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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. (CMU). The 1960s at Stanford AI Labs (SAIL) Copyright © 2005, American Association for Artificial Intelligence. All rights reserved. 0738-4602-2005 / $2.00 WINTER 2005 5 25th Anniversary Issue A Sampling of AI Research in 1980, from the Proceedings of AAAI-80 Vision Badler, O’Rourke, Platt, and Morris, Uni- Problem Solving versity of Pennsylvania Early Vision Processing Using Contextual Information in Com- Cooperating Expert Systems Recovering Surface Orientation from Tex- puter Vision, Olivier D. Faugeras, USC HEARSAY-III: A Framework for Expert ture, Andrew P. Witkin, SRI International Systems, Balzer, Erman, London, and Program Synthesis Shape-from-Texture Paradigm, John R. Williams, USC ISI Kender and Takeo Kanade, Carnegie Mel- Question Ordering in Mixed Initiative Quantifying and Simulating the Behav- lon University Program Specification Dialogue, Louis ior of KBIS, Lesser, Reed, and Pavlin, Uni- Steinberg, Rutgers Low Level Vision Systems, William B. versity of Massachusetts Thompson and Albert Yonas, University of Some Algorithm Design Methods, Steve Representation of Task Knowledge in Minnesota Tappel, Systems Control, Inc. User Interfaces, Eugene Ball and Phil Interpreting Line Drawings as 3D Sur- Automatic Goal-Directed Program Hayes, Carnegie Mellon University Transformation, Stephen Fickas, USC ISI faces, Harry G. Barrow and Jay M. Tenen- Problem Solving and Control baum, SRI Incremental, Informal Program Acquisi- Representation of Control Knowledge tion, Brian McCune, AI and D Systems Shape Encoding and Subjective Con- in Expert Systems, Janice S. Aikins, Stan- tours, Brady, Grimson, MIT and Lan- A Basis for a Theory of Program Synthe- ford University gridge, CSIRO sis, P. A. Subrahmanyam, USC ISI DELTA-MIN: A Search-Control Method Scene Analysis A Program Model for Computer Aided for Information-Gathering Problems, Program Synthesis, Richard J. Wood, Carbonell, Carnegie Mellon University Information Needed to Label a Scene, University of Maryland Eugene C. Freuder, University of New On Waiting, Arthur M. Farley, University Hampshire Theorem Proving of Oregon Interpretive Vision and Restriction An Efficient Relevance Criterion for Me- A Planner for Reasoning about Knowl- Graphs, Rodney Brooks and Thomas Bin- chanical Theorem Proving, David A. edge and Action, Douglas E. Appelt, SRI ford, Stanford Plaisted, University of Illinois Urbana- International Sticks, Plates, and Blobs: A 3D Object Champaign Making Judgments, Hans J. Berliner, Car- Representation for Scene Analysis, On Proving Laws of the Algebra, Jacek negie Mellon University Shapiro, Moriarty, Mulgaonkar, and Haral- Leszczylowski, Polish Academy of Sciences Multiple-Agent Planning Systems, Kurt ick, Virginia Polytechnic Institute and State Establishing Completeness Results in Konolige and Nils J. Nilsson, SRI University Theorem Proving, Peterson, University of A Simple Game-Searching Algorithm Motion Analysis Missouri at St. Louis with Proven Optimal Properties, Judea Automatic Generation of Semantic At- Pearl, UCLA Constraint-Based Inference from Image tachments in FOL, Luigla Aiello, Stanford Problem Solving in Using Interactive Motion, Daryl T. Lawton, University of Dialog, Harry C. Reinstein, IBM Palo Alto Massachusetts HCPRVR: An Interpreter for Logic Pro- grams, Daniel Chester, University of Texas Scientific Center Static Analysis of Moving Jointed Ob- at Austin Representing Knowledge in an Interac- jects, Jon A. Webb, University of Texas at tive Planner, Ann E. Robinson and David Austin First Experiments with Rue Automated Deduction, Vincent J. Digricoli, The Cou- E. Wilkins, SRI Bootstrap Stereo, Marsha Jo Hannah, rant Institute Inference with Recursive Rules, Stuart C. Lockheed Palo Alto Research Laboratory Shapiro and Donald P. McKay, SUNY Buf- Robotic Vision Mathematical falo and Theoretical Foundations Locating Partially Visible Objects: The Knowledge Representation Local Feature Focus Method, Robert C. What’s Wrong with Non-Monotonic Bolles, SRI International Logic? David J. Israel, Bolt Beranek and Advanced Knowledge Collision Avoidance Among 3D Ob- Newman, Inc. Representation jects, Ahuja, Chien, Yen, and Bridwell, Pathology on Game Trees: A Summary A Frame-Based Production System Ar- University of Illinois of Results, Dana S. Nau, University of chitecture, David E. Smith and Jan E. Automated Inspection Using Gray-Scale Maryland Clayton, Stanford Statistics, Stephen T. Barnard, SRI Interna- Max-Min Chaining of Weighted Causal Knowledge Embedding in the Descrip- tional Assertions Is Loop Free, Ng and Walker, tion System Omega, Hewitt, Attardi, and Human Movement Understanding, Rutgers University Simi, MIT 6 AI MAGAZINE 25th Anniversary Issue A Representation Language, Russell Consultation System Behave Intelli- Trouble-Shooting by Plausible Infer- Greiner and Douglas B. Lenat, Stanford gently, René Reboh, SRI ence, Leonard Friedman, JPL, Caltech. Applied Knowledge Representa- An Approach to Acquiring and Apply- An Application of the Prospector Sys- tion ing Knowledge, Norman Haas and Gary tem to Uranium Resource Evaluation, G. Hendrix, SRI John Gaschnig, SRI Spatial and Qualitative Aspects of Rea- Self-Correcting Generalization, Stephen soning about Motion, Kenneth D. For- Some Requirements for a Computer- B. Whltehill, University of California, Ir- bus, MIT Based Legal Consultant, L. Thorne Mc- vine Computer Interpretation of Human Carty, Rutgers Stick Figures, Martin Herman, Carnegie Specialized Systems Mellon University Natural Language Intelligent Retrieval Planning, Jonathan Research on Expert Problem Solving in When Expectation Fails: A Self-Correct- J. King, Stanford University Physics, Novak and Araya, University of ing Inference System, Richard H. Gran- Texas at Austin A Theory of Metric Spatial Inference, ger, Jr., University of California Irvine Drew McDermott, Yale University Knowledge-Based Simulation, Philip Generating Relevant Explanations: Nat- Design Sketch for a Million-Element Klahr and William S. Faught, The Rand ural Language Responses to Questions NETL Machine, Scott E. Fahlman, Carne- Corporation about Database Structure, Kathleen R. gie Mellon Interactive Frame Instantiation, Carl McKeown, University of Pennsylvania Perceptual Reasoning in a Hostile Envi- Engelman, Ethan A. Scarl, and Charles H. The Semantic Interpretation of Nomi- Berg, MITRE ronment, Thomas D. Garvey and Martin A. Fischler, SRI nal Compounds, Timothy Wilking Finin, University of Illinois Specialized Issues in Knowledge Overview of an Example Generation Representation System, Edwina L. Rissland and Elliot M. Towards an AI Model of Argumentation, Descriptions for a Programming Envi- Soloway, University of Massachusetts Birnbaum, Flowers, and McGuire, Yale ronment, Ira Goldstein and Daniel Bo- Structure Comparison and Semantic In- University brow, Xerox PARC terpretation of Differences, Wellington Knowledge Representation for Syntactic Rule-Based Inference in Large Knowl- Yu Chiu, USC ISI / Semantic Processing, Bobrow and Web- edge Bases, William Mark, USC ISI Performing Inferences over Recursive ber, University of Pennsylvania A Process for Evaluating Tree-Consisten- Data Bases, Naqvi and Henschen, North- Language and Memory: Generalization cy, John L. Goodson, Rutgers University western University as a Part of Understanding, Michael Leb- Reasoning about Change in Knowledge- Piaget and Artificial Intelligence, Jarrett owitz, Yale able Office Systems, Gerald R. Barber, K. Rosenberg, University of California, Ber- Failures in Natural Language Systems: MIT keley Applications to Data Base Query Sys- On Supporting the Use of Procedures in tems, Mays, University of Pennsylvania Office Work, Fikes and Henderson, Jr., Xe- Applications rox PARC R1: An Expert in the Computer Systems Memory Models Metaphors and Models, Michael R. Gene- Domain, John McDermott, Carnegie Mel- Organizing
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