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This book discusses issues in generating coherent, effective natural language descriptions with integrated text and examples. This is done in the context of a system for generating documentation dynamically from the underlying software representations. Good documentation is critical for user acceptance of any complex system. Advances in areas such as knowledge-based systems, natural language, and multimedia generation now make it possible to investigate the automatic generation of documentation from the underlying knowledge bases. This has several important benefits: it is always accessible; it is always current, because the documentation reflects the underlying representation; and, it can take the communication context, such as the user, into account. The work described in this book compiles results from cognitive psychology and education on effective presentation of examples, as well as work on computational generation of examples from intelligent tutoring systems. It also takes into account computational learning from examples, and a characterization of good examples for just this purpose. Issues arising from these research areas--as well as issues coming from the author's own corpus analysis of instructional and explanatory texts--are discussed in the context of generating natural language descriptions of software constructs. A text planner is used for a hierarchy of communicative goals. Examples are treated as an integral part of the planning process and their interaction with text is represented at all stages. The strengths and limitations of this approach are also discussed. Although the focus of this book is the generation of natural language descriptions, a similar set of issues need to be addressed in the generation of multimedia descriptions. This book will be of interest to all researchers working in the areas of natural language interfaces, intelligent tutoring systems, documentation and technical writing, and educational psychology.
Vol inclu all ppers & postrs presntd at 2000 Cog Sci mtg & summaries of symposia & invitd addresses. Dealg wth issues of representg & modelg cog procsses, appeals to scholars in all subdiscip tht comprise cog sci: psy, compu sci, neuro sci, ling, & philo
The judiciary is in the early stages of a transformation in which AI (Artificial Intelligence) technology will help to make the judicial process faster, cheaper, and more predictable without compromising the integrity of judges' discretionary reasoning. Judicial decision-making is an area of daunting complexity, where highly sophisticated legal expertise merges with cognitive and emotional competence. How can AI contribute to a process that encompasses such a wide range of knowledge, judgment, and experience? Rather than aiming at the impossible dream (or nightmare) of building an automatic judge, AI research has had two more practical goals: producing tools to support judicial activities, including programs for intelligent document assembly, case retrieval, and support for discretionary decision-making; and developing new analytical tools for understanding and modeling the judicial process, such as case-based reasoning and formal models of dialectics, argumentation, and negotiation. Judges, squeezed between tightening budgets and increasing demands for justice, are desperately trying to maintain the quality of their decision-making process while coping with time and resource limitations. Flexible AI tools for decision support may promote uniformity and efficiency in judicial practice, while supporting rational judicial discretion. Similarly, AI may promote flexibility, efficiency and accuracy in other judicial tasks, such as drafting various judicial documents. The contributions in this volume exemplify some of the directions that the AI transformation of the judiciary will take.
With this volume in honour of Don Walker, Linguistica Computazionale con tinues the series of special issues dedicated to outstanding personalities who have made a significant contribution to the progress of our discipline and maintained a special collaborative relationship with our Institute in Pisa. I take the liberty of quoting in this preface some of the initiatives Pisa and Don Walker have jointly promoted and developed during our collaboration, because I think that they might serve to illustrate some outstanding features of Don's personality, in particular his capacity for identifying areas of potential convergence among the different scientific communities within our field and establishing concrete forms of coop eration. These initiatives also testify to his continuous and untiring work, dedi cated to putting people into contact and opening up communication between them, collecting and disseminating information, knowledge and resources, and creating shareable basic infrastructures needed for progress in our field. Our collaboration began within the Linguistics in Documentation group of the FID and continued in the framework of the !CCL (International Committee for Computational Linguistics). In 1982 this collaboration was strengthened when, at CO LING in Prague, I was invited by Don to join him in the organization of a series of workshops with participants of the various communities interested in the study, development, and use of computational lexica.
Automated Discourse Generation to the User-Centered Revolution: 1970-1995
Invited papers; knowledge representation and automated reasoning; tutoring systems; machine learning; neural networks; distributed AI; knowledge acquisition and knowledge bases; posters.
This handbook of computational linguistics, written for academics, graduate students and researchers, provides a state-of-the-art reference to one of the most active and productive fields in linguistics.
One-on-One Tutoring by Humans and Computers articulates the CIRCSIM-Tutor project, an attempt to develop a computer tutor that generates a natural language dialogue with a student. Editors Martha Evens and Joel Michael present the educational context within which the project was launched, as well as research into tutoring, the process of implementation of CIRCSIM-Tutor, and the results of using CIRCSIM-Tutor in the classroom. The domain of this project is cardiovascular physiology, specifically targeting first-year medical students, though the idea is applicable to the development of intelligent tutoring systems across populations, disciplines, and domains. This 5 year-long project was motivated by the belief that students need assistance in building appropriate mental models of complex physiological phenomena, as well as practice in expressing these ideas in their own words to fully develop those models, and experience in problem-solving to use those models effectively. The book outlines directions for future research, and includes distinct features such as: *detailed studies of human one-on-one tutoring; *learning outcomes resulting from use of the tutor; *natural language input parsed and translated into logical form; and *natural language output generated using the LFG paradigm. This volume will appeal to educators who want to improve human tutoring or use computer tutors in the classroom, and it will interest computer scientists who want to build those computer tutors, as well as anyone who believes that language is central to teaching and learning.
Computational Models of Mixed-Initiative Interaction brings together research that spans several disciplines related to artificial intelligence, including natural language processing, information retrieval, machine learning, planning, and computer-aided instruction, to account for the role that mixed initiative plays in the design of intelligent systems. The ten contributions address the single issue of how control of an interaction should be managed when abilities needed to solve a problem are distributed among collaborating agents. Managing control of an interaction among humans and computers to gather and assemble knowledge and expertise is a major challenge that must be met to develop machines that effectively collaborate with humans. This is the first collection to specifically address this issue.