science

State-space planning

State-space 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 State-space planning rather than just read about it. In short: In artificial intelligence and computer programming, state-space planning is a process used in designing programs to search for data or solutions to problems. In a computer algorithm that searches a data structure for a piece of data, for example a program that looks up a word in a computer dictionary, the state space is a collective term for all the data to be searched.

Key takeaways

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

Reference excerpt

In artificial intelligence and computer programming, state-space planning is a process used in designing programs to search for data or solutions to problems. In a computer algorithm that searches a data structure for a piece of data, for example a program that looks up a word in a computer dictionary, the state space is a collective term for all the data to be searched. Similarly, artificial intelligence programs often employ a process of searching through a finite universe of possible procedures for reaching a goal, to find a procedure or the best procedure to achieve the goal. The universe of possible solutions to be searched is called the state space. State-space planning is the process of deciding which parts of the state space the program will search, and in what order.

Definition The simplest classical planning (see Automated planning and scheduling) algorithms are state-space search algorithms. These are search algorithms in which the search space is a subset of the state space: Each node corresponds to a state of the world, each arc corresponds to a state transition, and the current plan corresponds to the current path in the search space. Forward search and backward search are two of main samples of state-space planning. In the algorithms that follow, by "non-deterministic", we mean that the chosen graph search algorithm for picking a next branch is arbitrary. One can brute-force (BFS, DFS, IDS, etc.), use heuristics (A*, IDA*, etc.), etc. This is a choice which generally depends on the nature of the problem.

Forward search Forward search is an algorithm that searches forward from the initial state of the world to try to find a state that satisfies the goal formula. We say an action is applicable in a state s if the preconditions of this action are true in s. With O the set of actions, s0 the initial state, and g the goal state:

Forward-search(O, s0, g) s = s0 P = the empty plan loop if s satisfies g then return P applicable = {a | a is a ground instance of an operator in O, and a is applicable in s} if applicable = ∅ then return failure nondeterministically choose an action a from applicable s = γ(s, a) P = P.a

Backward search Backward search is an algorithm that begins with goal state and back track to its initial state. This method is sometimes called "back propagation". We say an action is relevant if its add-effects (literals of the state turned true) are in G, and none of its del-effects (literals of the state turned false) are in G. With O the set of actions, s0 the initial state, and g the goal state:

Backward-search(O, s0, g) s = s0 P = the empty plan loop if s satisfies g then return P relevant = {a | a is a ground instance of an operator in O that is relevant for g} if relevant = ∅ then return failure nondeterministically choose an action a from relevant P = a.P s = γ−1(s, a)

Worked examples

Example 1 — a first encounter with State-space planning

Start with the simplest possible case. Write down what State-space 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 State-space 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 State-space 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 State-space planning

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

Affiliate

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

How to study State-space planning in 20 minutes

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

Frequently asked questions

What is State-space planning in simple terms?

Why does State-space 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 State-space 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 State-space planning.