science

Preference-based planning

Preference-based 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 Preference-based planning rather than just read about it. In short: In artificial intelligence, preference-based planning is a form of automated planning and scheduling which focuses on producing plans that additionally satisfy as many user-specified preferences as possible. In many problem domains, a task can be accomplished by various sequences of actions (also known as plans).

Key takeaways

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

Reference excerpt

In artificial intelligence, preference-based planning is a form of automated planning and scheduling which focuses on producing plans that additionally satisfy as many user-specified preferences as possible. In many problem domains, a task can be accomplished by various sequences of actions (also known as plans). These plans can vary in quality: there can be many ways to solve a problem, but preferred generally are ways more, e.g., cost-effective, quick, and safe. Preference-based planners take these preferences into account when producing a plan for a given problem. Examples of preference-based planning software include PPLAN and HTNPlan-P (preference-based hierarchical task network (HTN) planning).

Overview Preferences can be regarded as soft constraints on a plan. The quality of a plan increases when more preferences are satisfied but it may not be possible to satisfy all preferences in one plan. This differs from hard constraints which must be satisfied in all plans produced by the planning software. These hard constraints are part of the domain knowledge while the soft constraints (or preferences) are separately specified by the user. This allows the same domain knowledge to be reused for various users who may have different preferences. The use of preferences may also increase the length of a plan in order to satisfy more preferences. For example, when planning a journey from home to school, the user may prefer to buy a cup of coffee along the way. The planning software could now plan to visit the coffee shop first and then continue to school. This increases the length of the plan but the user's preference is satisfied.

Planning Domain Definition Language The Planning Domain Definition Language, as of version 3.0, supports the specification of preferences through preference statements. For example, the statement

(preference (always (clean room1))) indicates that the user prefers that room1 should be clean at each state of the plan. In other words, the planner should not schedule an action that causes room1 to become dirty. As this example shows, a preference is evaluated with regard to all states of a plan (if semantically required). In addition to always, other constructs based on linear temporal logic are also supported, such as sometime (at least once during the plan), sometime-after (to be planned after a particular state) and at-most-once (the preference holds during at most one sequence of states in the plan).

Plan quality In addition to determining whether a preference is satisfied, we also need to compute the quality of a plan based on how many preferences are satisfied. For this purpose, PDDL 3.0 includes an expression called is-violated <name> which is equal to "the number of distinct preferences with the given name that are not satisfied in the plan". For a plan, a value can now be computed using a metric function, which is specified with :metric:

(:metric minimize (+ (* 5 (is-violated pref1)) (* 7 (is-violated pref2)))) This example metric function specifies that the calculated value of the plan should be minimized (i.e., a plan with value v1 and a plan with value v2 such that v1 < v2, the former plan is strictly preferred). The value of a plan is computed by the given function, which is expressed in Polish notation. In this case, violation of the second preference, pref2, has been given a greater penalty than the first preference, pref1.

Constraints satisfaction problem In the area of constraint satisfaction problems, flexible variants exist that deal with soft constraints in a similar way to preferences in preference-based planning.

References

Worked examples

Example 1 — a first encounter with Preference-based planning

Start with the simplest possible case. Write down what Preference-based 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 Preference-based 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 Preference-based 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 Preference-based planning

In research: Preference-based 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 Preference-based 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: Preference-based planning is common in secondary-school and first-year university syllabi. It links to neighbouring topics Automated planning and scheduling, Strategic management, so understanding it makes those chapters shorter.
In everyday life: Look for Preference-based 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 Preference-based planning in 20 minutes

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

Frequently asked questions

What is Preference-based planning in simple terms?

Why does Preference-based 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 Preference-based 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 Preference-based planning.