computer science

Jump point search

Jump point search 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 Jump point search rather than just read about it. In short: In computer science, jump point search (JPS) is an optimization to the A* search algorithm for uniform-cost grids. It reduces symmetries in the search procedure by means of graph pruning, eliminating certain nodes in the grid based on assumptions that can be made about the current node's neighbors, as long as certain conditions relating to the grid are satisfied.

Key takeaways

Jump point search 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 Jump point search to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Jump point search from memory before moving on to harder problems.

Reference excerpt

In computer science, jump point search (JPS) is an optimization to the A* search algorithm for uniform-cost grids. It reduces symmetries in the search procedure by means of graph pruning, eliminating certain nodes in the grid based on assumptions that can be made about the current node's neighbors, as long as certain conditions relating to the grid are satisfied. As a result, the algorithm can consider long "jumps" along straight (horizontal, vertical and diagonal) lines in the grid, rather than the small steps from one grid position to the next that ordinary A* considers. Jump point search preserves A*'s optimality, while potentially reducing its running time by an order of magnitude.

History Harabor and Grastien's original publication provides algorithms for neighbor pruning and identifying successors. The original algorithm for neighbor pruning allowed corner-cutting to occur, which meant the algorithm could only be used for moving agents with zero width, limiting its application to either real-life agents (e.g., robotics) or simulations (e.g., many games). The authors presented modified pruning rules for applications where corner-cutting is not allowed the following year. This paper also presents an algorithm for pre-processing a grid in order to minimize online search times. A number of further optimizations were published by the authors in 2014. These optimizations include exploring columns or rows of nodes instead of individual nodes, pre-computing "jumps" on the grid, and stronger pruning rules.

Future work Although jump point search is limited to uniform cost grids and homogeneously sized agents, the authors are placing future research into applying JPS with existing grid-based speed-up techniques such as hierarchical grids.

References

Worked examples

Example 1 — a first encounter with Jump point search

Start with the simplest possible case. Write down what Jump point search 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 Jump point search 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 Jump point search 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 Jump point search

In research: Jump point search 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 Jump point search 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: Jump point search is common in secondary-school and first-year university syllabi. It links to neighbouring topics Game artificial intelligence, Graph algorithms, Search algorithms, so understanding it makes those chapters shorter.
In everyday life: Look for Jump point search 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 Jump point search in 20 minutes

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

Frequently asked questions

What is Jump point search in simple terms?

Why does Jump point search 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 Jump point search?

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 Jump point search.