science

Knowledge Query and Manipulation Language

Knowledge Query and Manipulation Language 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 Knowledge Query and Manipulation Language rather than just read about it. In short: The Knowledge Query and Manipulation Language, or KQML, is a language and protocol for communication among software agents and knowledge-based systems. It was developed in the early 1990s as part of the DARPA knowledge Sharing Effort, which was aimed at developing techniques for building large-scale knowledge bases which are share-able and re-usable.

Key takeaways

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

Reference excerpt

The Knowledge Query and Manipulation Language, or KQML, is a language and protocol for communication among software agents and knowledge-based systems. It was developed in the early 1990s as part of the DARPA knowledge Sharing Effort, which was aimed at developing techniques for building large-scale knowledge bases which are share-able and re-usable. While originally conceived of as an interface to knowledge based systems, it was soon repurposed as an Agent communication language. Work on KQML was led by Tim Finin of the University of Maryland, Baltimore County and Jay Weber of EITech and involved contributions from many researchers. The KQML message format and protocol can be used to interact with an intelligent system, either by an application program, or by another intelligent system. KQML's "performatives" are operations that agents perform on each other's knowledge and goal stores. Higher-level interactions such as contract nets and negotiation are built using these. KQML's "communication facilitators" coordinate the interactions of other agents to support knowledge sharing. Experimental prototype systems support concurrent engineering, intelligent design, intelligent planning, and scheduling. KQML is superseded by FIPA-ACL.

References

Worked examples

Example 1 — a first encounter with Knowledge Query and Manipulation Language

Start with the simplest possible case. Write down what Knowledge Query and Manipulation Language 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 Knowledge Query and Manipulation Language 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 Knowledge Query and Manipulation Language 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 Knowledge Query and Manipulation Language

In research: Knowledge Query and Manipulation Language 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 Knowledge Query and Manipulation Language 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: Knowledge Query and Manipulation Language is common in secondary-school and first-year university syllabi. It links to neighbouring topics Agent communications languages, Knowledge representation languages, so understanding it makes those chapters shorter.
In everyday life: Look for Knowledge Query and Manipulation Language 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 Knowledge Query and Manipulation Language in 20 minutes

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

Frequently asked questions

What is Knowledge Query and Manipulation Language in simple terms?

Why does Knowledge Query and Manipulation Language 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 Knowledge Query and Manipulation Language?

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 Knowledge Query and Manipulation Language.