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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.
Pages in category "Automated planning and scheduling" The following 28 pages are in this category, out of 28 total. This list may not reflect recent changes. ...
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.
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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The scheduler is an operating system module that selects the next jobs to be admitted into the system and the next process to run. Operating systems may feature up to three distinct scheduler types: a long-term scheduler (also known as an admission scheduler or high-level scheduler), a mid-term or medium-term scheduler, and a short-term scheduler.