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Planning tasks succinctly represent labeled transition systems, with each ground action corresponding to a label. This granularity, however, is not necessary for solving planning tasks and can be harmful, especially for model-free methods. In this work, we propose automatic approach to reduce the label sets for planning domains.
Using artificial intelligence automated planning to efficiently execute space debris collection. Testing and evaluation results illustrated in the project readme
OWLS-FDplan is a service composition planner: Given a set of problem and/or domain described in OWL as input, as well as a service composition problem expressed in OWL-S 1.1, OWLS-FDplan produces a set of plans solving that problem.
KLEP (Key-Lock-Executable-Process) is a groundbreaking AI framework that utilizes symbolic AI for dynamic decision-making. It integrates keys, locks, executables, and processes to foster ethical, modular, and transparent AI applications, offering a novel approach for developers and researchers in AI and cognitive science.
An AI planning project based on the 2019 international university timetabling competition which used commercial software IBM CPLEX to generate feasible course timetables with minimum penalty.