Search Results for author:
Found 4 papers, 0 papers with code
A study on a Q-Learning algorithm application to a manufacturing assembly problem
This study focuses on the implementation of a reinforcement learning algorithm in an assembly problem of a given object, aiming to identify the effectiveness of the proposed approach in the optimisation of the assembly process time.
Deep reinforcement learning applied to an assembly sequence planning problem with user preferences
The results support the potential for the application of deep reinforcement learning in assembly sequence planning problems with human interaction.
DistMS: A Non-Portfolio Distributed Solver for Maximum Satisfiability
The most successful parallel SAT and MaxSAT solvers follow a portfolio approach, where each thread applies a different algorithm (or the same algorithm configured differently) to solve a given problem instance.
Exploiting Resolution-based Representations for MaxSAT Solving
Usually, these MaxSAT algorithms perform better when small unsatisfiable subformulas are found early.
