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Automated planning and scheduling, sometimes denoted as simply AI planning, [1] is a branch of artificial intelligence that concerns the realization of strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehicles.
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O-Plan, Open Planning Architecture [4] UMCP, the first probably sound and complete HTN planning systems. [5] I-X/I-Plan [6] SHOP2, a HTN-planner developed at University of Maryland, College Park. [7] PANDA, a system designed for hybrid planning, an extension of HTN planning developed at Ulm University, Germany. [8] HTNPlan-P, preference-based ...
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.
The Planning Domain Definition Language (PDDL) was developed mainly to make the 1998/2000 International Planning Competition possible, and then evolved with each competition. PDDL is an attempt to standardize Artificial Intelligence (AI) planning languages.
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).
Satplan (better known as Planning as Satisfiability) is a method for automated planning. It converts the planning problem instance into an instance of the Boolean satisfiability problem , which is then solved using a method for establishing satisfiability such as the DPLL algorithm or WalkSAT .
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