The nested Chinese restaurant process is extended to design a nonparametric topic-model tree for representation of human choices. Each tree branch corresponds to a type of person, and each node (topic) has a corresponding probability vector over items that may be selected. The observed data are assumed to have associated temporal covariates (corresponding to the time at which choices are made), and we wish to impose that with increasing time it is more probable that topics deeper in the tree are utilized. This structure is imposed by developing a new “change point" stick-breaking model that is coupled with a Poisson and product-of-gammas construction. To share topics across the tree nodes, topic distributions are drawn from a Dirichlet process. As a demonstration of this concept, we analyze real data on course selections of undergraduate students at Duke University, with the goal of uncovering and concisely representing structure in the curriculum and in the characteristics of the student body.

The titles of the eight symposia were Artificial Intelligence and Health Communication, Artificial Intelligence and Sustainable Design, Artificial Intelligence for Business Agility, Computational Physiology, Help Me Help You: Bridging the Gaps in Human-Agent Collaboration, Logical Formalizations of Commonsense Reasoning, Multirobot Systems and Physical Data Structures, and Modeling Complex Adaptive Systems As If They Were Voting Processes. The goal of the Artificial Intelligence and Health Communication symposium was to advance the conceptual design of automated systems that provide health services to patients and consumers through interdisciplinary insight from artificial intelligence, health communication and related areas of communication studies, discourse studies, public health, and psychology. There is a large and growing interest in the development of automated systems to provide health services to patients and consumers. In the last two decades, applications informed by research in health communication have been developed, for example, for promoting healthy behavior and for managing chronic diseases. While the value that these types of applications can offer to the community in terms of cost, access, and convenience is clear, there are still major challenges facing design of effective health communication systems. Overall, the participants found the format of the symposium engaging and constructive, and they The symposium was organized around five main expressed the desire to continue this initiative in concepts: (1) Patient empowerment and education further events.

This prize is awarded biennially to recognize and encourage outstanding artificial intelligence research advances that are made by using experimental (Max Planck Institute for Biological Nectar, as well as poster presentations methods of computer science. Cybernetics), Karrie Karahalios (University by a select number of exceptional Thrun and Whittaker, whose teams of Illinois), Michael Kearns technical papers, short papers, student won the 2005 DARPA Grand Challenge (University of Pennsylvania), and Kurt abstracts, and doctoral consortium abstracts. A special Joint will feature talks on five award-winning in particular for high-impact IAAI-11/AAAI-11 Invited Talk by deployed AI applications and 14 contributions to the field of artificial David Ferrucci (IBM T. J. Watson Research emerging applications. The week is intelligence through innovation and Center) on "Building Watson: filled with a host of other programs, achievement in autonomous vehicle An Overview of DeepQA for the ...

Officers of  FLAIRS in 2011.

List of the organizers and conference committees for FLAIRS-24, including special tracks.

This study examines the relation between the linguistic features of freewrites and human assessments of freewriting quality. This study builds upon the authors’ previous studies in which a model was developed based on the linguistic features of freewrites written by 9th and 11th grade students to predict freewrite quality. The current study reexamines this model using number of propositions as a predictor instead of number of words because the number of propositions was expected to be a better proxy for number of ideas in contrast to simple text length. The results indicated that there were only slight advantages for using a measure for number of propositions, indicating that from an artificial intelligence perspective, the number of words was the better measure.

The AAAI-10 Workshop program was held Sunday and Monday, July 11–12, 2010 at the Westin Peachtree Plaza in Atlanta, Georgia. The AAAI-10 workshop program included 13 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Fun, Bridging the Gap between Task and Motion Planning, Collaboratively-Built Knowledge Sources and Artificial Intelligence, Goal-Directed Autonomy, Intelligent Security, Interactive Decision Theory and Game Theory, Metacognition for Robust Social Systems, Model Checking and Artificial Intelligence, Neural-Symbolic Learning and Reasoning, Plan, Activity, and Intent Recognition, Statistical Relational AI, Visual Representations and Reasoning, and Abstraction, Reformulation, and Approximation. This article presents short summaries of those events.

An experiment was conducted to test the efficacy of a new intelligent hypermedia system, MetaTutor, which is intended to prompt and scaffold the use of self-regulated learning (SRL) processes during learning about a human body system. Sixty-eight (N=68) undergraduate students learned about the human circulatory system under one of three conditions: prompt and feedback (PF), prompt-only (PO), and control (C) condition. The PF condition received timely prompts from animated pedagogical agents to engage in planning processes, monitoring processes, and learning strategies and also received immediate directive feedback from the agents concerning the deployment of the processes. The PO condition received the same timely prompts, but did not receive any feedback following the deployment of the processes. Finally, the control condition learned without any assistance from the agents during the learning session. All participants had two hours to learn using a 41-page hypermedia environment which included texts describing and static diagrams depicting various topics concerning the human circulatory system. Results indicate that the PF condition had significantly higher learning efficiency scores, when compared to the control condition. There were no significant differences between the PF and PO conditions. These results are discussed in the context of development of a fully-adaptive hypermedia learning system intended to scaffold self-regulated learning.

This paper provides an overview of a web-based, database-driven cognitive support system for scaffolding ill-structured problem solving processes through fostering self-regulation. Self-regulation learning and ill-structured problem-solving theories guided the design framework of this cognitive tool. Of particular interest are the roles of question prompts, expert view, and peer review mechanisms in supporting self-monitoring, self-regulation, and self-reflection in the processes of ill-structured problem solving, which have been tested through empirical studies in various content domains and contexts. Based on findings, suggestions are made to improve the cognitive support system for future research, including mapping self-regulation learning processes more closely with ill-structured problem-solving processes, and focusing on the system’s capability to automatically adapt scaffolding based on individual needs and prior knowledge.

In the winter, 2004 issue of AI Magazine, we reported Vulcan Inc.'s first step toward creating a question-answering system called "Digital Aristotle." The goal of that first step was to assess the state of the art in applied Knowledge Representation and Reasoning (KRR) by asking AI experts to represent 70 pages from the advanced placement (AP) chemistry syllabus and to deliver knowledge-based systems capable of answering questions from that syllabus. This paper reports the next step toward realizing a Digital Aristotle: we present the design and evaluation results for a system called AURA, which enables domain experts in physics, chemistry, and biology to author a knowledge base and that then allows a different set of users to ask novel questions against that knowledge base. These results represent a substantial advance over what we reported in 2004, both in the breadth of covered subjects and in the provision of sophisticated technologies in knowledge representation and reasoning, natural language processing, and question answering to domain experts and novice users.