science

Rog-O-Matic

Rog-O-Matic is a science 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 Rog-O-Matic rather than just read about it. In short: Rog-O-Matic is a bot developed in 1981 to play and win the video game Rogue, by four graduate students in the Computer Science Department at Carnegie-Mellon University in Pittsburgh: Andrew Appel, Leonard Hamey, Guy Jacobson and Michael Loren Mauldin. Described as a "belligerent expert system", Rog-O-Matic performs well when tested against expert Rogue players, even winning the game.

Key takeaways

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

Reference excerpt

Rog-O-Matic is a bot developed in 1981 to play and win the video game Rogue, by four graduate students in the Computer Science Department at Carnegie-Mellon University in Pittsburgh: Andrew Appel, Leonard Hamey, Guy Jacobson and Michael Loren Mauldin. Described as a "belligerent expert system", Rog-O-Matic performs well when tested against expert Rogue players, even winning the game.

In a test during a three-week period in 1983, Rog-O-Matic had a higher median score than any of the 15 top Rogue players at the Carnegie-Mellon University and, at the University of Texas at Austin, found the Amulet of Yendor in a passageway on the 26th level, continued on to the surface and emerged into the light of day. Because all information in Rogue is communicated to the player via ASCII text, Rog-O-Matic has automatic access to the same information a human player has. The program is still the subject of some scholarly interest; a 2005 paper said:

Rog-O-Matic differs from traditional expert systems in that it has the ability to work within a dynamic environment, for example the randomly generated terrain and adversaries. More importantly, the system was designed to operate in spite of limited information, recording and integrating knowledge about the environment as it is discovered.

Notes

References Mauldin M.; Jacobson G.; Appel A.; Hamey L. (16 May 1984). "ROG-O-MATIC: A Belligerent Expert System". Carnegie Mellon University Department of Computer Science. Retrieved 2007-10-02.

External links "Rogue-like Archive". Source for both Rogue and Rog-O-Matic "Rogue Archive". GitHub.

Worked examples

Example 1 — a first encounter with Rog-O-Matic

Start with the simplest possible case. Write down what Rog-O-Matic claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, 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 Rog-O-Matic 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 Rog-O-Matic 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 Rog-O-Matic

In research: Rog-O-Matic appears in science 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 Rog-O-Matic 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: Rog-O-Matic is common in secondary-school and first-year university syllabi. It links to neighbouring topics Expert systems, Game artificial intelligence, so understanding it makes those chapters shorter.
In everyday life: Look for Rog-O-Matic 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 Rog-O-Matic in 20 minutes

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

Frequently asked questions

What is Rog-O-Matic in simple terms?

Why does Rog-O-Matic matter?

Because it connects several science 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 Rog-O-Matic?

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 Rog-O-Matic.