations of the Thermometers puzzle game as our evaluation domains.


Learning Behavior from Limited Demonstrations in the Context of Games

AAAI Conferences

A significant amount of work has advocated that Learning from Demonstration (LfD) is a promising approach to allow end-users to create behaviors for in-game characters without requiring programming. However, one major problem with this approach is that many LfD algorithms require large amounts of training data, and thus are not practical. In this paper, we focus on LfD with limited training data, and specifically on the problem of Active Learning from Demonstration in settings where the amount of data that can be queried from the demonstrator is limited by a predefined budget. We extend our novel Active Learning from Demonstration approach, SALT, and compare it to related LfD algorithms in both task performance (reward) and similarity to the demonstrator's behavior, when used with relatively small amounts of training data. We use Super Mario Bros. and two variations of the Thermometers puzzle game as our evaluation domains.


Snodgrass

AAAI Conferences

The exploration of Procedural Content Generation via Machine Learning (PCGML) has been growing in recent years. However, while the number of PCGML techniques and methods for evaluating PCG techniques have been increasing, little work has been done in determining how the quality and quantity of the training data provided to these techniques effects the models or the output. Therefore, little is known about how much training data would actually be needed to deploy certain PCGML techniques in practice. In this paper we explore this question by studying the quality and diversity of the output of two well-known PCGML techniques (multi-dimensional Markov chains and Long Short-term Memory Recurrent Neural Networks) in generating Super Mario Bros. levels while varying the amount and quality of the training data.


Uriarte

AAAI Conferences

A significant amount of work exists on handling partial observability for different game genres in the context of game tree search. However, most of those techniques do not scale up to RTS games. In this paper we present an experimental evaluation of a recently proposed technique, "single believe state generation," in the context of StarCraft. We evaluate the proposed approach in the context of a StarCraft playing bot and show that the proposed technique is enough to bring the performance of the bot close to the theoretical optimal where the bot can observe the whole game state.