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Modeling User Knowledge with Dynamic Bayesian Networks in Interactive Narrative Environments

AAAI Conferences

Recent years have seen a growing interest in interactive narrative systems that dynamically adapt story experiences in response to users’ actions, preferences, and goals. However, relatively little empirical work has investigated runtime models of user knowledge for informing interactive narrative adaptations. User knowledge about plot scenarios, story environments, and interaction strategies is critical in a range of interactive narrative contexts, such as mystery and detective genre stories, as well as narrative scenarios for education and training. This paper proposes a dynamic Bayesian network approach for modeling user knowledge in interactive narrative environments. A preliminary version of the model has been implemented for the Crystal Island interactive narrative-centered learning environment. Results from an initial empirical evaluation suggest several future directions for the design and evaluation of user knowledge models for guiding interactive narrative generation and adaptation.


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.


Does Artificial Intelligence Have A Dirty Little Secret?

#artificialintelligence

After reading a recent pair of articles covering the topic of artificial intelligence (AI), I am confused. On one hand, there's recent PwC findings suggesting AI could drive $15.7 trillion in productivity gains by 2030. On the other, a recent piece from the New York Times makes a compelling case that, despite all the hype, AI's dirty little secret is that "it still has a long, long way to go." There's no question AI is a developing technology. As the New York Times piece points out, we can find plenty of examples of robots falling over while opening doors, driverless cars needing human intervention, and machines that still cannot read reliably at the level of a sixth grader.


These Nearly Impossible Geography Questions Can Only Be Answered by Grade-Schoolers

National Geographic

Watch National Geographic staff try to answer some of the same questions asked to student contestants in the National Geography Bee. Each year, millions of fourth- through eighth-graders across the United States compete for the chance to participate in the National Geographic Bee finals. Many train for hundreds of hours and can invoke obscure geographic facts--like that the Madaba Map of the Holy Land was made with floor tiles--at a moment's notice. The ones that make the cut head to Washington, D.C., to show off their knowledge. They answer exceedingly difficult questions about the world--its history, inhabitants, lands, and seas--in a competition to take home the championship title, which comes with a $50,000 college scholarship and an all-expenses-paid Lindblad expedition, this year to the Galápagos Islands.


Inferential Statistics

#artificialintelligence

This Course 109,382 recent views This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data Duke University Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge.