astronomy

John E. Laird

John E. Laird 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 John E. Laird rather than just read about it. In short: John Edwin Laird (born March 16, 1954) is a computer scientist who created the Soar cognitive architecture at Carnegie Mellon University with Paul Rosenbloom and Allen Newell. Laird is a professor in the Computer Science and Engineering Division of the Electrical Engineering and Computer Science Department of the University of Michigan.

Key takeaways

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

Reference excerpt

John Edwin Laird (born March 16, 1954) is a computer scientist who created the Soar cognitive architecture at Carnegie Mellon University with Paul Rosenbloom and Allen Newell. Laird is a professor in the Computer Science and Engineering Division of the Electrical Engineering and Computer Science Department of the University of Michigan.

Education and career Laird received a BS in Communication and Computer Science from the University of Michigan in 1975 and a Ph.D. in computer science from Carnegie Mellon University in 1983. His Ph.D. thesis advisor was Allen Newell. Laird was a researcher at Xerox PARC in the Intelligent Systems Laboratory from 1984 to 1986; in 1986, he joined the faculty at the University of Michigan. Laird has continued to research architectures of the mind and to develop and evolve the Soar architecture since his time at CMU. He organizes the annual Soar workshop and participates in the international Soar Research Group. In 1998, he co-founded Soar Technology, a company specializing in creating autonomous AI entities based on Soar; he currently serves on its board of directors. His research interests include cognitive architecture, problem-solving, learning, reinforcement learning, episodic memory, semantic memory, and emotion-inspired processing. He is a Fellow of ACM, the Association for the Advancement of Artificial Intelligence (AAAI), the Cognitive Science Society, and the American Association for the Advancement of Science (AAAS).

Publications The Soar Cognitive Architecture, Laird, J. E., 2012, MIT Press. The Soar Papers: Readings on Integrated Intelligence, Rosenbloom, Laird, and Newell (1993) Soar: An Architecture for General Intelligence, Artificial Intelligence, 33: 1-64. Laird, Rosenbloom, Newell, John and Paul, Allen (1987)

References Professional History on University of Michigan Website Archived 2017-01-22 at the Wayback Machine Soar Technology

External links Soar project home page John E. Laird's Home Page Archived 2008-09-25 at the Wayback Machine

Worked examples

Example 1 — a first encounter with John E. Laird

Start with the simplest possible case. Write down what John E. Laird 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 John E. Laird 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 John E. Laird 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 John E. Laird

In research: John E. Laird 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 John E. Laird 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: John E. Laird is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1954 births, American computer scientists, American computer specialist stubs, so understanding it makes those chapters shorter.
In everyday life: Look for John E. Laird 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 John E. Laird in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what John E. Laird 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 John E. Laird out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is John E. Laird in simple terms?

Why does John E. Laird 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 John E. Laird?

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 John E. Laird.