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In [[artificial intelligence]], '''preference-based planning''' is a form of [[automated planning and scheduling]] which focuses on producing plans that achieve outcomes in a way that also satisfies user-specified [[preference]]s insofar as possible. In many [[problem ___domain]]s, a task can be accomplished by various sequences of actions (also known as plans). These plans can however vary in quality: there can be many ways to solve a problem but one generally prefers a way that is, e.g., cost-effective, quick and safe.
Preference-based planners take these preferences into account when producing a plan for a given problem.
== Overview ==
Preferences can be regarded as soft contraints 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 a single plan. This differs from hard constraints which must be satisfied in all plans produced by the planning software.
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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 [[Starbucks]] first and then continue to school.<!-- please cite, I forgot which paper had this as example -->
== Planning Domain Definition Language ==
The [[Planning Domain Definition Language]] supports the specification of preferences through <code>preference</code> statements: for example, the statement <code>(preference (always (clean room1)))</code> indicates that the user prefers that <code>room1</code> should be clean at each state of the plan. In other words, the planner should not schedule an action that causes <code>room1</code> to become dirty.
==References==
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