astronomy

Richard Waldinger

Richard Waldinger is a astronomy topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Richard Waldinger rather than just read about it. In short: Richard Jay Waldinger is a computer science researcher at SRI International's Artificial Intelligence Center (where he has worked since 1969) whose interests focus on the application of automated deductive reasoning to problems in software engineering and artificial intelligence. Early life and education In his thesis (Carnegie Mellon University, 1969), which concerned the extraction of computer programs from proofs…

Key takeaways

Richard Waldinger belongs to astronomy; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Richard Waldinger to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Richard Waldinger from memory before moving on to harder problems.

Reference excerpt

Richard Jay Waldinger is a computer science researcher at SRI International's Artificial Intelligence Center (where he has worked since 1969) whose interests focus on the application of automated deductive reasoning to problems in software engineering and artificial intelligence.

Early life and education In his thesis (Carnegie Mellon University, 1969), which concerned the extraction of computer programs from proofs of theorems, he found that the application of the resolution rule accounted for the appearance of a conditional branch in the extracted program, while the use of the mathematical induction principle caused the introduction of recursion and other repetitive constructs.

Career Waldinger started at SRI International, then known as the Stanford Research Institute, in 1969, and has remained there since then. He has served coffee and cookies in his office at SRI twice a week since 1970.

QA4 Waldinger collaborated with Cordell Green, Robert Yates, Jeff Rulifson, and Jan Derksen on QA4, a PLANNER-like artificial intelligence language geared towards automatic planning and theorem proving. QA4 introduced the notion of context and also of associative-commutative unification, which made the associative and commutative axioms for operators not only unnecessary but also inexpressible. They applied the language to planning for the SRI robot, Shakey. With Bernie Elspas and Karl Levitt, Waldinger used QA4 for program verification (proving that a program does what it's supposed to), obtaining automatic verifications for the unification algorithm and Hoare's FIND program.

Program synthesis While Waldinger's thesis had dealt with the synthesis of applicative programs, which return an output but produce no side effects, Waldinger then turned to the synthesis of imperative programs, which do both. To deal with the problem of achieving simultaneously goals that interfere with each other, he introduced the notion of goal regression, which was obtained from earlier work in program verification by Floyd, King, Hoare, and Dijkstra. Since imperative programs are analogous to plans, the approach was also applicable to classical AI planning problems. In collaboration with Zohar Manna, of Stanford University, Waldinger developed nonclausal resolution, a form of resolution that did not require the translation of logical sentences into a restricted clausal form. Not only was the translation expensive, but also it sometimes pathologically complicated the proof of the resulting theorem; these problems were circumvented by the new rule. They applied the rule on paper to produce a detailed synthesis of a unification algorithm. In a separate paper, they synthesized a novel square-root algorithm; they found that the notion of binary search appears spontaneously by a single application of the resolution rule to the specification of the square root.

SNARK Some of Manna and Waldinger's theorem proving ideas were incorporated into the design of Mark Stickel's SNARK theorem prover. NASA researchers, led by Mike Lowry, used SNARK in the implementation of the software-development environment Amphion, which has been used to construct programs to analyze data from NASA missions for planetary astronomers. Software constructed automatically by Amphion has been used to plan photography for the Cassini-Huygens NASA mission; this is perhaps the most practical application to date of software constructed automatically by deductive methods. The SNARK system has been incorporated by the Kestrel Institute into their software development environment Specware, which has been used by Waldinger for the validation of the first-order axiomatization of DAML, the DARPA agent markup language, and its successor, OWL. SNARK uncovered inconsistencies not only in the axioms for DAML, but also in the axioms for the foundational language KIF, on which the DAML axiomatization was based. Recently, Waldinger has worked on the application of deductive methods to answer questions in geography, biology, and intelligence analysis. In collaboration with the Kestrel Institute, he has been using SNARK to authenticate security protocols.

Memberships and awards In 1991, Waldinger was elected as a fellow of the Association for the Advancement of Artificial Intelligence.

Personal life In his personal life, Waldinger is a student of aikido, yoga, and meditation. A member of an established writing group, he has published food journalism and erotic fiction. He is married and has two children and three grandchildren.

References

Further reading Gerd Große and Richard Waldinger. "Towards a Theory of Simultaneous Actions" EWSP 1991: 78-87. Zohar Manna and Richard Waldinger. "The Origin of a Binary-Search Paradigm" Sci. Comput. Program. 9(1): 37-83 (1987)

External links Richard Waldinger’s home page

Worked examples

Example 1 — a first encounter with Richard Waldinger

Start with the simplest possible case. Write down what Richard Waldinger claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In astronomy, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Richard Waldinger before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Richard Waldinger ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Richard Waldinger

In research: Richard Waldinger appears in astronomy research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Richard Waldinger in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Richard Waldinger is common in secondary-school and first-year university syllabi. It links to neighbouring topics Artificial intelligence researchers, Carnegie Mellon University alumni, Fellows of the Association for the Advancement of Artificial Intelligence, so understanding it makes those chapters shorter.
In everyday life: Look for Richard Waldinger outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Richard Waldinger in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Richard Waldinger means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Richard Waldinger out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Richard Waldinger in simple terms?

Why does Richard Waldinger matter?

Because it connects several astronomy ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Richard Waldinger?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Richard Waldinger.