Eurisko

Last updated
Original author(s) Douglas Lenat
Written in RLL-1
Type Discovery system

Eurisko (Gr., I discover) is a discovery system written by Douglas Lenat in RLL-1, a representation language itself written in the Lisp programming language. A sequel to Automated Mathematician, it consists of heuristics, i.e. rules of thumb, including heuristics describing how to use and change its own heuristics. [1] [2] Lenat was frustrated by Automated Mathematician's constraint to a single domain and so developed Eurisko; his frustration with the effort of encoding domain knowledge for Eurisko led to Lenat's subsequent development of Cyc. Lenat envisioned ultimately coupling the Cyc knowledgebase with the Eurisko discovery engine.

Contents

History

Development commenced at Carnegie Mellon in 1976 and continued at Stanford University in 1978 when Lenat returned to teach. "For the first five years, nothing good came out of it", Lenat said. But when the implementation was changed to a frame language based representation he called RLL (Representation Language Language), heuristic creation and modification became much simpler. Eurisko was then applied to a number of domains with surprising success, including VLSI chip design.

Lenat and Eurisko gained notoriety by submitting the winning fleet (a large number of stationary, lightly-armored ships with many small weapons) [3] to the United States Traveller TCS national championship in 1981, forcing extensive changes to the game's rules. However, Eurisko won again in 1982 when the program discovered that the rules permitted the program to destroy its own ships, permitting it to continue to use much the same strategy. [3] Tournament officials announced that if Eurisko won another championship the competition would be abolished; Lenat retired Eurisko from the game. [4] The Traveller TCS wins brought Lenat to the attention of DARPA, [5] which has funded much of his subsequent work.

Lenat was known for keeping his source code confidential during his lifetime. In 2023, it was reported that source code for both Eurisko and the previous Automated Mathematician system had been found in public code archives. [6] The following year, Eurisko code was shown running under Medley Interlisp. [7]

In the first-season The X-Files episode "Ghost in the Machine", Eurisko is the name of a fictional software company responsible for the episode's "monster of the week", facilities management software known as "Central Operating System", or "COS". COS (described in the episode as an "adaptive network") is shown to be capable of learning when its designer arrives at Eurisko headquarters and is surprised to find that COS has given itself the ability to speak. The designer is forced to create a virus to destroy COS after COS commits a series of murders in an apparent effort to prevent its own destruction.[ citation needed ]

Lenat is mentioned and Eurisko is discussed at the end of Richard Feynman's Computer Heuristics Lecture as part of the Idiosyncratic Thinking Workshop Series. [8]

Lenat and Eurisko are mentioned in the 2019 James Rollins novel Crucible that deals with artificial intelligence and artificial general intelligence.

Notes

  1. ↑ Lenat, Douglas (1983). "EURISKO: A program that learns new heuristics and domain concepts". Artificial Intelligence. 21 (1–2): 61–98. doi:10.1016/s0004-3702(83)80005-8.
  2. ↑ Drexler, K. Eric (1986). "Thinking Machines (Chapter 5)". Engines of Creation. Doubleday. ISBN   978-0-385-19973-5. Archived from the original on 2018-02-16. Retrieved 2006-06-24. EURISKO ... is guided by heuristics ... in effect, various rules of thumb.
  3. 1 2 Gladwell, Malcolm (2009-05-11). "How underdogs can win". The New Yorker. Retrieved 2010-01-11.
  4. ↑ Johnson, George (1984). "Eurisko, The Computer With A Mind Of Its Own". Washington, D.C.: The Alicia Patterson Foundation. Archived from the original on 2019-04-29.
  5. ↑ Understanding Computers: Artificial Intelligence. Amsterdam: Time-Life Books. 1986. p. 84. ISBN   978-0-7054-0915-5.
  6. ↑ "Doug Lenat's source code for AM and possibly EURISKO w/Traveller found in public archives". white-flame.com. Retrieved 2023-11-25.
  7. ↑ "EURISKO lives | Paraprogramming Dispatches". blog.funcall.org. Retrieved 2024-04-23.
  8. ↑ "Richard Feynman Computer Heuristics Lecture". Archived from the original on 2021-12-12 – via www.youtube.com.

Related Research Articles

<span class="mw-page-title-main">Cyc</span> Artificial intelligence project

Cyc is a long-term artificial intelligence project that aims to assemble a comprehensive ontology and knowledge base that spans the basic concepts and rules about how the world works. Hoping to capture common sense knowledge, Cyc focuses on implicit knowledge. The project began in July 1984 at MCC and was developed later by the Cycorp company.

<span class="mw-page-title-main">Expert system</span> Computer system emulating the decision-making ability of a human expert

In artificial intelligence (AI), an expert system is a computer system emulating the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural programming code. Expert systems were among the first truly successful forms of AI software. They were created in the 1970s and then proliferated in the 1980s, being then widely regarded as the future of AI — before the advent of successful artificial neural networks. An expert system is divided into two subsystems: 1) a knowledge base, which represents facts and rules; and 2) an inference engine, which applies the rules to the known facts to deduce new facts, and can include explaining and debugging abilities.

Knowledge representation and reasoning is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge, in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning.

CycL in computer science and artificial intelligence, is an ontology language used by Douglas Lenat's Cyc artificial intelligence project. Ramanathan V. Guha was instrumental in designing early versions of the language. A close CycL variant exists named MELD.

<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.

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.

In the history of artificial intelligence, neat and scruffy are two contrasting approaches to artificial intelligence (AI) research. The distinction was made in the 1970s and was a subject of discussion until the mid-1980s.

Dendral was a project in artificial intelligence (AI) of the 1960s, and the computer software expert system that it produced. Its primary aim was to study hypothesis formation and discovery in science. For that, a specific task in science was chosen: help organic chemists in identifying unknown organic molecules, by analyzing their mass spectra and using knowledge of chemistry. It was done at Stanford University by Edward Feigenbaum, Bruce G. Buchanan, Joshua Lederberg, and Carl Djerassi, along with a team of highly creative research associates and students. It began in 1965 and spans approximately half the history of AI research.

<span class="mw-page-title-main">Logic in computer science</span> Academic discipline

Logic in computer science covers the overlap between the field of logic and that of computer science. The topic can essentially be divided into three main areas:

A knowledge-based system (KBS) is a computer program that reasons and uses a knowledge base to solve complex problems. Knowledge-based systems were the focus of early artificial intelligence researchers in the 1980s. The term can refer to a broad range of systems. However, all knowledge-based systems have two defining components: an attempt to represent knowledge explicitly, called a knowledge base, and a reasoning system that allows them to derive new knowledge, known as an inference engine.

The Stanford Research Institute Problem Solver, known by its acronym STRIPS, is an automated planner developed by Richard Fikes and Nils Nilsson in 1971 at SRI International. The same name was later used to refer to the formal language of the inputs to this planner. This language is the base for most of the languages for expressing automated planning problem instances in use today; such languages are commonly known as action languages. This article only describes the language, not the planner.

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.

The following outline is provided as an overview of and topical guide to artificial intelligence:

A hyper-heuristic is a heuristic search method that seeks to automate, often by the incorporation of machine learning techniques, the process of selecting, combining, generating or adapting several simpler heuristics to efficiently solve computational search problems. One of the motivations for studying hyper-heuristics is to build systems which can handle classes of problems rather than solving just one problem.

In information technology a reasoning system is a software system that generates conclusions from available knowledge using logical techniques such as deduction and induction. Reasoning systems play an important role in the implementation of artificial intelligence and knowledge-based systems.

Inductive programming (IP) is a special area of automatic programming, covering research from artificial intelligence and programming, which addresses learning of typically declarative and often recursive programs from incomplete specifications, such as input/output examples or constraints.

<span class="mw-page-title-main">Traveller Adventure 5: Trillion Credit Squadron</span> Science-fiction role-playing game supplement

Traveller Adventure 5: Trillion Credit Squadron is a 1981 role-playing game adventure for Traveller, written by Marc Miller, John Harshman and published by Game Designers' Workshop. Players each design their own fighting starship squadrons within a budget of one trillion credits (Cr1,000,000,000,000) and fight them against each other.

A discovery system is an artificial intelligence system that attempts to discover new scientific concepts or laws. The aim of discovery systems is to automate scientific data analysis and the scientific discovery process. Ideally, an artificial intelligence system should be able to search systematically through the space of all possible hypotheses and yield the hypothesis - or set of equally likely hypotheses - that best describes the complex patterns in data.

References

This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.