While online handwriting recognition is an area of longstanding and ongoing research, the recent emergence of portable, pen-based computers has focused urgent attention on usable, practical solutions. We discuss a combination and improvement of classical methods to produce robust recognition of hand-printed English text for a recognizer shipping in new models of Apple Computer's The ANN character classifier required some innovative training techniques to perform its task well. The dictionaries required large word lists, a regular expression grammar (to describe special constructs such as date, time, and telephone numbers), and a means of combining all these dictionaries into a comprehensive language model. In addition, well-balanced prior probabilities had to be determined for in-dictionary and out-of-dictionary writing. Together with a maximum-likelihood search engine, these elements form the basis of the so-called "Print Recognizer," which was first shipped in NEWTON OS 2.0-based MES-SAGEPAD 120 units in December 1995 and has There is ample prior work in combining low-level classifiers with dynamic time warping, hidden Markov models, Viterbi algorithms, and other search strategies to provide integrated segmentation and recognition for writing (Tappert, Suen, and Wakahara 1990) and speech (Renals et al. 1992).

My observations are based on learning sciences research of the past several decades, the possibilities of new technologies of the past few years, and my experience as program officer for the National Science Foundation's Cyberlearning and Future Learning Technologies program. My thesis is that new technologies have potential to transform possibilities for fostering learning in both formal and informal learning environments by making it possible and manageable for learners to engage in the kinds of project work that professionals engage in and learn important content, skills, practices, habits, and dispositions from those experiences. The expertise of AI researchers and practitioners is critical to that vision, but it will require teaming up with others -- for example, technology imagineers, educators, and learning scientists. The articles report on the newest in intelligent tutoring systems and resources (Bredeweg et al. 2013, Rus et al. 2013; Chaudhri et al. 2013), virtual humans and conversational agents (Swartout et al. 2013), assessment and student modeling for personalization (Conati and Kardan 2013, Koedinger et al. 2013), and intelligently controlled virtual environments (Lester et al. 2013). The final article in the set (Woolf et al. 2013), of which I am a coauthor, suggests needs and challenges facing STEM education (science, technology, engineering, and mathematics) that artificial intelligence might address -- mentors for every learner, fostering learning of 21st-century skills, automating assessment in ways that support learning, universal access, and lifelong and life-wide learning.

Alan Turing's decades-old question still influences artificial intelligence because of the simple test he proposed in his article in Mind. In this article, AI Magazine collects presentations about the first round of the classic Turing Test of machine intelligence, held November 8, 1991 at The Computer Museum, Boston. Robert Epstein, Director Emeritus, Cambridge Center for Behavioral Studies, and an adjunct professor of psychology, Boston University, University of Massachusetts (Amherst), and University of California (San Diego) summarizes some of the difficult issues during the planning of this first real-time competition, and describes the event. Presented in tandem with Dr. Epstein's article is the actual transcript of session that won the Loebner Prize Competition--Joseph Weintraub's computer program PC Therapist. In 1985 an old friend, Hugh Loebner, told me excitedly that the Turing Test should be made into an annual contest.

The articles in this special issue of AI Magazine include those that propose specific tests and those that look at the challenges inherent in building robust, valid, and reliable tests for advancing the state of the art in AI. To people outside the field, the test -- which hinges on the ability of machines to fool people into thinking that they (the machines) are people -- is practically synonymous with the quest to create machine intelligence. Within the field, the test is widely recognized as a pioneering landmark, but also is now seen as a distraction, designed over half a century ago, and too crude to really measure intelligence. Intelligence is, after all, a multidimensional variable, and no one test could possibly ever be definitive truly to measure it. Moreover, the original test, at least in its standard implementations, has turned out to be highly gameable, arguably an exercise in deception rather than a true measure of anything especially correlated with intelligence.

However, existing online learning techniques typically applied to robot learning (for example, Hexmoor, Meeden, and Murphy [1997]) differ fundamentally from human learning. Online root learning using robot sensors is not equivalent to autonomous mental development in robots, nor should mental develop-This article describes a workshop on mental development and learning issues that are relevant to both machine and human sciences. It was jointly funded by the National Science Foundation and the Defense Advanced Research Projects Agency and held at Michigan State University on 5 to 7 April 2000. Such systems already exist (for example, systems that use neural network techniques). There is a need, therefore, for increased studies in computational autonomous mental development (CAMD) that are of interest to both machine and human intelligence researchers.

In this article, we describe a deployed educational technology application: the Criterion Online Essay Evaluation Service, a web-based system that provides automated scoring and evaluation of student essays. Criterion has two complementary applications: (1) Critique Writing Analysis Tools, a suite of programs that detect errors in grammar, usage, and mechanics, that identify discourse elements in the essay, and that recognize potentially undesirable elements of style, and (2) e-rater version 2.0, an automated essay scoring system. Critique and e-rater provide students with feedback that is specific to their writing in order to help them improve their writing skills and is intended to be used under the instruction of a classroom teacher. Both applications employ natural language processing and machine learning techniques. All of these capabilities outperform baseline algorithms, and some of the tools agree with human judges in their evaluations as often as two judges agree with each other. Unfortunately, this puts an enormous load on the classroom teacher, who is faced with reading and providing feedback for perhaps 30 essays or more every time a topic is assigned. As a result, teachers are not able to give writing assignments as often as they would wish. With this in mind, researchers have sought to develop applications that automate essay scoring and evaluation. Work in automated essay scoring began in the early 1960s and has been extremely productive (Page 1966; Burstein et al. 1998; Foltz, Kintsch, and Landauer 1998; Larkey 1998; Rudner 2002; Elliott 2003). Detailed descriptions of most of these systems appear in Shermis and Burstein (2003). Pioneering work in the related area of automated feedback was initiated in the 1980s with the Writer's Workbench (MacDonald et al. 1982). The Criterion Online Essay Evaluation Service combines automated essay scoring and diagnostic feedback. The feedback is specific to the student's essay and is based on the kinds of evaluations that teachers typically provide when grading a student's writing. Criterion is intended to be an aid, not a replacement, for classroom instruction. Its purpose is to ease the instructor's load, thereby enabling the instructor to give students more practice writing essays. Criterion contains two complementary applications that are based on natural language processing (NLP) methods. Critique is an application that is comprised of a suite of programs that evaluate and provide feedback for errors in grammar, usage, and mechanics, that identify the essay's discourse structure, and that recognize potentially undesirable stylistic features. The companion scoring application, e-rater version 2.0, extracts linguistically-based features from an essay and uses a statistical model of how these features are related to overall writing quality to assign a holistic score to the essay. Figure 1 shows Criterion's interface for submit-

This article describes a method for building a cognitive map of a virtual urban environment. Our routines enable virtual humans to map their environment using a realistic model of perception. We based our implementation on a computational framework proposed by Yeap and Jefferies (1999) for representing a local environment as a structure called an absolute space representation (ASR). Their algorithms compute and update ASRs from a 2-1/2-dimensional (2-1/2D) sketch of the local environment and then connect the ASRs together to form a raw cognitive map. Our work extends the framework developed by Yeap and Jefferies in three important ways.

A learning problem is referred to as classification if its output take discrete values in a set of possible categories and regression if it has continuous real-valued output. A simple and useful model of an input-output functional relationship is to assume that the output variable can be expressed approximately as a linear combination of its input vector components. These linear models include the linear least squares method for regression and the logistic regression method for classification. Because a linear model has limited prediction power by itself, there has been extensive research in nonlinear models such as neural networks. However, there are two major problems with the use of nonlinear models: First, they are theoretically difficult to analyze, and second, they are computationally difficult to solve.

The services and benefits that clients receive are based largely on such reviews and assessments. The failure to perform accurate reviews and assessments in a timely manner can result in a client being denied access to services when they most need them. The typical scenario with regard to case review and assessment in social services situations involves a professional caseworker reviewing a client's file, conducting a phone or in-person interview if necessary, and making an assessment using the information obtained from the review and heuristics developed from experience. The caseworker is generally a professional who possesses expertise in the appropriate field. Some examples of fields where this expertise is found include medicine, mental health, and education.

Its multiple functions-- including grammar-based gap-filling exercises, scenario show, free chatting, and chatting on a given topic--can satisfy the various requirements for students with different backgrounds and learning abilities. After a brief explanation of the conception of the dialogue system, as well as a survey of related works, I will illustrate the system structure and describe its pedagogical functions with the underlying AI techniques, such as natural language processing and rulebased reasoning, in detail. I will summarize the free Internet usage within a six-month period and its integration into English classes in universities and middle schools. The evaluation findings about the class integration show that the chatting function has been improved and frequently utilized by users, and the application of the CSIEC system on English instruction can motivate learners to practice English and enhance their learning process. Finally, I will conclude with potential improvements.