science

Multi-agent planning

Multi-agent planning 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 Multi-agent planning rather than just read about it. In short: In computer science multi-agent planning involves coordinating the resources and activities of multiple agents. NASA says, "multiagent planning is concerned with planning by (and for) multiple agents.

Key takeaways

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

Reference excerpt

In computer science multi-agent planning involves coordinating the resources and activities of multiple agents. NASA says, "multiagent planning is concerned with planning by (and for) multiple agents. It can involve agents planning for a common goal, an agent coordinating the plans (plan merging) or planning of others, or agents refining their own plans while negotiating over tasks or resources. The topic also involves how agents can do this in real time while executing plans (distributed continual planning). Multiagent scheduling differs from multiagent planning the same way planning and scheduling differ: in scheduling often the tasks that need to be performed are already decided, and in practice, scheduling tends to focus on algorithms for specific problem domains".

See also Automated planning and scheduling Distributed artificial intelligence Cooperative distributed problem solving and Coordination Multi-agent systems and Software agent and Self-organization Multi-agent reinforcement learning Task Analysis, Environment Modeling, and Simulation (TAEMS or TÆMS)

References

Further reading Durfee's (1999) chapter on Distributed Problem Solving and Planning desJardins et al. (1999). A Survey of Research in Distributed, Continual Planning. de Weerdt, Mathijs; Clement, Brad (2009). "Introduction to Planning in Multiagent Systems" (PDF). Multiagent and Grid Systems. 5 (4): 345–355. doi:10.3233/MGS-2009-0133.. Shoham, Yoav; Leyton-Brown, Kevin (2009). Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. New York: Cambridge University Press. ISBN 978-0-521-89943-7. See Chapter 2; downloadable free online. Vlassis, Nikos (2008). A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence. San Rafael, CA: Morgan & Claypool Publishers. ISBN 978-1-59829-526-9.

External links Tutorial on planning in multiagent systems

Worked examples

Example 1 — a first encounter with Multi-agent planning

Start with the simplest possible case. Write down what Multi-agent planning 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 Multi-agent planning 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 Multi-agent planning 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 Multi-agent planning

In research: Multi-agent planning 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 Multi-agent planning 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: Multi-agent planning is common in secondary-school and first-year university syllabi. It links to neighbouring topics Automated planning and scheduling, Multi-agent systems, Organization stubs, so understanding it makes those chapters shorter.
In everyday life: Look for Multi-agent planning 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.

Ask Teacher Smith questions about this articleOpens your AI tutor with a question about “Multi-agent planning” →

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Multi-agent planning in 20 minutes

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

Frequently asked questions

What is Multi-agent planning in simple terms?

Why does Multi-agent planning 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 Multi-agent planning?

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 Multi-agent planning.