Woody Bledsoe

Last updated • 3 min readFrom Wikipedia, The Free Encyclopedia

Woodrow Wilson Bledsoe
Bledsoe.gif
Born(1921-11-12)November 12, 1921
DiedOctober 4, 1995(1995-10-04) (aged 73)
Alma mater University of California, Berkeley
SpouseVirginia (née Norgaard)
Children4
Awards
Scientific career
Thesis Separative Measures for Topological Spaces  (1953)
Doctoral advisor Anthony Perry Morse
Doctoral students Robert S. Boyer

Woodrow Wilson "Woody" Bledsoe (November 12, 1921 – October 4, 1995) was an American mathematician, computer scientist, and prominent educator. He is one of the founders of artificial intelligence (AI), making early contributions in pattern recognition, [1] facial recognition, [2] and automated theorem proving. [3] [4] [5] [6] He continued to make significant contributions to AI throughout his long career. One of his influences was Frank Rosenblatt. [7]

Contents

Beginning in 1966, he worked at the department of mathematics and computer science of the University of Texas at Austin, holding the Peter O'Donnell Jr. Centennial Chair in Computing Science starting in 1987. [8] :723

Bledsoe joined the Church of Jesus Christ of Latter-day Saints as an adult, and served in the church as a bishop, counselor to the stake presidency, and stake patriarch. He also served as a leader in the Boy Scouts of America. [9] [10] Bledsoe died on October 4, 1995, of amyotrophic lateral sclerosis, more commonly known as ALS or Lou Gehrig's disease.

Works

The n-tuple method (1959) was an early method for learning a pattern recognition program. The basic method is illustrated by the problem of recognizing 36 alphanumerical characters (0-9, a-z). [11]

Let the input be a 10x15 binary image. It is equivalent to a single string with 150 binary letters. Now, randomly partition the 150 binary pixels into 75 pairs. Each pair has 4 possibilities: 00, 01, 10, 11. Now we will define a 300x36 binary matrix as follows:

Let represent the 00-state of the first pair, and similarly for the others. We have 300 such states, each represented in a row. The 36 columns each correspond to one alphanumerical character. The entire binary matrix is arranged as follows:The pattern recognizer is defined by the binary matrix. It is trained by firstsetting all entries to zero, then it is presented with several binary images of each alphanumerical character. For each image, the corresponding entries in the matrix are set to one, and the other entries are unchanged. This is an example of machine learning.

After the training the recognizer, it can be used to recognize new images. First compute the new image's corresponding column vector, then take the dot-product with each column of the binary matrix. The column with the highest dot-product is outputted as the most likely character.

Further reading

Selected publications

Related Research Articles

Planner is a programming language designed by Carl Hewitt at MIT, and first published in 1969. First, subsets such as Micro-Planner and Pico-Planner were implemented, and then essentially the whole language was implemented as Popler by Julian Davies at the University of Edinburgh in the POP-2 programming language. Derivations such as QA4, Conniver, QLISP and Ether were important tools in artificial intelligence research in the 1970s, which influenced commercial developments such as Knowledge Engineering Environment (KEE) and Automated Reasoning Tool (ART).

<span class="mw-page-title-main">Douglas Lenat</span> Computer scientist and AI pioneer

Douglas Bruce Lenat was an American computer scientist and researcher in artificial intelligence who was the founder and CEO of Cycorp, Inc. in Austin, Texas.

The Automated Mathematician (AM) is one of the earliest successful discovery systems. It was created by Douglas Lenat in Lisp, and in 1977 led to Lenat being awarded the IJCAI Computers and Thought Award.

<span class="mw-page-title-main">John McCarthy (computer scientist)</span> American scientist (1927–2011)

John McCarthy was an American computer scientist and cognitive scientist. He was one of the founders of the discipline of artificial intelligence. He co-authored the document that coined the term "artificial intelligence" (AI), developed the programming language family Lisp, significantly influenced the design of the language ALGOL, popularized time-sharing, and invented garbage collection.

In artificial intelligence, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems, symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems.

A blackboard system is an artificial intelligence approach based on the blackboard architectural model, where a common knowledge base, the "blackboard", is iteratively updated by a diverse group of specialist knowledge sources, starting with a problem specification and ending with a solution. Each knowledge source updates the blackboard with a partial solution when its internal constraints match the blackboard state. In this way, the specialists work together to solve the problem. The blackboard model was originally designed as a way to handle complex, ill-defined problems, where the solution is the sum of its parts.

In artificial intelligence research, commonsense knowledge consists of facts about the everyday world, such as "Lemons are sour", or "Cows say moo", that all humans are expected to know. It is currently an unsolved problem in Artificial General Intelligence. The first AI program to address common sense knowledge was Advice Taker in 1959 by John McCarthy.

In computer science, in particular in knowledge representation and reasoning and metalogic, the area of automated reasoning is dedicated to understanding different aspects of reasoning. The study of automated reasoning helps produce computer programs that allow computers to reason completely, or nearly completely, automatically. Although automated reasoning is considered a sub-field of artificial intelligence, it also has connections with theoretical computer science and philosophy.

<span class="mw-page-title-main">Alan Bundy</span> British artificial intelligence researcher (born 1947)

Alan Richard Bundy is a professor at the School of Informatics at the University of Edinburgh, known for his contributions to automated reasoning, especially to proof planning, the use of meta-level reasoning to guide proof search.

<span class="mw-page-title-main">Alexander Brudno</span> Russian computer scientist (1918–2009)

Alexander L'vovich Brudno was a Russian computer scientist, best known for fully describing the alpha-beta pruning algorithm. From 1991 until his death he lived in Israel.

Richard Jay Waldinger is a computer science researcher at SRI International's Artificial Intelligence Center whose interests focus on the application of automated deductive reasoning to problems in software engineering and artificial intelligence.

David Alan Plaisted is a computer science professor at the University of North Carolina at Chapel Hill.

<span class="mw-page-title-main">Carl Hewitt</span> American computer scientist; Planner programming languagedesigner (1944-2022)

Carl Eddie Hewitt was an American computer scientist who designed the Planner programming language for automated planning and the actor model of concurrent computation, which have been influential in the development of logic, functional and object-oriented programming. Planner was the first programming language based on procedural plans invoked using pattern-directed invocation from assertions and goals. The actor model influenced the development of the Scheme programming language, the π-calculus, and served as an inspiration for several other programming languages.

<span class="mw-page-title-main">Eric Horvitz</span> American computer scientist, and Technical Fellow at Microsoft

Eric Joel Horvitz is an American computer scientist, and Technical Fellow at Microsoft, where he serves as the company's first Chief Scientific Officer. He was previously the director of Microsoft Research Labs, including research centers in Redmond, WA, Cambridge, MA, New York, NY, Montreal, Canada, Cambridge, UK, and Bangalore, India.

Donald W. Loveland is a professor emeritus of computer science at Duke University who specializes in artificial intelligence. He is well known for the Davis–Putnam–Logemann–Loveland algorithm.

Herbert Leo Gelernter was a professor in the Computer Science Department of Stony Brook University.

Christoph Walther is a German computer scientist, known for his contributions to automated theorem proving. He is Professor emeritus at Darmstadt University of Technology.

James Robert Slagle was an American computer scientist notable for his many achievements in Artificial Intelligence. Since 1984 he has been the Distinguished Professor of Computer Science at the University of Minnesota, Minneapolis, with former appointments at Johns Hopkins University, the National Institutes of Health, the Naval Research Laboratory, Lawrence Livermore Radiation Laboratory, University of California, Berkeley, and the Massachusetts Institute of Technology.

<span class="mw-page-title-main">Helen Chan Wolf</span> Artificial intelligence pioneer

Helen Chan Wolf is an artificial intelligence pioneer who worked on facial recognition technology and Shakey the robot, the world's first autonomous robot, at SRI International.

Deepak Kapur is a Distinguished Professor in the Department of Computer Science at the University of New Mexico.

References

  1. W.W. Bledsoe (1966). "Some Results on Multicategory Pattern Recognition". J. ACM . 13 (2): 304–316. doi: 10.1145/321328.321340 . S2CID   17150326.
  2. Raviv, Shaun. "The Secret History of Facial Recognition". Wired. ISSN   1059-1028 . Retrieved August 31, 2023.
  3. W.W. Bledsoe (1971). "Splitting and Reduction Heuristics in Automatic Theorem Proving" (PDF). Artif. Intell. 2 (1): 55–77. doi:10.1016/0004-3702(71)90004-x.
  4. W.W. Bledsoe (September 1975). "A New Method for Proving Certain Presburger Formulas". Proc. IJCAI (PDF). pp. 15–21.
  5. W.W. Bledsoe (1977). "Non-Resolution Theorem Proving". Artificial Intelligence . 9: 1–35. CiteSeerX   10.1.1.455.6139 . doi:10.1016/0004-3702(77)90012-1. Preceding technical report ATP29 (Sep.1975)
  6. W.W. Bledsoe and Kenneth Kunen and Robert E. Shostak (1985). "Completeness Results for Inequality Provers". Artif. Intell. 27 (3): 255–288. doi:10.1016/0004-3702(85)90015-3. Preceding technical report ATP65 (1983)
  7. McCorduck, Pamela (2018). Machines who think: a personal inquiry into the history and prospects of artificial intelligence. An A K Peters book. Boca Raton London New York: CRC Press. p. 106. ISBN   978-1-56881-205-2.
  8. Jean-Louis Lassez; Gordon Plotkin, eds. (1991). Computational Logic Essays in Honor of Alan Robinson. Cambridge/MA: MIT Press. ISBN   978-0-262-12156-9.
  9. Memorial Resolution – Woodrow W. Bledsoe
  10. "UT science pioneer 'Woody' Bledsoe dies". Austin American-Statesman. October 6, 1995. Retrieved March 13, 2013.
  11. Bledsoe, W. W.; Browning, I. (1959). "Pattern recognition and reading by machine". Papers presented at the December 1-3, 1959, eastern joint IRE-AIEE-ACM computer conference on - IRE-AIEE-ACM '59 (Eastern). ACM Press. pp. 225–232. doi:10.1145/1460299.1460326. ISBN   978-1-4503-7868-0. S2CID   15672245.