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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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In artificial intelligence, hierarchical task network (HTN) planning is an approach to automated planning in which the dependency among actions can be given in the form of hierarchically structured networks. Planning problems are specified in the hierarchical task network approach by providing a set of tasks, which can be:
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.
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.
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 ...
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:
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