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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 ...
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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.
Automated decision-making involves using data as input to be analyzed within a process, model, or algorithm or for learning and generating new models. [7] ADM systems may use and connect a wide range of data types and sources depending on the goals and contexts of the system, for example, sensor data for self-driving cars and robotics, identity data for security systems, demographic and ...
PDDL is a human-readable format for problems in automated planning that gives a description of the possible states of the world, a description of the set of possible actions, a specific initial state of the world, and a specific set of desired goals. Action descriptions include the prerequisites of the action and the effects of the action.
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.