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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 ...
The function can be extended to sequences of actions by the following recursive equations: (, [ ]) = (, [,, …,]) = ( (,), [, …,]) A plan for a STRIPS instance is a sequence of actions such that the state that results from executing the actions in order from the initial state satisfies the goal conditions.
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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.
The name graphplan is due to the use of a novel planning graph, to reduce the amount of search needed to find the solution from straightforward exploration of the state space graph. In the state space graph: the nodes are possible states, and the edges indicate reachability through a certain action. On the contrary, in Graphplan's planning graph:
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.