computer science

Graphplan

Graphplan is a computer 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 Graphplan rather than just read about it. In short: Graphplan is an algorithm for automated planning developed by Avrim Blum and Merrick Furst in 1995. Graphplan takes as input a planning problem expressed in STRIPS and produces, if one is possible, a sequence of operations for reaching a goal state.

Key takeaways

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

Reference excerpt

Graphplan is an algorithm for automated planning developed by Avrim Blum and Merrick Furst in 1995. Graphplan takes as input a planning problem expressed in STRIPS and produces, if one is possible, a sequence of operations for reaching a goal state. The name graphplan is due to the use of a novel planning graph, to reduce the amount of search needed to find the solution from straightforward exploration of the state space graph. In the state space graph:

the nodes are possible states, and the edges indicate reachability through a certain action. On the contrary, in Graphplan's planning graph:

the nodes are actions and atomic facts, arranged into alternate levels, and the edges are of two kinds: from an atomic fact to the actions for which it is a condition, from an action to the atomic facts it makes true or false. The first level contains true atomic facts identifying the initial state. Lists of incompatible facts that cannot be true at the same time and incompatible actions that cannot be executed together are also maintained. The algorithm then iteratively extends the planning graph, proving that there are no solutions of length l-1 before looking for plans of length l by backward chaining: supposing the goals are true, Graphplan looks for the actions and previous states from which the goals can be reached, pruning as many of them as possible thanks to incompatibility information. A closely related approach to planning is the Planning as Satisfiability (Satplan). Both reduce the automated planning problem to search for plans of different fixed horizon lengths.

References A. Blum and M. Furst (1997). Fast planning through planning graph analysis. Artificial intelligence. 90:281-300.

External links Avrim Blum's Graphplan home page PLPLAN: A Java GraphPlan Implementation NPlanner: A .NET GraphPlan Implementation Archived 2013-12-31 at the Wayback Machine Emplan and JavaGP: C++ and Java implementations of Graphplan MIT OpenCourseWare lecture on GraphPlan and making planning graphs

Worked examples

Example 1 — a first encounter with Graphplan

Start with the simplest possible case. Write down what Graphplan claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer 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 Graphplan 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 Graphplan 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 Graphplan

In research: Graphplan appears in computer 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 Graphplan 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: Graphplan is common in secondary-school and first-year university syllabi. It links to neighbouring topics Automated planning and scheduling, Search algorithms, so understanding it makes those chapters shorter.
In everyday life: Look for Graphplan 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 “Graphplan” →

Affiliate

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

How to study Graphplan in 20 minutes

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

Frequently asked questions

What is Graphplan in simple terms?

Why does Graphplan matter?

Because it connects several computer 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 Graphplan?

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 Graphplan.