Conventional BEADS makes use of trial-and-error strategies to set up the working parameters, which makes the process slow and inconsistent.
We formally characterize different planning problems, where solutions have a chance of success ("weak planning"), are guaranteed to achieve the goal ("strong planning"), or achieve the goal with iterative trial-and-error strategies ("strong cyclic planning").
The accumulating effects of dimensional and geometric variability in modular construction have traditionally been managed using trial and error strategies and use of standardized tolerance values for similar stick-built construction scenarios.
Currently, many approaches, such as experience-based trial-and-error strategies, finite element methods, and expert systems, are applied to improve both quality and efficiency of roll pass design.
However, especially in cases where we are confronted with moderately symptomatic and more severe episodes, finding the right solution is difficult, and trial and error strategies are still a daily occurrence.
Crucially, the robots adapt through an "intelligent trial and error" strategy that allows them to cope with new challenges independently, rather than being pre-programmed to deal with every imaginable obstacle.
Various minimization strategies have been proposed for the least squares error strategy.
Finally, the optimal neural structure is attained using trial and error strategy.
Particle swarm optimization, more widely, the swarm intelligence algorithms are based on the "trail and error" strategy.
Therefore, the trial-and-error strategy is taken to find the most preferred model among alternative updated models resulted from varying weighting factors.
