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Stanford Research Institute Problem Solver

Stanford Research Institute Problem Solver 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 Stanford Research Institute Problem Solver rather than just read about it. In short: The Stanford Research Institute Problem Solver, known by its acronym STRIPS, is an automated planner developed by Richard Fikes and Nils Nilsson in 1971 at SRI International. The same name was later used to refer to the formal language of the inputs to this planner.

Key takeaways

  • Stanford Research Institute Problem Solver 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 Stanford Research Institute Problem Solver to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Stanford Research Institute Problem Solver from memory before moving on to harder problems.

Reference excerpt

The Stanford Research Institute Problem Solver, known by its acronym STRIPS, is an automated planner developed by Richard Fikes and Nils Nilsson in 1971 at SRI International. The same name was later used to refer to the formal language of the inputs to this planner. This language is the base for most of the languages for expressing automated planning problem instances in use today; such languages are commonly known as action languages. This article only describes the language, not the planner.

Definition A STRIPS instance is composed of:

An initial state; The specification of the goal states – situations that the planner is trying to reach; A set of actions. For each action, the following are included: preconditions (what must be established before the action is performed); postconditions (what is established after the action is performed). Mathematically, a STRIPS instance is a quadruple ⟨ P , O , I , G ⟩ {\displaystyle \langle P,O,I,G\rangle } , in which each component has the following meaning:

P {\displaystyle P} is a set of conditions (i.e., propositional variables);

O {\displaystyle O} is a set of operators (i.e., actions); each operator is itself a quadruple ⟨ α , β , γ , δ ⟩ {\displaystyle \langle \alpha ,\beta ,\gamma ,\delta \rangle } , each element being a set of conditions. These four sets specify, in order, which conditions must be true for the action to be executable, which ones must be false, which ones are made true by the action and which ones are made false;

I {\displaystyle I} is the initial state, given as the set of conditions that are initially true (all others are assumed false);

G {\displaystyle G} is the specification of the goal state; this is given as a pair ⟨ N , M ⟩ {\displaystyle \langle N,M\rangle } , which specify which conditions are true and false, respectively, in order for a state to be considered a goal state. A plan for such a planning instance is a sequence of operators that can be executed from the initial state and that leads to a goal state. Formally, a state is a set of conditions: a state is represented by the set of conditions that are true in it. Transitions between states are modeled by a transition function, which is a function mapping states into new states that result from the execution of actions. Since states are represented by sets of conditions, the transition function relative to the STRIPS instance ⟨ P , O , I , G ⟩ {\displaystyle \langle P,O,I,G\rangle } is a function

succ : 2 P × O → 2 P , {\displaystyle \operatorname {succ} :2^{P}\times O\rightarrow 2^{P},}

where 2 P {\displaystyle 2^{P}} is the set of all subsets of P {\displaystyle P} , and is therefore the set of all possible states. The transition function succ {\displaystyle \operatorname {succ} } for a state C ⊆ P {\displaystyle C\subseteq P} , can be defined as follows, using the simplifying assumption that actions can always be executed but have no effect if their preconditions are not met:

The function succ {\displaystyle \operatorname {succ} } can be extended to sequences of actions by the following recursive equations:

succ ⁡ ( C , [ ] ) = C {\displaystyle \operatorname {succ} (C,[\ ])=C}

succ ⁡ ( C , [ a 1 , a 2 , … , a n ] ) = succ ⁡ ( succ ⁡ ( C , a 1 ) , [ a 2 , … , a n ] ) {\displaystyle \operatorname {succ} (C,[a_{1},a_{2},\ldots ,a_{n}])=\operatorname {succ} (\operatorname {succ} (C,a_{1}),[a_{2},\ldots ,a_{n}])}

A plan for a STRIPS instance is a sequence of actions such that the state that results from executing the actions in order from the initial state satisfies the goal conditions. Formally, [ a 1 , a 2 , … , a n ] {\displaystyle [a_{1},a_{2},\ldots ,a_{n}]} is a plan for G = ⟨ N , M ⟩ {\displaystyle G=\langle N,M\rangle } if F = succ ⁡ ( I , [ a 1 , a 2 , … , a n ] ) {\displaystyle F=\operatorname {succ} (I,[a_{1},a_{2},\ldots ,a_{n}])} satisfies the following two conditions:

N ⊆ F {\displaystyle N\subseteq F}

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Stanford Research Institute Problem Solver

Start with the simplest possible case. Write down what Stanford Research Institute Problem Solver 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 Stanford Research Institute Problem Solver 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 Stanford Research Institute Problem Solver 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 Stanford Research Institute Problem Solver

In research
Stanford Research Institute Problem Solver 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 Stanford Research Institute Problem Solver 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
Stanford Research Institute Problem Solver is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1971 software, Automated planning and scheduling, History of artificial intelligence, so understanding it makes those chapters shorter.
In everyday life
Look for Stanford Research Institute Problem Solver 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 Stanford Research Institute Problem Solver in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Stanford Research Institute Problem Solver means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Stanford Research Institute Problem Solver out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Stanford Research Institute Problem Solver in simple terms?

The Stanford Research Institute Problem Solver, known by its acronym STRIPS, is an automated planner developed by Richard Fikes and Nils Nilsson in 1971 at SRI International. The same name was later used to refer to the formal language of the inputs to this planner.

Why does Stanford Research Institute Problem Solver 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 Stanford Research Institute Problem Solver?

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 Stanford Research Institute Problem Solver.

Tags

  • 1971 software
  • Automated planning and scheduling
  • History of artificial intelligence
  • SRI International software

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