Artificial Intelligence Has Enjoyed Tre- List of Offshoot Technologies, from Time Mendous Success Over the Last Twenty five Sharing to Functional Programming
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Gigabit Telerobotics: Applying Advanced Information Infrastructure
University of Pennsylvania ScholarlyCommons Technical Reports (CIS) Department of Computer & Information Science January 1993 Gigabit Telerobotics: Applying Advanced Information Infrastructure Ruzena Bajcsy University of Pennsylvania David J. Farber University of Pennsylvania Richard P. Paul University of Pennsylvania Jonathan M. Smith University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/cis_reports Recommended Citation Ruzena Bajcsy, David J. Farber, Richard P. Paul, and Jonathan M. Smith, "Gigabit Telerobotics: Applying Advanced Information Infrastructure", . January 1993. University of Pennsylvania Department of Computer and Information Science Technical Report No. MS-CIS-93-11. This paper is posted at ScholarlyCommons. https://repository.upenn.edu/cis_reports/422 For more information, please contact [email protected]. Gigabit Telerobotics: Applying Advanced Information Infrastructure Abstract Advanced manufacturing concepts such as "Virtual Factories" use an information infrastructure to tie together changing groups of specialized facilities into agile manufacturing systems. A necessary element of such systems is the ability to teleoperate machines, for example telerobotic systems with full- capability sensory feedback loops. We have identified three network advances needed for splitting robotic control from robotic function: increased bandwidth, decreased error rates, and support for isochronous traffic. Theseeatur f es are available in the Gigabit networks under -
Object Recognition Using Vision and Touch
OBJECT RECOGNITION USING VISION AND TOUCH Peter Allen and Ruzena Bajcsy Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104 number of false recognitions. However, the model primitives must ABSTRACT be computable from the sensed data. More complex object models A system is described that integrates vision and are being built [8] but they are of limited power unless the sensing tactile sensing in a robotics environment to perform systems can uncover the structural primitives and relationships they object recognition tasks. It uses multiple sensor systems contain. The approach used here allows complex and rich models (active touch and passive stereo vision) to compute of objects that extends the kinds of generic objects that can be three dimensional primitives that can be matched recognized by the system. This is due to the rich nature of the sur• against a model data base of complex curved surface face and feature primitives the sensors compute. Surfaces are the objects containing holes and cavities. The low level actual parts of an object that we see; the primitive computed is sensing elements provide local surface and feature exactly this. Holes and cavities are important visual and tactile matches which arc constrained by relational criteria features; these are also computed by integrating touch and vision. embedded in the models. Once a model has been Further, the system described here is viewpoint independent. The invoked, a verification procedure establishes confidence problems caused by visual occlusion are overcome by the ability to measures for a correct recognition. The three dimen* use active touch sensing in visually occluded areas. -
Active Perception
Active Perception RUZENA BAJCSY, MEMBER, IEEE Invited Paper Active Perception (Active Vision specifically) is defined as a study II. WHATIS ACTIVESENSING? of Modeling and Control strategies for perception. By modeling we mean models of sensors, processing modules and their interac- In the robotics and computer vision literature, the term tion. We distinguish local models from global models by their “active sensor” generally refers to a sensor that transmits extent of application in space and time. The local models repre- (generally electromagnetic radiation, e.g., radar, sonar, sent procedures and parameters such as optical distortions of the lens, focal lens, spatial resolution, band-pass filter, etc. The global ultrasound, microwaves and collimated light) into the envi- models on the other hand characterize the overall performance ronment and receives and measures the reflected signals. and make predictions on how the individual modules interact. The We believe that the use of active sensors is not a necessary control strategies are formulated as a search of such sequence of condition on active sensing, and that sensing can be per- steps that would minimize a loss function while one is seeking the formed with passive sensors (that only receive, and do not most information. Examples are shown as the existence proof of the proposed theory on obtaining range from focus and sterolver- emit, information), employed actively. Here we use the term gence on 2-0 segmentation of an image and 3-0 shape parametri- active not to denote a time-of-flight sensor, but to denote za tion. a passive sensor employed in an active fashion, purpose- fully changing the sensor’s state parameters according to sensing strategies. -
Toward Memory-Based Reasoning
SPECIAL ISSUE Articles TOWARD MEMORY-BASED REASONING The intensive use of memory to recall specific episodes from the past rather than rules-should be the foundation of machine reasoning. CRAIG STANFILL and DAVID WALTZ The traditional assumption in artificial intelligence reasoning warrants further experimentation. Two (AI) is that most expert knowledge ·is encoded in the bodies of evidence have led us to support this form of rules. We consider the phenomenon of rea hypothesis. The first is the study of cognition: It is soning from memories of specific episodes, however, difficult to conceive of thought without memory. to be the foundation of an intelligent system, rather The second is the inability of AI to achieve success than an adj unct to some other reasoning method. in any broad domain or to effectively capture the This theory contrasts with much of the current notion of "common sense," much of which, we be work in similarity-based learning, which tacitly lieve, is based on essentially undigested memories of assumes that learning is equivalent to the automatic past experience. generation of rules, and differs from work on We will primarily discuss the application of our "explanation-based" and "case-based" reasoning in hypothesis to the limited case of similarity-based in that it does not depend on having a strong domain duction. The goal of similarity-based induction is to model. make decisions by looking for patterns in data. This With the development of new parallel architec .approach has been studied extensively in the con tures, specifically the Connection Machine@ system, text of "similarity-based learning," which uses ob the operations necessary to implement this approach served patterns in the data to create classification . -
Winter and Spring), and T C
COMPUTER SCIENCE DEPARTMENT REPORT TO COMPUTER SCIENCE ADVISORY COMMITTEE October 1973 FACULTY AND STAFF Don Knuth has returned from a year's leave spent in Norway. George Dantzig is on sabbatical leave for the current academic year. Roger Schank is on leave and Harold Stone is here but on research leave for the academic year. John T. Gill of the Electrical Engineering Department has been named to the list of affiliated faculty. Two of our faculty received distinguished awards in the past year Dantzig was awarded the Honorary Degree of Doctor of Science by Technion- Israel Institute of Technology. Knuth was made a Fellow of the American Academy of Science. Visiting faculty members for the current year are Peter Franaszek, Terry Winograd, C.A.R. Hoare (Autumn quarter), Joseph Oliger (Winter quarter), Robert M. Keller (Winter and Spring), and T C . Hu (Spring quarter). The department's resources are augmented by about 19 professional people who are research associates, research computer scientists, etc. Also several professionals from the local area serve as lecturers helping with the teaching program of the department. OTHER VISITORS In addition to the visiting faculty listed above, there are a number of other visitors who have come here to participate in the research acti- vities of the department. Among these are Mario and Luigia Aiello from Italy, Gheorghe Dodescu from Romania, Geoffrey Dromey from Australia, lan Duff from England, Kjell Overholt from Norway and F. yon Henke from Germany. STUDENTS AND ENROLLMENTS We have approximately 115 graduate student majors in our Department but there is no undergraduate major. -
A Framework for Representing Knowledge Marvin Minsky MIT-AI Laboratory Memo 306, June, 1974. Reprinted in the Psychology of Comp
A Framework for Representing Knowledge Marvin Minsky MIT-AI Laboratory Memo 306, June, 1974. Reprinted in The Psychology of Computer Vision, P. Winston (Ed.), McGraw-Hill, 1975. Shorter versions in J. Haugeland, Ed., Mind Design, MIT Press, 1981, and in Cognitive Science, Collins, Allan and Edward E. Smith (eds.) Morgan-Kaufmann, 1992 ISBN 55860-013-2] FRAMES It seems to me that the ingredients of most theories both in Artificial Intelligence and in Psychology have been on the whole too minute, local, and unstructured to account–either practically or phenomenologically–for the effectiveness of common-sense thought. The "chunks" of reasoning, language, memory, and "perception" ought to be larger and more structured; their factual and procedural contents must be more intimately connected in order to explain the apparent power and speed of mental activities. Similar feelings seem to be emerging in several centers working on theories of intelligence. They take one form in the proposal of Papert and myself (1972) to sub-structure knowledge into "micro-worlds"; another form in the "Problem-spaces" of Newell and Simon (1972); and yet another in new, large structures that theorists like Schank (1974), Abelson (1974), and Norman (1972) assign to linguistic objects. I see all these as moving away from the traditional attempts both by behavioristic psychologists and by logic-oriented students of Artificial Intelligence in trying to represent knowledge as collections of separate, simple fragments. I try here to bring together several of these issues by pretending to have a unified, coherent theory. The paper raises more questions than it answers, and I have tried to note the theory's deficiencies. -
Origins of the American Association for Artificial Intelligence
AI Magazine Volume 26 Number 4 (2006)(2005) (© AAAI) 25th Anniversary Issue The Origins of the American Association for Artificial Intelligence (AAAI) Raj Reddy ■ This article provides a historical background on how AAAI came into existence. It provides a ratio- nale for why we needed our own society. It pro- vides a list of the founding members of the com- munity that came together to establish AAAI. Starting a new society comes with a whole range of issues and problems: What will it be called? How will it be financed? Who will run the society? What kind of activities will it engage in? and so on. This article provides a brief description of the consider- ations that went into making the final choices. It also provides a description of the historic first AAAI conference and the people that made it happen. The Background and the Context hile the 1950s and 1960s were an ac- tive period for research in AI, there Wwere no organized mechanisms for the members of the community to get together and share ideas and accomplishments. By the early 1960s there were several active research groups in AI, including those at Carnegie Mel- lon University (CMU), the Massachusetts Insti- tute of Technology (MIT), Stanford University, Stanford Research Institute (later SRI Interna- tional), and a little later the University of Southern California Information Sciences Insti- tute (USC-ISI). My own involvement in AI began in 1963, when I joined Stanford as a graduate student working with John McCarthy. After completing my Ph.D. in 1966, I joined the faculty at Stan- ford as an assistant professor and stayed there until 1969 when I left to join Allen Newell and Herb Simon at Carnegie Mellon University Raj Reddy. -
(Autumn Q. .Ter), Josep! Iger ■ La Robert M
1 COMPUTER SCIENC3 Di P-RTMEN. TO COMPUTER SCjPiMCE ADV ...PRY CO- ' -tooer 197? pPuiflY AND STAFF Don Knuth has returned from a year's le "way. G- ; ritzig is on sabbatical, leave for che current aco . /-ear. Roger Sc'J Ls on leave and Harold Stone is here but or. reseax . t^ave for ;he acadein year. John T. Gill of the Electrical Engineering I tment has been nam* 1 1 the list of affiliated faculty. Two of our faculty received distinguished a .-" "■"': h 3t yes Dantzig was awarded the Honorary Degree of Doctor i/, lechnion- ] srael Institute of Technology. Knuth was made a ."' . th Ameri< '■ :demy of Science. Visiting faculty members for the current yeaj ?■ isz. ['erry Winograd, C.A.R. Hoare (Autumn q. .ter), Josep! Iger ■ La Robert M. Keller (Winter and Spring;, and T C. IP 9 lg .uarterp The department's resources are augmented by p.rofe3^.ior >eople 'ho are r -search associates, research commute., scientists, etc. Also several professionals fr..-- the local area serve as curers helj ./ith the teaching program of ti i 'partment. OTHER VISITORS In addition to the visiting faculty listed ..re re a : ■Z other visitors who have come here to participate ses. .. Ac tes of the department. Among these are Mario ar. Aie: L; fr, ■rghe Dodescu from Romania, Geoffrey Dromey from ] Dv: ( ■ il'ml, i'.,i'-ll 'v -riu IM. iVi'iu Norway aiP ("' "■-a FT< i-j ■ . i>. ■■■i'lNlS AND ENROL! PENIS We have approximately 115 graduc p- student m; , separtm< .. there is no undergraduate major. -
IJCAI Organization
IJCAI Organization Trustees Former Trustees Serving on the President: Francesca Rossi (University of Padova, Italy) Executive Committee Craig Knoblock (University of Southern California, USA) Hiroaki Kitano (Sony Computer Science Laboratories, Inc., Japan) Sebastian run (Stanford University, USA) Ramon López de Mántaras (Spanish Scientific Research Council, Spain) Michael Wooldridge (University of Oxford, UK) Craig Boutilier (University of Toronto, Canada) Qiang Yang (Hong Kong University of Science and Technology, Fausto Giunchiglia (University of Trento, Italy) Hong Kong) Anthony G. Cohn (University of Leeds, UK) Gerhard Brewka (University of Leipzig, Germany) Hector Levesque (University of Toronto, Canada) Subbarao Kambhampati (Arizona State University, USA) Luigia Carlucci Aiello (Universita' di Roma, Italy) Toby Walsh (University of New South Wales, Australia) Michael P. Georgeff (Georgeff International Inc, Australia) Fahiem Bacchus (University of Toronto, Canada) C. Raymond Perrault (SRI International, USA) Carles Sierra (Artificial Intelligence Research Institute of the Spanish Wolfgang Wahlster (German Research Center for AI, Germany) Research Council, Spain) Barbara J. Grosz (Harvard University, USA) Trustees Elect Wolfgang Bibel (Darmstadt University of Technology, Germany) Alan Bundy (University of Edinburgh, UK) Jeffrey Rosenschein (e Hebrew University of Jerusalem, Israel) Alan Mackworth (University of British Columbia, Canada) Jérôme Lang (University Paris Dauphine, France) Patrick J. Hayes (IHMC-UWF, USA) D. Raj Reddy (Carnegie -
Chapter 1 of Artificial Intelligence: a Modern Approach
1 INTRODUCTION In which we try to explain why we consider artificial intelligence to be a subject most worthy of study, and in which we try to decide what exactly it is, this being a good thing to decide before embarking. INTELLIGENCE We call ourselves Homo sapiens—man the wise—because our intelligence is so important to us. For thousands of years, we have tried to understand how we think; that is, how a mere handful of matter can perceive, understand, predict, and manipulate a world far larger and ARTIFICIAL INTELLIGENCE more complicated than itself. The field of artificial intelligence, or AI, goes further still: it attempts not just to understand but also to build intelligent entities. AI is one of the newest fields in science and engineering. Work started in earnest soon after World War II, and the name itself was coined in 1956. Along with molecular biology, AI is regularly cited as the “field I would most like to be in” by scientists in other disciplines. A student in physics might reasonably feel that all the good ideas have already been taken by Galileo, Newton, Einstein, and the rest. AI, on the other hand, still has openings for several full-time Einsteins and Edisons. AI currently encompasses a huge variety of subfields, ranging from the general (learning and perception) to the specific, such as playing chess, proving mathematical theorems, writing poetry, driving a car on a crowded street, and diagnosing diseases. AI is relevant to any intellectual task; it is truly a universal field. 1.1 WHAT IS AI? We have claimed that AI is exciting, but we have not said what it is. -
Robots Being Exhibited.No Link 1
Association for the Advancement of Artificial Intelligence 445 Burgess Drive Menlo Park, CA 94025 (650) 328-3123 www.aaai.org For press inquiries only, contact: Sara Hedberg (425) 444-7272 2008 Robot Workshop and Exhibition Backgrounder July 14-16, 2008 Hyatt Regency McCormick Place, Chicago, Illinois Second Level, Hyatt Regency Conference Center, CC23 In conjunction with the AAAI-08 Conference This year AAAI is sponsoring two Robot Workshops and Exhibitions – one on Robots and Creativity (pages 1-9) and one on Mobility and Manipulation (pages 10-14). Below are schedules, descriptions of the robots to be presented, and brief biographies of the researchers involved. Exhibits will be showcased on July 15 and 16. This gives audiences opportunities to see and experience what was discussed in the Workshop. Workshop on Robotics and Creativity July 14, 2008, 8:20a.m.-12:30 p.m. This workshop explores the roles that creativity plays in robotics. This includes research where robots employ cognitive models and computation to display creativity. Also explored are partnerships among artists, scientists and engineers to generate a creative synergy and stimulate breakthroughs. Format A distinguished panel will kick off the workshop by providing a broad overview of the area, emerging paradigms and exciting opportunities. Those invited to exhibit at AAAI, will give short 10-minute briefs. These talks provide examples and personal thoughts of creativity and robotics. The workshop concludes with a 60-min Discussion Forum to capture thoughts and generate -
Bert Huang, Ph.D. Research Interests Education Employment History
Bert Huang, Ph.D. Assistant Professor, Department of Computer Science Virginia Tech, Blacksburg, VA 24060 [email protected], (646) 875-8838 http://berthuang.com Research Interests Machine learning, structured prediction, multi-relational learning, large-scale machine learning, probabilistic inference, belief propagation, network analysis, graph mining, combinatorial optimization, computational learning theory, social media analysis, data science, big data, computational social science. Education Doctor of Philosophy, Computer Science Columbia University, 2011 Thesis: Learning with Degree-Based Subgraph Estimation Advised by T. Jebara and A. Salleb-Aouissi Master of Science, Computer Science Columbia University, 2006 Bachelor of Science, Computer Science, Philosophy Brandeis University, 2004 Employment History Assistant Professor. Virginia Tech Department of Computer Science. Spring 2015–Present Postdoctoral Research Associate. University of Maryland Dept. of Computer Science. Fall 2011–Fall 2014 Graduate Research Assistant. Columbia University Dept. of Computer Science. Fall 2006–Summer 2011 Research Intern. IBM Research, Thomas J. Watson Research Center. Summer 2010 Lecturer. Columbia University Department of Computer Science. Fall 2008–Spring 2010 Teaching Virginia Tech Department of Computer Science Instructor, Introduction to Artificial Intelligence. Spring 2015. University of Maryland Department of Computer Science Co-Instructor, Link Mining (joint with L. Getoor). Spring 2012. Columbia University Department of Computer Science Instructor, Object Oriented Programming and Design in Java. Spring 2010. Instructor, Data Structures in Java. Fall 2009. Instructor, Data Structures and Algorithms. Spring 2009. Instructor, Introduction to Computer Science and Programming in C. Fall 2008. Teaching Assistant, Machine Learning. Spring 2007. Teaching Assistant, Introduction to Computer Science and Programming in C. Spring 2006. Awards Andrew P. Kosoresow Memorial Award for Excellence in Teaching and Service.