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AITopics/Bioinformatics
Bioinformatics at Stanford University. By Russ B. Altman. "The following offers a summary of information about bioinformatics at Stanford. It is meant as advisory information for people interested in bioinformatics, written from my own perspective." Biomedical Security Institute - a collaboration between Carnegie Mellon University and the University of Pittsburgh.
AITopics/BridgeDo not pass Go. Computers can beat the world's best chess players but have yet to master other classic games like Go. By David Levy. The Guardian (October 24, 2002). "Ever since Garry Kasparov's sensational 1997 loss to the IBM chess monster Deep Blue, the chess world has thirsted for revenge. But the first opportunity ended in failure in Bahrain on Saturday, when Kasparov's former pupil and successor as World Champion, Vladimir Kramnik, could only draw an 8-game match against one of the world's leading chess engines, Fritz. But this was just the latest in a long series of human versus computer encounters that illustrate the inexorable march of artificial intelligence (AI). It's a story that began at a Dartmouth University conference in 1956, when several of the founding fathers of AI defined the goals of that infant science. One of them was to create a computer program that could defeat the world chess champion. Success would, those scientists believed, reach to the very core of human intellectual endeavour. By the early 1990s, due in no small part to the successes achieved in computer chess, the interest of the AI community had spread to many other games of skill, including backgammon, bridge, Go and Scrabble. Where exactly are we now in this fascinating struggle? ... Two games proving even tougher to crack than chess are bridge and Go." Internet Bridge Archive. Maintained by Marcus Buchhorn, Australian National University. Scroll down the page to get Computers and Bridge link. Scroll further for an interesting newsgroup discussion thread on what needs to be considered in writing bridge playing programs. AITopics/BusinessImagination at Work - Robots. Supercomputers. AI. Five CIO 100 honorees are boosting revenue, cutting costs and maybe saving the planet with these and other cool technologies. Editorial by Meridith Levinson. CIO (August 15, 2006). "If necessity is the mother of invention, then capitalism is surely the mother of innovation. Five of this year's CIO 100 honorees were driven to develop unique applications of undeniably cool technologies by the almighty dollar (the need to make it and to save it). ... To prevent its energy costs from skyrocketing, public utility JEA implemented an intelligent system that determines the perfect mix of oil and natural gas to produce electricity while minimizing nitrous oxide emissions. ... The Ohio State University Medical Center freed up maintenance staff and increased customer satisfaction by deploying robots to handle tasks such as removing trash and transporting meals." This article is part of their special Business Intelligence report. Eureka! Knowledge Discovery. By Neena Buck. Software Magazine. December 2000/January 2001 cover story. "Knowledge discovery and data mining (KDD) is evolving from an esoteric art and a point solution, to a mainstream technology embedded in a variety of solutions, to help businesses turn information into insight." Intelligent Software. By Helen Atkinson. DC Velocity Magazine (July 2003). "Sure you have plenty of brainpower. But when it comes to complex logistics or warehousing decisions, an intelligent software 'agent' may be able to make the call better, faster or more cost effectively than you can." Artificial Intelligence ...Within. Artificial Intelligence methods continue to provide upfront benefits in industrial and consumer arenas, although they're increasingly found working quietly in the background. By Frank J. Bartos. Control Engineering (September 1, 2003). "Perhaps we don't hear much about artificial intelligence (AI) methods used within today's technologies because it's slightly unnerving when computers emulate human thinking. Yet we, and computers themselves, continue to improve the way AI works quietly in the background to optimize, reduce process costs, and improve timing and product quality. For some tough, nonlinear applications, AI may be the only solution. ... Actually, AI consists of various technologies—expert systems, fuzzy logic, artificial neural networks, and genetic algorithms, among others." This article not only explains the technology, but also provides examples of many real world applications. Watch that eek-mail. By Chen Bin. The Straits Times (January 14, 2003). "I refer to the case of a manager being sued for defamation by a rival firm ('Manager sued over e-mail to his staff'; ST, Jan 8) based on the contents of an internal e-mail. This case has once again highlighted the potential risks associated with e-mail use in today's business environment. ... The good news is that there are affordable technology solutions in the market to automate the management of e-mail use. E-mail filtering technology developed with artificial intelligence can scan all the e-mail and look for potential risks before they are allowed to go through. Organisations can use e-mail filtering tools to define their own control rules which will then be enforced automatically." Harnessing Artificial Intelligence in Heavy Industry. CANMET Energy Technology Centre - Ottawa. "Heavy industry is enlisting artificial intelligence - an advanced computer science that seeks to approximate some of the capacities of the human brain - to automate complex processes and extend the skills of human operators. Well-considered application of the technology can boost productivity, quality and energy efficiency, according to a report by the Emerging Technologies Program. ... About 70 percent of all AI installations in heavy industry are expert systems; the remainder are neural networks and fuzzy logic systems. ... Some 40 percent of AI applications in heavy industry involve process control. ... Users report a wide range of benefits from the use of AI, including improved decision making, more responsive control, more efficient material flow, increased labour efficiency, greater consistency in product quality and reduced maintenance costs." "As I was preparing to write this article, I searched the CIO.com website to see if my topic had a name. Lo and behold, not only did it have a name, but Tom Davenport wrote an article about it. [Decision Evolution; October 1, 2004.] 'It' is what he called, 'automated decision systems.' In his article, Mr. Davenport observed that we have moved beyond decision support systems to something that is more powerful and more useful than has been realized in the past. The promise of artificial intelligence, and all of its successors, is beginning to be realized in real world applications. I found myself agreeing with his main points while experiencing keen a sense of déjà vu. Over the years, I've read similar comments about earlier generations of the next big thing in IT that did not live up to the hype, including artificial intelligence (AI), as Davenport notes. In his concluding paragraph, he states: 'This brave new world has been along time coming, but it is clearly upon us now. Businesses need to incorporate automated decision making into their strategies and processes or they won't be successful very long…' Is he accurately predicting the future, or will this be another case of over-promise, under-deliver? As CIO, you will need to make that call. Make the right decision and you're a hero. Guess wrong and you're not. For what it's worth, after much soul searching, I agree with Davenport. This time it's for real. His call for action is prudent, and CIOs need to act now." 'Intelligent' grinding may save $1 billion. United Press International / available from The Washington Times (May 6, 2004). "Researchers at Indiana's Purdue University are working to develop an 'intelligent' grinding system that could save U.S. companies $1 billion annually. The savings would come in manufacturing costs by improving precision-grinding processes for parts production. ... [Yung] Shin said his team of researchers hope to develop a system that enables 'relatively inexperienced employees to operate grinding machinery with the same precision as these rare, highly experienced workers.' The 'intelligent optimization and control grinding processes' use artificial-intelligence software, which mimics how people think, in order to learn and adapt to changing conditions."
Integrated Reasoning Projects from the National Research Council of Canada. Among the many projects you'll discover here is The Paper Maker's Advisor (PMA): "a monitoring and diagnostic system for use in paper mills." OSHA eTools: "eTools are 'stand-alone,' interactive, Web-based training tools on occupational safety and health topics. They are highly illustrated and utilize graphical menus. Some also use expert system modules, which enable the user to answer questions, and receive reliable advice on how OSHA regulations apply to their work site. Expert Advisors are based solely on expert systems." BU grad finds pattern for success - Vestal man's embroidery technology impacts textile industry. By My-Ly Nguyen. Press & Sun-Bulletin (March 2, 2003). "Five computer work stations, a commercial embroidery machine and a lot of brain power are nearly all David Goldman needs to run his growing business, Soft Sight Inc. The software company he founded in August 1998, after earning his doctorate in computer science at Binghamton University, may revolutionize the textile industry with its flagship product: one of the first embroidery design automation systems to hit the global market. ... Goldman's product digitally automates the embroidery design process using sophisticated artificial intelligence software that can mimic the thought and decision process of a human counterpart. A scanned image is used to generate the stitch placement needed to optimally sew the image on a commercial embroidery machine. Human error is dramatically reduced and the costs of producing embroidered apparel can decrease significantly, Goldman said. 'We haven't seen a program of this sophistication before,' said Larry Lawley, president of Data-Stitch Inc., an embroidery equipment and software company based in the Dallas/Fort Worth, Texas area. 'The technology's impact on the industry has been phenomenal.' ... Goldman and his team are continually working to enhance the automation technology with help from the National Science Foundation and Binghamton University." Agent-Based Systems for Intelligent Manufacturing: A State-of-the-Art Survey. By Weiming Shen and Douglas H. Norrie. (1999). Knowledge and Information Systems 1(2); 129-156. "Abstract. Agent technology has been considered as an important approach for developing distributed intelligent manufacturing systems. A number of researchers have attempted to apply agent technology to manufacturing enterprise integration, supply chain management, manufacturing planning, scheduling and control, materials handling, and holonic manufacturing systems. This paper gives a brief survey of some related projects in this area, and discusses some key issues in developing agent-based manufacturing systems such as agent technology for enterprise integration and supply chain management, agent encapsulation, system architectures, dynamic system reconfiguration, learning, design and manufacturability assessments, distributed dynamic scheduling, integration of planning and scheduling, concurrent scheduling and execution, factory control structures, potential tools and standards for developing agent-based manufacturing systems. An extensive annotated bibliography is provided.' Manager's Guide to Neural Networks. From Z Solutions. "From the standpoint of an individual manager's team, the challenge is increasingly one of understanding and organizing large amounts of information to improve knowledge of the organization's business and markets." AITopics/CaseBasedReasoningA Discourse on Law and Artificial Intelligence. By Michael Aikenhead (1996). 5 Law Technology Journal 1. "[T]he dichotomy between rule based systems and cased based reasoning systems in AI and law research reflects an underlying jurisprudential debate that has raged for the last century. ... Instead of implying that legal reasoning is primarily a process of deduction or a process of analogising the theory of law as discourse requires a richer view of the process of legal reasoning."
Nagel, Rebecca Thompson. June/July 1998. HAL, Esq. - Will computers someday replace attorneys in the delivery of legal services? We profile one woman whose work with artificial intelligence could forecast the future of the profession. Law Office Computing (subscription req'd.). "A computer that can think like an attorney? Artificial intelligence in a real-life application? Science fiction, right? Well, a system like the one described above is not yet available...commercially. But it does exist in the laboratory of University of Massachusetts, Amherst professor Dr. Edwina Rissland. ... The key to these programs is case-based reasoning (CBR) -- a subsection of AI that uses examples and analogy, as opposed to rules or logic, to solve problems." AITopics/CheckersKonane -- Hawaiian Checkers. From Peter Ingebretson. "About the Game: Konane is an old Hawaiian game, similar to many varieties of checkers. Strategy is reasonably simple, but the game is difficult to win against a talented opponent. The AI playing this game uses a simple minimax algorithm, with alpha-beta pruning to reduce the size of the search tree. As such, it is quite challenging when searching moves deeper than five or six turns in advance." AITopics/Chess
Deep Blue victory still a milestone 10 years later. By Julie Moran Alterio. The Journal News (May 6, 2007). "$137.50. That's how much it costs today to buy the home version of the Deep Fritz software that beat world chess champion Vladimir Kramnik in a match last year. What a difference a decade makes. This week marks the 10th anniversary of the first time a computer bested a reigning world chess champ. That feat cost Armonk-based IBM Corp. about $5 million. The face-off between IBM's Deep Blue and Garry Kasparov in New York City culminated in a victory for machine over man in the final joust of the six-game match May 11, 1997. ... What might be surprising to contemplate today is how much Kasparov was favored to win at the time of the match. ... The first computer chess programs date to the 1950s, including one written for an IBM 704 mainframe that took 8 minutes to make a move and could be defeated by a beginner. ... When he was interviewed on National Public Radio, a caller asked what the big deal was, didn't they just program the moves into the computer, [Joel] Benjamin recalled. 'A lot of people didn't realize just how historic it was because even then computers were credited with being able to do anything,' he said." Chess Is Too Easy. By Selmer Bringsjord. Technology Review. March/April 1998. "The victory last spring by IBM's Deep Blue computer over the world's greatest human chess player, Gary Kasparov, obliterated Dreyfus's prediction. But does it also argue for Strong rather than Weak AI ?" The Game of Chess. By Herbert A. Simon and Jonathan Schaeffer. Handbook of Game Theory with Economic Applications, vol. 1, Robert J. Aumann and Sergiu Hart (editors), Elsevier Science Publishers, Netherlands, pp. 1-17, 1992. (Also available as CMU technical report AIP-105.) Available in several formats from CiteSeer.
Link to Review of book: , bgb , 10/17/08 and its collection of "Ask the Expert" questions (and answers) about computer chess. Standage, Tom. The Turk: The Life and Times of the Famous Eighteenth-Century Chess-Playing Machine. Walker & Company, New York, 2002. "Part historical detective story, part biography, The Turk relates the saga of the machine's remarkable and checkered career against the backdrop of the industrial revolution, as mechanical technology opened up dramatic new possibilities and the relationship between people and machines was being redefined. Today, in the midst of the computer age, it has assumed a new significance, as scientists and philosophers continue to debate the possibility of machine intelligence. To modern eyes, the Turk now seems to have been a surprisingly farsighted invention, and its saga is a colorful and important part of the history of technology." From the "About the Book" page in the book's web site where you'll also find an interview with the author, Chater One of the book, and much more. AITopics/CommonSenseNewcomer's Guide to Commonsense Computing. From Commonsense Computing @ Media [the MIT Media Lab]. "Why give computers common sense? What does it even mean to give computers common sense? Here are several articles explaining why this problem is both challenging and important to solve if we want to take our computing technology to the next level." FIXED: , bgb , 10/17/08
Guess who's smarter. As sophisticated as computing has become, machines still lack the common sense of a 3-year-old. But MIT artificial intelligence researchers are tackling ways to start building that basic breadth of knowledge into programs and applications. By D.C. Denison. The Boston Globe (May 26, 2003; page D1). "But now there are signs that 'common sense' artificial intelligence research may be making a comeback, sparked by projects like [Push] Singh's Open Mind database. For the first time, after decades of theoretical research, researchers and programmers have begun using a freely distributed, natural language common sense database to start the process of building common sense into products, programs, and applications. In fact, as Singh sits in his cramped office in the Media Lab, he's able to point in the direction of a number of MIT researchers using his database for applications that may soon bring common sense AI to consumers. A few doors down to the right, Barbara Barry, a graduate student in the Media Lab's Interactive Cinema group, is working with Singh to build common sense into video cameras. On the other side of the Media Lab, Henry Lieberman, a research scientist who works with the Software Agents Group, is using common sense to enhance e-mail programs, language translation software, even a search engine. And just outside Singh's office, the Media Lab's 'wearable computing' group is building common sense into the devices and sensors they believe many of us will be wearing in the future." From 2001 to 2001: Common Sense and the Mind of HAL. By Douglas B. Lenat. [From HAL's Legacy: 2001's Computer as Dream and Reality edited by: David G. Stork. MIT Press. The MIT Press provides an abstract online, and the author has the full-text of the article available on the Cycorp web site.] Artificial Intelligence - Help Wanted - AI Pioneer Minsky. By Kevin Featherly. Newsbytes (August 31, 2001). "It is hard to find people who want to tackle common-sense reasoning, [Minsky] said, mainly because creating common-sense responses is an enormous programming challenge." "Birth of a Thinking Machine. For 17 years, a team has been trying to develop the most sophisticated artificial intelligence system ever. This summer, the public will be able to see its work." By Michael A. Hiltzik. The Los Angeles Times (June 21, 2001) / made available by Cycorp. "Cyc already has displayed the ability to identify common-sense absurdities. 'Cyc already knows that people have to be a certain age before they're hired for a job,' Lenat says, meaning that it could clear such inaccurate entries as mistaken birth dates from corporate payroll records."
"Learner is a system which interactively acquires knowledge about the everyday world over objects. We aim to collect knowledge about everyday world which computers do not have and which is not easily obtainable with current text extraction methods. The collection is carried out both over the web and in a kiosk setting at a science museum exhibit titled 'Robots and Us'. Learner, developed by Dr. Timothy Chklovski, currently at the Interactive Knowledge Capture group at ISI [the Information Sciences Institute at USC], is poised to supercede an earlier version of Learner, the work carried out by Dr. Chklovski as PhD research at MIT." Also see this related USC press release (August 2, 2005). AITopics/ConstraintBasedReasoningArtificial intelligence - Solving problems for the real world. By Billy Defrain. Daily Nebraskan (March 21, 2005). "For a typical American, the mention of artificial intelligence may conjure up nasty images of a robot wielding a plasma rifle atop a pyramid of human skulls. Mention artificial intelligence to Berthe Choueiry, though, and she thinks of problems. Choueiry, an associate professor of computer science and engineering, conducts artificial intelligence research at the University of Nebraska-Lincoln. And out of the wide field of artificial intelligence, her research focuses on constraint processing. This involves developing techniques to solve decision problems and applying them to real world uses, Choueiry said. ... She works to generate 'solutions that hopefully apply to real world problems like resource allocation, airline times and natural language processing,' Choueiry said. Although her tools are elaborate mathematical functions, her goal is to keep them as simple as possible. 'We’d like to develop tools for you to use constraints without even thinking of them or having to learn what they are,' she said. Constraint propagation, which is Choueiry’s speciality, is just one method of reasoning for artificial intelligence. Constraint-based reasoning is a deductive process in which the program looks at a group of data by considering which responses are not acceptable. These unacceptable responses are constraints.... Choueiry said one of the most difficult aspects of constraint processing is the concept of combinatorial explosion." Constraint Satisfaction Problems: Definition of CSP - A simple example: the crossword puzzle. From Marc Torrens. Follow the links to find out what crossword puzzles have to do with CSP's. CSP & Games from Professors Tomás Lozano-Pérez & Leslie Kaelbling's Spring 2003 course, Artificial Intelligence. Available from MIT OpenCourseWare. AITopics/Creativity
Agents & Creativity. By Margaret A. Boden. "Published in the Communications of the Association for Computing Machinery, special issue on Agents (ed. D. Riecken), summer 1994." Artificial Genius. By Margaret A. Boden. Discover Magazine. (October 1996). The full text is available from the magazine's online archive. FIXED: , bgb , 10/17/08 -- http://findarticles.com/p/articles/mi_m1511/is_/ai_18693675?tag=artBody;col1 Precis of "The Creative Mind: Myths and Mechanisms". By Margaret A. Boden. [London: Weidenfeld & Nicolson 1990 (Expanded edn., London: Abacus, 1991.)] "[U]nedited preprint (not a quotable final draft) of: Boden, Margaret A. (1994). Precis of The creative mind: Myths and mechanisms. Behavioral and Brain Sciences 17 (3): 519-570. The final published draft of the target article, commentaries and Author's Response are currently available only in paper." Making Machines Creative. By Roger C. Schank & Chip Cleary. (1995). In: S Smith, T B Ward & R A Finke (eds) The Creative Cognition Approach. MIT Press. 229-247. "For much of the history of AI and cognitive science, creativity was viewed as an esoteric and perhaps somewhat magical process that was above and beyond 'normal' processing. As a result, few researchers have risked tackling it. Instead of being banished to the untouchable heights of cognition, creativity belongs squarely in its center. Far from being esoteric, creativity arises from relatively simple mental processes. Far from being magical, it depends on pre-existing, though complex, mental structures. The creative process is not above and beyond 'normal' reasoning, but rather is central to it." Computational Creativity Workshop at IJCAI-05. "This workshop will bring together researchers from AI, Cognitive Science and other related areas such as Psychology, Philosophy and Arts working on Computational Creativity, providing the opportunity to promote presentation and discussion of ongoing work in the area. The workshop should encourage cross-fertilization between the various approaches, including the study of cognitive and computational models for Creativity, and the application of current AI techniques to the development of Creative Systems. The workshop will provide a forum for identifying trends and opportunities for research on creativity and promising practices concerning the development of creative systems. This workshop is the latest in a growing list of events that have, since 1997, solidified and added rigour to the computational treatment of creative processes (symposia and workshops associated with AISB 00, ICCBR 01, AISB 01, ECAI 02, AISB 02, IJCAI 03, AISB 03, LREC 04, ECCBR 04)." AITopics/CrosswordPuzzles
Thesis: Design and Implementation of Crossword Compilation Programs Using Sequential Approaches. By Sik Cambon Jensen (1997). AITopics/DataMiningTutorial Slides on Statistical Data Mining. Authored by Andrew Moore, CMU. NOT BROKEN: , bgb , 10/17/08 Knowledge-based Scientific Discovery from Geological Databases. By C. Li and G. Biswas. (1995). "It is common knowledge in the oil industry that the typical cost of drilling a new offshore well is in the range of $30 40 million, but the chance of that site being an economic success is 1 in 10. Recent advances in drilling technology and data collection methods have led to oil companies and their ancillaries collecting large amounts of geophysical/geographical data ... Can this vast amount of history from previously explored fields be systematically utilized to evaluate new plays and prospects?" Business Intelligence - The Value in Mining Data. By Jonathan Wu. DM Review (February 2002). "Data mining can best be described as a business intelligence (BI) technology that has various techniques to extract comprehensible, hidden and useful information from a population of data. This BI technology makes it possible to discover hidden trends and patterns in large amounts of data. The output of a data mining exercise can take the form of patterns, trends or rules that are implicit in the data. ... The following are examples of practical uses of data mining and the value it provides those who use this technology to mine their data. ... Fraud Detection ... Inventory Logistics ... Defect Analysis ... Focused Hiring." AITopics/DecisionTreesSimple Tree Searches. By J. Matthews. Available from Generation 5. "This essay will cover very basic tree searches, the depth-first and breadth-first. In artificial intelligence, we use trees to represent a lot of things, from sentence structures, equations and even game states. Often, a method of searching the trees to find a specified goal is required - these algorithms are the simplest methods to do this." http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/BrokenLinks01sep08?action=edit Decision Trees. From MLnet. Explore the various algorithms and methods, and use the cross-reference link to learn about Regression Trees. AITopics/DesignWhat We Know About Learning. By Herbert A. Simon, Department of Psychology, Carnegie Mellon University. (Speech presented at the 1997 Frontiers in Education Conference.) "A characteristic of design that is special to it, besides this gradual emergence of goals, is that the largest task is to generate alternatives. There are lots of theories of decision making, a field that has been heavily cultivated by economists and statisticians. But most theories of decision making start out with a given set of alternatives and then ask how to choose among them. In design, by contrast, most of the time and effort is spent in generating the alternatives, which aren't given at the outset." AITopics/DiscourseAnalysisDiscourse Analysis Tutorial. From Dave Inman, School of Computing, South Bank University, London. Blogging for Dollars - How would you like to survey 20 million consumers in two minutes? By Justin Martin. Forbes Small Business (December 2005). "[T]o know what the masses are saying about your product, you would have to dig through 350,000 fresh daily postings on a staggering 20 million blogs worldwide.... Enter Umbria, a market research firm in Boulder that designs software to find useful consumer intelligence on the Internet. ... Another big challenge is to decipher what's on a blogger's mind. To figure out whether an opinion is strong or tepid, for example, it helps to know that 'awesome' is a stronger endorsement than 'pretty cool,' and that 'shoddy' is less damning than 'abominable.' Umbria has several employees with Ph.D.s in linguistics and artificial intelligence who are forever tweaking the software to make it better at categorizing opinions. Kaushansky claims his software can even identify sarcasm, a useful skill in the prickly blogosphere. ... The software can also estimate the author's age and gender. ... Automation is the source of Umbria's competitive edge: affordability." Local Discourse and Reference. Lecture notes from Bill Wilson, Associate Professor in the Artificial Intelligence Group, School of Computer Science and Engineering, University of NSW. "This section concerns the problem of deciding what phrases (especially noun phrases) refer to. It introduces a simple model of global discourse structure called the history list, and presents an algorithm for referent determination in simple cases." Computational Semantics Laboratory. "We are a research group at the Center for the Study of Language and Information (CSLI) at Stanford University, under the direction of Stanley Peters. We work on a number of projects which involve semantics -- the study of meaning -- at the intersection of linguistics and computer science. A unifying theme in our research is an emphasis on the role of context in determining meaning. We are particularly interested in theoretical models of communication, language, dialogue, computation, and inference which take into account the context in which these activities are occurring."
Mutual Beliefs of Multiple Conversants: A Computational Model of Collaboration in Air Traffic Control. By David G. Novick and Karen Ward, Oregon Graduate Institute of Science & Technology. In Proceedings of the the Eleventh National Conference on Artificial Intelligence, 196 - . Menlo Park, Calif.: AAAI Press. "This work develops a computational model for representing and reasoning about dialogue in terms of the mutuality of belief of the conversants. We simulated cooperative dialogues at the speech act level and compared the simulations with actual dialogues between pilots and air traffic controllers engaged in real tasks. In the simulations, addressees and overhearers formed beliefs and took actions appropriate to their individual roles and contexts. The result is a computational model capable of representing the evolving context of complete real-world multiparty task-oriented conversations in the air traffic control domain." AITopics/DramaGames of infinite possibilities. By Jonathan B. Cox. The News & Observer (January 15, 2003). "R. Michael Young, an assistant professor of computer science at N.C. State University, is working on research that might one day make video games more enjoyable. Young, 41, is studying ways to build artificial intelligence -- the ability of computers to act like humans -- into games so that users get movielike stories. With such technology, for example, a game could adjust to a player's actions and provide a different experience every time it is played. He sat down with Connect's Jonathan B. Cox to discuss his work. ... 'Specifically, the stuff I look at tries to take ideas from conventional AI [artificial intelligence], linguistics, cognitive psychology and ideas about narrative theory and look at computational models of narrative, so that you can take these computational tools that are well founded on the other theories from other disciplines and automatically create stories inside a virtual environment.'" Columbia Newsblaster - an automatic system for event tracking and summarization. Developed by members of the Columbia NLP Group.
Bookish Math - Statistical tests are unraveling knotty literary mysteries. By Erica Klarreich. Science News (December 20, 2003; Vol. 164, No. 25). "Stylometry ['the science of measuring literary style'] is now entering a golden era. In the past 15 years, researchers have developed an arsenal of mathematical tools, from statistical tests to artificial intelligence techniques, for use in determining authorship. ... For decades, computers have supported the work of experts in stylometry. Now, computers are becoming experts in their own right, as some researchers apply artificial intelligence techniques to the question of authorship." AITopics/EarthAndAtmosphericScience
Meteorological Applications of Artificial Intelligence. An extensive collection of links maintained by Bjarne K. Hansen. Cloud Physics and Severe Weather Research Division,Meteorological Research Branch,Meteorological Service of Canada.
Tornado technology improves warning time. By Shannon Womble. Savannah Morning News (August 9, 2000). "Using artificial intelligence, advanced image processing and Doppler radar data, researchers said the Warning Decision Support Systems can give Georgians an extra four to five minutes of warning time. The newer Doppler systems, commonly used by television meteorologists, only provide on average about eight minutes of warning time." AITopics/EmotionA Human Touch for Machines - The radical movement of affective computing is turning the field of artificial intelligence upside down by adding emotion to the equation. By Charels Piller. Los Angeles Times (May 7, 2002). "For the last decade, the UC San Diego psychologist has traveled a quixotic path in search of the next evolutionary leap in computer development: training machines to comprehend the deeply human mystery of what we feel. [Javier ] Movellan's devices now can identify hundreds of ways faces show joy, anger, sadness and other emotions. The computers, which operate by recognizing patterns learned from a multitude of images, eventually will be able to detect millions of expressions. ... Such computers are the beginnings of a radical movement known as 'affective computing.' The goal is to reshape the very notion of machine intelligence. ... Such devices may never replicate human emotional experience. But if their developers are correct, even modest emotional talents would change machines from data-crunching savants into perceptive actors in human society. At stake are multibillion-dollar markets for electronic tutors, robots, advisors and even psychotherapy assistants. ... Classical AI researchers model the mind through the brute force of infinite logical calculations. But they falter at humanity's fundamental motivations. ... Movellan is part of a growing network of scientists working to disprove long-held assumptions that computers are, by nature, logical geniuses but emotional dunces. ... Scientists don't foresee machines with Hal's emotional skills--or, fortunately, its malevolence--soon. But they already have debunked AI orthodoxy considered sacrosanct only five years ago--that logic is the one path to machine intelligence. It took psychologists and neuroscientists--outside the computer priesthood--to see inherent limits in the mathematical pursuit of intelligence that has dominated computer science." Films Such as 'I, Robot' Affirm Human Superiority. Duke News & Communications (July 14, 2004). "'I, Robot,' which opens Friday, revisits one of science fiction's common themes: A creation that develops a will of its own and turns against its creator. But why is that idea so appealing? It speaks to our society's deep fears that, as robots become more apparently human, we discover how machinelike we are, said Priscilla Wald, a Duke University English professor who studies how science is represented in popular culture. ... People feel anxious when they learn how easy it is to program a computer to appear to have emotions. This is possible because we follow predictable patterns, she said. 'Our sense of our uniqueness is threatened by the idea that we are predictable,' she said. 'The farther we go with artificial intelligence and the more human our machines become, the more we understand how machinelike we are. Many people find that deeply disturbing.'" Toward Empathetic Agents in Tutoring Systems. By Jessica Faivre, Roger Nkambou, and Claude Frasson. 2003. In Proceedings of the Sixteenth International Florida Artificial Intelligence Research Society Conference, 161-165. Menlo Park, Calif.: AAAI Press. Abstract: "This paper presents a way of improving computerbased with lifelike presence in learning environment. The approach combines Intelligent Tutoring Systems with research on human emotion in Cognitive Sciences, Psychology and Communication. Considering the relations between emotion, cognition and action in contextual learning, we propose an architecture of a multiagent-based instructional system in which two adaptive emotional agents have been integrated. One manifests the tutor's emotional expressions trough a 3D embodied agent, whereas the second is designed to elicit and analyse the learner's emotional experiences during the interactions with the system. We present here the system's architecture and its first implementation."
Letting your computer know how you feel. By Cliff Saran. ComputerWeekly (June 24, 2003). "Kate Hone, a lecturer in the department of information systems and computing at Brunel University, is the principal investigator in a project that aims to evaluate the potential for emotion-recognition technology to improve the quality of human-computer interaction. ... Affective computing can be defined as 'computing that relates to, arises from, or deliberately influences emotion'. A number of different types of research are encompassed within this term. For instance, some artificial intelligence researchers in the field of affective computing are interested in how emotion contributes to human and, by analogy, computer problem solving or decision making..." Norman, Donald A. 2004. Emotional Design: Why we love (or hate) everyday things.' Basic Books. Reviewed by Charles Arthur: Machines have feelings too - A new book argues that computers should be given emotions. The Independent (November 5, 2003). "Professor [Donald] Norman's thesis is that emotion - that is, gut reaction - is an essential part of our reaction to anything we interact with. Don't dismiss emotion, he argues: it's a useful function that evolution has equipped us with so that we don't have to think about everything. ... But Professor Norman goes rather further than this. He doesn't just consider what makes us react to machines and objects that we use: he takes the thinking forward to pondering the question of whether machines - such as robots and computers - should have emotions. He thinks they should, as does Professor Rosalind Pickard, an artificial intelligence expert. She told him: 'I wasn't sure [machines] had to have emotions until I was writing a paper on how they would respond intelligently to our emotions without having their own. In the course of writing that paper, I realised it would be a heck of a lot easier if we just gave them emotions.'" Picard, Rosalind. W. 1997. Affective Computing. MIT Press. "The latest scientific findings indicate that emotions play an essential role in decision making, perception, learning, and more--that is, they influence the very mechanisms of rational thinking. Not only too much, but too little emotion can impair decision making. According to Rosalind Picard, if we want computers to be genuinely intelligent and to interact naturally with us, we must give computers the ability to recognize, understand, even to have and express emotions." AITopics/Engineering
Intelligence test - Artificial intelligence is unlikely to replace engineers, says Charles Clarke, but knowledge-based tools are already playing an important part in testing and 'genetically engineering' complex design processes. By Charles Clark. Design Engineering - e4engineering.com (February 9, 2004). "Artificial intelligence is not an idea that will go down well with experienced engineers, but there's no doubt that certain software tools can take the tedium out of everyday design work. Knowledge based engineering (KBE) has been with us since the 1980s and large companies such as Boeing, BAE Systems and NASA, along with car giants Jaguar and Lotus Engineering, have bought into it. But it hasn't taken off more widely until recently, because in its traditional form it was prohibitively expensive - around £50,000 a seat just for the software was the norm."
Darwin in a Box - Are you ready for computers that speed up the process of evolution and teach themselves to think? By Steven Johnson. Discover Magazine (August 2003; Vol. 24 No. 8). "The idea is called a genetic algorithm. It creates a random population of potential solutions, then tests each one for success, selecting the best of the batch to pass on their 'genes' to the next generation, including slight mutations to introduce variation. The process is repeated until the program evolves a workable solution. ... Bill Gross and his team of inventors at Idealab in Pasadena, California, are using genetic algorithms to develop a new solar energy device (see 'Catch the Fire,' page 52). Gross believes genetic algorithms have the potential to revolutionize engineering." Smart cars - Knowledge is power...and safety. By Paul Sharke. Mechanical Engineering (March 2003). "A 2003 concept Ford Taurus blends forward collision radar, low light cameras, blind spot monitoring, lane-departure, and rear-collision warnings with telematics. A phone can block incoming calls if pre-crash sensing and navigational data tells the system the driver is too busy to answer."
ANNIE [Artificial Neural Networks In Engineering] Conference, an "international gathering of researchers interested in Smart Engineering System Design using neural networks, fuzzy logic, evolutionary programming, data mining, and artificial life." Advanced Engineering Informatics (Pre-2002 title: Artificial Intelligence in Engineering). Elsevier. "In general, researchers and commercial developers now employ a range of advanced computing techniques including, but not limited to, those originating from AI research. Although some techniques are still useful for automating mundane tasks, many are capable of enhancing the working environment and empowering engineers in ways that have not previously been possible. In all areas that involve knowledge-intensive tasks, a new philosophy that is specifically tailored to computer applications in engineering is revolutionising the field: an 'engineering informatics' is emerging."
NSPE [National Society of Professional Engineers] Ethics Cases Meet Artificial Intelligence. Engineering Times (July 2001). "Over the last several years, researchers at the University of Pittsburgh have been using NSPE's Board of Ethical Review cases in a rather unique way. Researchers Bruce McLaren and Kevin Ashley are not using the cases to directly teach students about engineering ethics or moral reasoning. Rather, they have been using the cases in research aimed at advancing artificial intelligence. ... [T]heir research attempts to build computational models of the reasoning process with cases and examples in domains such as law and practical ethics. The research aims to develop computational models that facilitate retrieval of relevant information and are used in tutoring systems that help students learn to reason by using cases." Applications of Artificial Intelligence in Engineering XIII. Editors: G. Rzevski, Brunel University, UK, R.A. ADEY, Wessex Institute of Technology, UK and P. NOLAN, National University of Ireland, Galway. http://www.aaai.org/Library/Magazine/vol21.php#Winter AITopics/FilteringEspion carving niches in e-mail security. By Ted griggs. The Advocate & WBRZ News 2 (June 9, 2006). "Espion International, a local e-mail security firm, hopes to make its reputation by providing the health-care industry with something the federal government now demands: privacy and protection for patient information. ... Espion International’s artificial intelligence algorithm, which performs many of the functions that normally require a person, monitors incoming and outgoing e-mail. The program detects anything considered personal information, from Social Security numbers to insurance policy numbers, then checks to see if those messages also contain medical information. ... Companies and consumers will spend close to $4 billion this year on anti-spam, anti-virus and e-mail security, and the amount is growing by around 25 percent per year, according to the Ferris Group, a San Francisco-based market and technology research firm." Information Filtering Resources. From the Information Filtering Project at the University of Maryland. "This page lists all known internet-accessible information filtering resources." SurfControl - Beyond Business interview on CNNFN's Money & Markets television broadcast (October 2, 2002 at 5PM EST). Cable News Network's CNNFN. "Francis: The current corporate crime wave and the central role of e-mail as evidence has companies clamoring for more sophisticated technology to identify all kinds of messages now. ... Hays: Joining us now with an inside look is Steve Purdham, CEO of SurfControl, a company that makes e-mail filtering technology. ... Francis: Now you're now employing a technology we call Neural Network technology, often used by credit card companies to spot patterns of fraud that you might not see with a naked eye. How does that work when it comes to e-mail? It's more than just looking for a dirty word here or a racial epithet there? Purdham: Yes. Absolutely. Neural Networks is a new type of technology that helps us be able to look at the fingerprint in an e-mail, looking for the - for example, if you're looking for the word 'breast', that doesn't actually say it's a sex site, it could be a medical site or it could be a medical e-mail or it could be a recipe, for example. So you have to look at things in context. An artificial intelligence, neural networks actually allows you to build a fingerprint so that the fact that the word 'breast' appears doesn't mean there's a bad e-mail. It means it actually could be a risk and therefore you go down the part of the chain." AITopics/Fraud
Inspired by immunity - By developing programs that mimic some of the functions of the immune system, computer scientists are tackling problems from fighting fraud to controlling robots. By Erica Klarreich. Nature 415, 468 - 470 (January 31, 2002). NASD Regulation's Advanced Detection System Wins Awards From Smithsonian, Data Warehousing Institute, and Artificial Intelligence Association. Press release from the National Association of Securities Dealers, Inc. (NASD¨). September 15, 1998. "ASD Regulation, Inc. announced today that its Advanced Detection System (ADS) has recently won three prestigious awards in recognition of its superior technology and ability to help deter fraud and guard marketplace integrity. ... ADS is a market surveillance, data mining, and fraud/violative behavior detection software package that monitors Nasdaq¨ for potential late-trade reporting, market integrity, and best execution violations. The system combines data visualization, time sequence pattern matching, rule-pattern matching, and data mining in a single application that looks for patterns or practices of potentially violative behavior." Signs of Fraud Go Beyond Signature - Credit Card Companies Use Artificial Intelligence to Thwart Thieves. By Margaret Webb Pressler. The Washington Post (July 21, 2002; Page H05). "As it turns out, however, credit card companies no longer rely on retail clerks to catch the crooks. ... 'We're at a level whereby we can understand with artificial intelligence . . . the potentially fraudulent transactions,' said Raf Sorrentino, vice president of risk management for First Data Corp., one of the country's biggest providers of credit card processing and payment services. Credit card fraud costs the industry about a billion dollars a year, or 7 cents out of every $100 dollars spent on plastic. But that is down significantly from its peak about a decade ago, Sorrentino says, in large part because of the powerful technology that can recognize unusual spending patterns." AI Technologies to Defeat Identity Theft Vulnerabilities. By Latanya Sweeney, The Laboratory for International Data Privacy (also known as the "Data Privacy Lab") at Carnegie Mellon University. AAAI Spring Symposium, AI Technologies for Homeland Security, 2005. VISA EU Launches New Advanced Fraud Detection Tool. System to deliver significant increase in fraud detection rates. VISA EU press release (December 29, 2003). "Visa Intelligent Scoring of Risk (VISOR) is an advanced neural network system that scrutinises card transactions to deliver a highly accurate risk score by analysing the spending behaviour of each cardholder along with the profile of each merchant."
AITopics/FuzzyLogic
International Fuzzy Systems Association. "The International Fuzzy Systems Association (IFSA) is a worldwide organization dedicated to the support, development and promotion of the fundamental issues of fuzzy theory related to (a) sets, (b) logics, (c) relations, (d) natural languages, (e) concept formation, (f) linguistic modeling, (g) vagueness, (h) information granularity, etc. and their applications to (1) systems modeling, (2) system analysis, (3) diagnosis, (4) prediction and (5) control in decision support systems in management and administration of human organizations as well as electro-mechanical systems in manufacturing and process industries." AITopics/GeneticAlgorithms
The Hitch-Hiker's Guide to Evolutionary Computation: A list of Frequently Asked Questions (FAQ). Jörg Heitkötter and David Beasley, eds. (2000). Questions include What's a Genetic Algorithm (GA)? and What's Genetic Programming (GP)? Genetic Algorithm Optimizer. From the Artificial Intelligence Lab at the University of Arizona. Marshall Ramsey's introductory demo allows you to view the graphical version of the genetic algorithm described in the text. An Introduction to Genetic Algorithms In Java. By Michael Lacy. Java Developer's Journal (Vol. 6, Issue 3; March 1, 2001). "In the January issue of JDJ (Vol. 6, issue 1) I introduced a technique born in the AI community that uses concepts from biological natural selection to solve complex and often highly nonlinear optimization problems encountered in computer science - the genetic algorithm. I examined the building blocks of genetic algorithms and why java is well suited to their implementation. This month I'll discuss the details of a simple genetic algorithm implementation in the hopes that your curiosity will be sparked to pursue further investigation." A Fast TSP solver Using A Genetic Algorithm. In this TSP demo, you can place the cities on the map! From The University of Texas at Arlington's Department of Computer Science and Engineering. GA Archives. "The Genetic Algorithms Archive is a repository for information related to research in genetic algorithms and other forms of evolutionary computation. Available from this site are past issues of the GA-List digest, source code for many GA implementations, and announcements about GA-related conferences. Also, links are given to many interesting sites around the World with material related to evolutionary computation. This archive is maintained by Alan C. Schultz at The Navy Center for Applied Research in Artificial Intelligence." IlliGAL: The Illinois Genetic Algorithms Laboratory. "[W]e study nature's search algorithm of choice, genetics and evolution, as a practical approach to solving difficult problems on a computer." Important Genetic Algorithm Information Sites from the Los Alamos Genetic Algorithms Niche. Maintained by Hillol Kargupta. "NaturalMotion's Active Character Technology (A.C.T.) is based on Oxford University's research on the control of human and animal body motions. In essence, we build a physical, biomechanically-realistic model of a character (e.g. a human or a dinosaur), implant an appropriate brain structure (usually a neural network), and use optimisation techniques (such as artificial evolution) to create the desired behaviour."
AITopics/Go
All Fired Up About Intelligent Detectors. "Thanks to developments in smoke and fire detection technologies, buildings are becoming increasingly automated. Artificial intelligence is a key factor." From Siemens AG. Heading for Disasters. By Barbara Forster. Computerworld (September 4, 2000). "The next generation of search-and-rescue workers looking for victims in buildings that collapsed in earthquakes or explosions will slither through eight-inch ducts, navigate dark, rubble-strewn corridors and be tossed into third-floor windows. They will be impervious to pain, fire and water. They will be robots - autonomous and mobile. Industrial robots have been used successfully for decades, but their descendants will be a relatively new breed with sufficient intelligence to carry out complex tasks, including planning and decision-making in unstructured and dynamic environments where missions are time-critical." Search-Rescue Robots Test Their Mettle in Tournaments - Researchers Aim to Improve Vehicles' Skills for Real-Life Use. By Guy Gugliotta. Washington Post TechNews (May 30, 2003). "Ten years ago, no one had tried to use robots for search and rescue, but by 2001 researchers had enough expertise to deploy robotic vehicles with some success to search through rubble at the World Trade Center and the damaged buildings around it. Now robots compete annually in two international search-and-rescue tournaments, measuring their progress in diabolically difficult arenas designed by the National Institute of Standards and Technology (NIST). RoboCup Rescue: A Grand Challenge for Multiagent and Intelligent Systems. By Hiroaki Kitano and Satoshi Tadokoro. AI Magazine 22(1): Spring 2001, 39-52. "In this article, we present a detailed analysis of the task domain and elucidate characteristics necessary for multiagent and intelligent systems for this domain. Then, we present an overview of the RoboCup Rescue project." CHEMREG: Using Case-Based Reasoning to Support Health and Safety Compliance in the Chemical Industry. By Kirk D. Wilson. AI Magazine, 19(1): Spring 1998, 47-58. "CHEMREG is a large knowledge-based system used by Air Products and Chemicals, Inc., to support compliance with regulatory requirements for communicating health and safety information in the shipping and handling of chemical products. This article concentrates on one of the knowledge bases in this system: the case-based reasoner. The case-based reasoner addresses the issue of how proper communication of public health and safety information can be ensured while rapid and cost-effective product evaluation is allowed in the absence of actual hazard testing of the product. CHEMREG generates estimates of hazard data for new products from similar products using an existing relational database as a case library." Biomedical Security Institute. "BMSI has a two-pronged approach: 1.) Development of a prototype computer-based surveillance, analysis and communication systems infrastructure to provide early warning of naturally occurring disease outbreaks and terrorist attacks employing biological pathogens. It will perform continuing real-time data mining and analysis of selected data streams (such as electronic medical records and microbiology laboratory results) for sentinel events or situations. ... 2.) A research program that integrates the latest research and technology in public health, biomedical sciences and biomedical informatics, including: ... Intelligent systems, data mining, artificial intelligence, bioinformatics and early detection/early warning computer-based surveillance systems...." Also see:
CREATE, the Center for Risk and Economic Analysis of Terrorism Events, "is an interdisciplinary national research center based at the University of Southern California and funded by the Department of Homeland Security. The Center is focused on risk and economic analysis of the U.S. and comprises a team of experts from across the country, including partnerships with New York University and the University of Wisconsin at Madison."
Fetch II - Counter Mine Intelligence. From iRobot. "The Fetch II robots perform their tasks autonomously but with the supervision of a single operator. Behavior Based intelligence in each Fetch II enables it to navigate through real world terrain autonomously, using a relative coordinate positioning system and task-specific sensors mounted on a robust mobility platform. The Behavior Based software mediates robot-robot interference within the swarm and supports mutual cooperation among them."
UrbanSearch and Rescue at the Perceptual Robotics Laboratory, University of South Florida. Lots of exciting information awaits you at this site including their collection of FAQs where you'll find answers to questions such as "What tasks can robots do in USAR?" AITopics/ImageUnderstanding
Finding people and animals using body plans and action plans. David Forsyth and Margaret Fleck, The Computer Science Division, University of California, Berkeley. "A body plan is a sophisticated model of the way jointed objects are put together; at present, we have two programs that can find objects using body plans. One program can tell, quite accurately, whether a picture contains a naked person or not. Another program can tell, again quite accurately, whether a picture contains a horse. "
An Intelligent Framework for Image Understanding. By Ahmed E. Ibrahim. "The goal of image understanding system often involves the identification of objects in images and the establishment of the relationships among the objects. This transformation of signals (the images) to symbols (the interpretation) in the visual domain is one of the most important and most difficult task in artificial intelligence."
Be sure to check out their video demos! Analysis and Recognition of Walking Movements. James Davis and Stephanie Taylor. International Conference on Pattern Recognition, Quebec City, Canada, August 11-15, 2002, pp. 315-318. AITopics/InductionInductive Learning Techniques: definition and example. From the MLnet Online Information Service. "In inductive learning, knowledge is compiled by generalising patterns in factual experience. ..." AITopics/InformationRetrievalAndExtractionInformation Service Agent Research. "The Information Service Agent Lab at Simon Fraser University develops novel techniques for interactive information gathering and integration. The research applies artificial intelligence planning and learning techniques and database technologies to create knowledge bases from large collections of dynamically changing, potentially inconsistent and heterogeneous data sources, permitting users access to information at the right abstraction level." Projects. Software Agents Group, MIT Media Lab. Wide-ranging approaches to information retrieval that include user profiling, information filtering, privacy, recommender systems, communityware, negotiation mechanisms and coordination.
Is There an Intelligent Agent in Your Future? By James A. Hendler (1999). (This wonderful paper received the AAAI-2000 Effective Expository Writing Award.) Savvysearch... By Adele Howe, and Daniel Dreilinger (1997). AI Magazine 18 (2): 19-25. Description of a metasearch engine that learns which search engines to query. In Search of a Lost Melody - Computer assisted music: identification and retrieval. By Kjell Lemstrom. Finnish Music Quarterly Magazine 3-4/2000. The Revolution in Legal Information Retrieval or: The Empire Strikes Back. By Erich Schweighofer (1999). The Journal of Information, Law and Technology 1999(1). "The issue is how to deal with the Artificial Intelligence (AI)-hard problem of making sense of the mass of legal information." Text Mining Technology - Turning Information Into Knowledge. A white paper from IBM (1998), Daniel Tkach, editor.
HP SpeechBot - audio search using speech recognition. From Hewlett-Packard.
MARVEL: "The Intelligent Information Management Department at IBM Research is developing a multimedia analysis and retrieval system called MARVEL. MARVEL helps organize the large and growing amounts of multimedia data (e.g., video, images, audio) by using machine learning techniques to automatically label its content. The system recently won the Wall Street Journal 2004 Innovation Award in the multimedia category." A demo is available. "NewsInEssence is a system for finding and summarizing clusters of related news articles from multiple sources on the Web. It is under development by the CLAIR group at the University of Michigan." You can see it in action here. SOPHIA Search Ltd: "established to exploit exciting new technology conceived and developed at the University of Ulster, Northern Ireland in partnership with St. Petersburg State University, Russia ." As stated on their How SOPHIA Searches page: "By automatically discovering themes present in the collection and breaking them down into topics SOPHIA creates intuitive groupings (clusters) of semantically related content and presents these to users as the result of a search." AITopics/IntelligentTutoringSystemsArtificial intelligence alive and well . The University of Auckland News (January 19, 2005). "While statistics students at The University of Auckland are taking a break from studies for summer, their new 'teacher' can’t wait for the new semester to begin. Maria, an assistant teacher in Statistical Interference, is an unusual individual. She looks to be in her mid-twenties but her age, she says, cannot be computed in human years. With a vocabulary of 203,000 words, a repertoire of 106,000 grammatical rules and 118,000 rules of logical inference, Maria is capable of conversation at quite a complex level. Maria is a robot, or artificial intelligence entity, created over two years of intense work and study by Shahin Maghsoudi, a PhD student and member of the Artificial Intelligence Group in the Faculty of Science. As part of his Masters degree in Computer Science, Shahin embarked on a project to create virtual robots which could be used as teaching assistants, helpdesk operators and web-based marketing assistants." Intelligent Tutoring Systems. A brief introduction by Eric Thomas. Part of San Diego State University's Encyclopedia of Educational Technology. The Roles of Artificial Intelligence in Education: Current Progress and Future Prospects. David McArthur, Matthew Lewis, and Miriam Bishay. (1993) RAND DRU-472-NSF. A very good overview with lots of basic information about intelligent tutoring systems. A teacher who gets by on artificial intelligence" (International Herald Tribune and Israeli Haaretz Daily, 12/20/98) and "Intelligent agents help humans learn from computers" (CNN Interactive, 8/25/97) are just two of the exciting articles about Pedagogical Agents and Guidebots that you'll find at CARTE's very informative site. [CARTE = The Center for Advanced Research in Technology for Education which is part of the Information Sciences Institute at the University of Southern California.] Be sure that you don't miss the demos and videos, or their many pedagogical agents and guidebots (see: research and projects). Talking Up a Good Game - Computer Simulation to Stimulate Soldiers to Speak in Tongues. By Paul Eng. ABCNEWS.com (March 9, 2004). "The first part of the game, says [Lewis] Johnson, acts as basically an 'intelligent tutoring' program.' ... But what makes the program really 'intelligent' are the computer-generated and -controlled characters, such as a virtual village leader and a virtual 'team member' that acts as an in-game guide. These game characters are programmed to react in ways that are unique to each individual user." Encouraging Student Reflection and Articulation using a Learning Companion . By Bradley Goodman, Amy Soller, Frank Linton, and Robert Gaimari (1998). International Journal of Artificial Intelligence in Education, 9(3-4). "The goal of the research presented in this paper is to promote more effective instructional exchanges between a student and an intelligent tutoring system The approach taken to meet this goal involves providing a simulated peer as a partner for the student in learning and problem solving. The learning companion described in this paper enhances learning by initiating a dialogue with a student forcing reflection and articulation on the student's learning."
CIRCLE: Center for Interdisciplinary Research on Constructive Learning Environments. "CIRCLE is an NSF-funded research center located at the University of Pittsburgh and Carnegie Mellon University, with multiple partnerships among schools, industries and other research institutions. CIRCLE's mission is to determine why highly effective forms of instruction, such as human one-on-one tutoring, work so well, and to develop computer-based constructive learning environments that foster equally impressive learning." Be sure to follow the links to "Projects" for that's where you'll find systems such as: The EPSILON [Encouraging Positive Social Interaction while Learning ON-Line] Project at the Learning Research and Development Center, University of Pittsburgh "is an interdisciplinary effort to provide dynamic, adaptive support for on-line learning communities. The support, in the form of an intelligent software agent, will focus on helping students improve their social and communication management skills. ... The EPSILON software will be driven by a computational model of effective learning interaction. The project will explore methods for dynamically analyzing on-line interaction during structured learning activities. Artificial Intelligence techniques will be employed for analyzing, studying, and characterizing on-line interaction." The project consortium members are: Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, European Research and Project Office GmbH, The University of Edinburgh, Technische Universiteit Eindhoven, Mathematisches Institut, University of Glasgow, Universidad de Malaga , Ernst Klett Verlag, and Universität des Saarlandes. Pedagogical Agents and Learning Systems (PALS) Research Group members at the Center for Research of Innovative Technologies for Learning (RITL), Florida State University, "investigate the affordances and constraints of animated pedagogical agents within eLearning environments.What is a 'pedagogical agent?' (As stated by W. Lewis Johnson): Pedagogical agents are autonomous agents that support human learning, by interacting with students in the context of interactive learning environments. They extend and improve upon previous work on intelligent tutoring systems in a number of ways. ..."
Murray, Tom. 1999. Authoring Intelligent Tutoring Systems: An analysis of the state of the art. International Journal of Artificial Intelligence in Education (1999), 10: 98-129. "This paper consists of an in-depth summary and analysis of the research and development state of the art for intelligent tutoring system (ITS) authoring systems. A seven-part categorization of two dozen authoring systems is given, followed by a characterization of the authoring tools and the types of ITSs that are built for each category. An overview of the knowledge acquisition and authoring techniques used in these systems is given. A characterization of the design tradeoffs involved in building an ITS authoring system is given. Next the pragmatic questions of real use, productivity findings, and evaluation are discussed. Finally, I summarize the major unknowns and bottlenecks to having widespread use of ITS authoring tools." AITopics/Interviews
Bruce Buchanan. Interviewed by John Aronis for Links, the newsletter of The Department of Computer Science at the University of Pittsburgh (Spring 2003; pages 2 - 4)." While working in the Stanford Artificial Intelligence Laboratory, Bruce and his collaborators made important contributions to artificial intelligence. Their assertion -- obvious in retrospect like most great ideas -- was that knowledge is important for intelligent behavior. They drove this point home with a series of programs that embodied the knowledge of scientific and medical experts -- sometimes rivaling or surpassing their abilities -- and the creation of an industry centered around expert systems." Henrik Christensen. Man and machine - Scholar envisions new devices to help extend the reach of the human race --- tirelessly. By Bill Husted. The Atlanta Journal-Constitution & ajc.com (April 23, 2006). "Once a week, a robot handles the vacuuming for Henrik Christensen. It does a good job but --- like a lot of housekeepers --- sometimes misses dirt in the corners. Christensen occupies the newly created KUKA Chair of Robotics at Georgia Tech's College of Computing. And he practices what he preaches with the robotic housekeeper. ... Q: Is there a place for that stereotypical robot, too? The machines that work around the house? ... Q: One of the public fears, when it comes to robots, is that they'll eventually cost people jobs. Do you think that's a rational fear? ... Q: What are the new developments in robotics? ..."
Michael Hawley. In His Own Words. A scientist at MIT's Media Lab reveals the true nature of a college of arts and sciences As told to Calvin Fussman. Discover Magazine ( September 2003; Vol. 24, No. 9). "I'm kind of a perfect mix of my parents. My dad was an electrical engineer at Bell Laboratories in Murray Hill, New Jersey. ... My mom was into English literature and music. I was really lucky to have the yin and the yang. .... I wound up at MIT as a protégé |
