My Computer Is an Honor Student -- But How Intelligent Is It? Standardized Tests as a Measure of AI Given the well-known limitations of the Turing test, there is a need for objective tests to both focus attention on, and measure progress toward, the goals of AI. In this paper we argue that machine performance on standardized tests should be a key component of any new measure of AI, because attaining a high level of performance requires solving significant AI problems involving language understanding and world modeling -- critical skills for any machine that lays claim to intelligence. In addition, standardized tests have all the basic requirements of a practical test: they are accessible, easily comprehensible, clearly measurable, and offer a graduated progression from simple tasks to those requiring deep understanding of the world. Here we propose this task as a challenge problem for the community, summarize our state-of-the-art results on math and science tests, and provide supporting data sets (www.allenai.org). In the years since, this test has been criticized as being a poor replacement for the original enquiry (for example, Hayes and Ford [1995]), which raises the question: what would a better replacement be?

The 28th International Workshop on Qualitative Reasoning (QR-15) presented advances toward reasoning tractably with massive qualitative and quantitative models, automatically learning and reasoning about continuous processes, and representing knowledge about space, causation, and uncertainty. The technical track included two invited talks, 11 oral presentations, and 5 poster presentations.

Woodrow Wilson (Woody) Bledsoe died on 4 October 1995 of ALS, more commonly known as Lou Gehrig's disease. Woody was one of the founders of AI, making early contributions in pattern recognition and automated reasoning. He continued to make significant contributions to AI throughout his long career. His legacy consists not only of his scientific work but also of several generations of scientists who learned from Woody the joy of scientific research and the way to go about it. Woody's enthusiasm, his perpetual sense of optimism, his can-do attitude, and his deep sense of duty to humanity offered those who knew him the hope and comfort that truly good and great men do exist. Woody was one of the founders of AI, making early contributions in pattern recognition and automated reasoning. He continued to make significant contributions to AI throughout his long career. His legacy consists not only of his scientific work but also of several generations of scientists who learned from Woody the ...

What Do You Need to Know to Use a Search Engine? Their ability to provide answers to queries quickly is a remarkable testament to the power of many of the fundamental methods of AI. They also highlight many of the issues that are common to sophisticated AI question-answering systems. It has become clear that people think of search programs in ways that are very different from traditional information sources. Rapid and ready-at-hand access, depth of processing, and the way they enable people to offload some ordinary memory tasks suggest that search engines have become more of a cognitive amplifier than a simple repository or front end to the Internet.

The R language is best suited for statistical computations and visualization. Even if you do not have any prior experience in programming or statistical software, this Learning Path will help you get you up and running not only with the basics of R but also statistically modeling. This learning journey begin by introducing R and setting things up so that you are ready to go using RStudio, the associated IDE. Then, you will look at R as a programming language and see how the standard things are done in it. You will obtain a dataset and then learn how to clean the dataset.

This special issue of AI Magazine covers select applications from the IAAI conference held in 2008 in Chicago. The articles address a broad range of very challenging issues and contain great lessons for AI researchers and application developers. Your rice cooker, toaster, and washing machine have their own minds. Your car parks itself; its transmission adapts itself to your driving preferences, and it tells the dealership which parts it thinks it will need to have replaced three months from now. Your PDA knows your preferences and acts as your personal radio station, playing only music you like.

The goal of an interactive narrative system is to immerse users in a virtual world such that they believe that they are an integral part of an unfolding story and that their actions can significantly alter the direction or outcome of the story. In this article we review the ways in which artificial intelligence can be brought to bear on the creation of interactive narrative systems. We lay out the landscape of about 20 years of interactive narrative research and explore the successes as well as open research questions pertaining to the novel use of computational narrative intelligence in the pursuit of entertainment, education, and training. The prevalence of storytelling in human culture may be explained by the use of narrative as a cognitive tool for situated understanding (Gerrig 1993). This narrative intelligence -- the ability to organize experience into narrative form -- is central to the cognitive processes employed across a range of experiences, from entertainment to active learning.

Many of the intelligent tutoring systems that have been developed during the last 20 years have proven to be quite successful, particularly in the domains of mathematics, science, and technology. They produce significant learning gains beyond classroom environments. They are capable of engaging most students' attention and interest for hours. We have been working on a new generation of intelligent tutoring systems that hold mixedinitiative conversational dialogues with the learner. The tutoring systems present challenging problems and questions to the learner, the learner types in answers in English, and there is a lengthy multiturn dialogue as complete solutions or answers evolve.

Included are articles on integrated systems such as virtual humans, an intelligent textbook, and a game-based learning environment as well as technology-focused components such as student models and data mining. The winter issue will conclude with an article summarizing the contemporary and emerging challenges at the intersection of AI and education. Everyone recognizes the need to improve teacher effectiveness, to improve student engagement, and to create a twenty-first century education system that maximizes potential of every student. The challenges that must be addressed to make these improvements greatly exceed the scope of any single approach, whether it is educational technology, improved teacher training and better after school programs, and so on. In past research, AI -- with its inextricable links to cognitive science, psychology, and mathematics -- has proven a close fit for many of these challenging educational problems.

Included are articles on integrated systems such as virtual humans, an intelligent textbook, and a game-based learning environment as well as technology-focused components such as student models and data mining. This issue concludes with an article summarizing the contemporary and emerging challenges at the intersection of AI and education. Everyone recognizes the need to improve teacher effectiveness, to improve student engagement, and to create a twenty-first century education system that maximizes potential of every student. The challenges that must be addressed to make these improvements greatly exceed the scope of any single approach, whether it is educational technology, improved teacher training and better after school programs, and so on. In past research, AI -- with its inextricable links to cognitive science, psychology, and mathematics -- has proven a close fit for many of these challenging educational problems.