In AI, psychology, and education, a growing body of research supports the view that learning is a goal-directed process. Psychological experiments show that people with varying goals process information differently, studies in education show that goals have a strong effect on what students learn, and functional arguments in machine learning support the necessity of goalbased focusing of learner effort. At the Fourteenth Annual Conference of the Cognitive Science Society, a symposium brought together researchers in AI, psychology, and education to discuss goaldriven learning. This article presents the fundamental points illuminated at the symposium, placing them in the context of open questions and current research directions in goal-driven learning. Learning is a central area of study for researchers interested in human cognition as well as those interested in machine intelligence.

It was planned and coordinated by Kristiina Jokinen (Nara Institute of Science and Technology [NAIST]), Mark Maybury (The MITRE Corporation), Michael Zock (LIMSI-CNRS), and Ingrid Zukerman (Monash University). Thirty scholars from Europe, the United States, Australia, and Japan participated in the workshop. The purpose of the workshop was to clarify the role of rational and cooperative planning in generation in general and to bridge the gaps that seem to exist between theoretical models of planning agents and practical aspects of natural language generation (NLG) architecture. In recent years, there has been a focus shift in NLG from the study of well-formedness conditions (grammars) to the exploration of the communicative adequacy of linguistic forms: Speaking is viewed as an indirect means for achieving commupresentations, attempted to provide further material for building bridges. The workshop finished with a panel on the gaps and bridges theme, summarizing the topics of the ...

Subrata Dasgupta from the University of Louisiana at Lafayette spoke about the computer's role in the current revolution in cognitive science. His talk came from a historical perspective--how humankind has always felt an overwhelming need to understand the world around us and to control it for our own benefit. He further described how this need is now embodied in our need to understand our own cognitive processes--the very same organ that allows us to understand is not at all well understood. He described the roles that the computer has played as a metaphor for description and explanation and as an instru-The Thirteenth Annual International Conference of the Florida Artificial Intelligence Research Society was held in Orlando, Florida, on 22 to 24 May. The conference included sessions on 11 topics.

Whether one is learning a programming language by implementing a computer program, or learning calculus by solving problems, context-sensitive feedback and guidance are crucial to keeping problem-solving efforts fruitful and efficient. This article reviews AIbased algorithms that can diagnose student difficulties during active problem solving and serve as the basis for providing context-sensitive and individualized guidance. Artificial intelligence (AI) research solutions have the potential to boost the impact of these systems by enhancing their diagnostic capabilities. For example, unlike one-on-one human tutoring, it is rare to find computer-based learning environments that not only provide opportunities for practice on complex problems (such as working on multistep algebra problems, or practicing programming by writing multiline code), but also provide contextually relevant feedback and guidance based on an analysis of individual problem-solving actions. The problem with the first extreme is that students develop skills in context and complexity that are different from their eventual application context.

For those who haven't heard of it, EAAI is a symposium that is held in conjunction with AAAI. The symposium provides a venue for researchers and educators to discuss pedagogical issues and share resources related to AI and education. This year, the symposium featured a range of activities, including two invited talks, paper presentations, poster presentations, panels, and workshops. Several main themes of discussion at the symposium included the introduction of AI concepts in early courses, active learning, and massive open online courses (MOOCs) and flipped classrooms. With the emergence of "big data" as a buzzword in the mainstream media, new students are often interested in learning about this area but may not have the math or computing skills to support their interests.

Past Research in Expert Systems at ETSU Artificial intelligence research at East Texas State University (ETSU) began in the fall of 1983 with the development of a knowledge-based expert system to solve configuration problems. The intention was to develop a generic system that could be transferred from one problem domain to another. The problem domains selected on which the system was to be tested were the configuration of Hewlett-Packard Model 29 computer systems and the generation of degree plans for graduate students in the Computer Science Department at ETSU. The configurator is based on a semantic network that utilizes frames as a method of representing knowledge. Frames are used as nodes in the network and can contain facts, rules, and links to other nodes.

It included an invited talk, paper presentations, model AI assignments, a teaching and mentoring workshop, a best educational video award, and a robotics track. The symposium was established in response to growing community interest in sharing best practices for (1) how to teach AI and (2) how AI can serve as a motivating problem for teaching concepts in other areas of computer science, especially in introductory computer science courses. EAAI encourages the sharing of innovative educational approaches that convey or leverage AI and its many subfields, including robotics, machine learning, natural language, and computer vision. EAAI follows the successful 2008 Spring Symposium on "Using AI to Motivate Greater Participation in Computer Science" and the 2008 AAAI AI Education Colloquium. Fifty-five attendees formally registered for the event, but many other AAAI attendees also visited one or more EAAI events.

Articulating thought in computerbased media is a powerful means for humans to develop their understanding of phenomena. We have created DynaLearn, an intelligent learning environment that allows learners to acquire conceptual knowledge by constructing and simulating qualitative models of how systems behave. DynaLearn uses diagrammatic representations for learners to express their ideas. The environment is equipped with semantic technology components that are capable of generating knowledge-based feedback and virtual characters that enhance the interaction with learners. Teachers have created course material, and successful evaluation studies have been performed.

Don was one of the pioneers of our field, whose early research built the foundation for the area that would later come to be labeled "knowledge based systems" (and still later "expert systems"). Don received a B.S. in Electrical Engineering from Iowa State University in 1958, and an M.S. in Electrical Engineering from the University of California, Berkeley in 1964. He then entered the Ph.D. program at Stanford's newly created Cotiputer Science Department. While at Berkeley he met a young professor named Ed Feigenbaum, and when Feigenbaum moved to Stanford in 1965 Don became Ed's first Ph.D. student. Ed recalls: "In mid-1965 the DENDRAL project began in earnest, and Don was its first (and at the time its only) Ph.D. student.

The Dialogue on Dialogues workshop was organized as a satellite event at the Interspeech 2006 conference in Pittsburgh, Pennsylvania, and it was held on September 17, 2006, immediately before the main conference. It was planned and coordinated by Michael McTear (University of Ulster, UK), Kristiina Jokinen (University of Helsinki, Finland), and James A. Larson (Portland State University, USA). The one-day workshop involved more than 40 participants from Europe, the United States, Australia, and Japan. One of the motivations for furthering the systems' interaction capabilities is to improve the AI Magazine Volume 28 Number 2 (2007) ( AAAI) However, relatively little work has so far been devoted to defining the criteria according to which we could evaluate such systems in terms of increased naturalness and usability. It is often felt that statistical speech-based research is not fully appreciated in the dialogue community, while dialogue modeling in the speech community seems too simple in terms of the advanced architectures and functionalities under investigation in the dialogue community.