Autor/Autorin: Gilon Daniel
Daniel Gilon holds a M.Sc degree in Information System Engineering from the Ben-Gurion University. His research interests are Algorithm Development for combinatoric problems and Games, especially when looking for a solution that is not optimal.
Potential Search (PS) is an algorithm that is designed to solve bounded cost search problems. In bounded cost search we are given a fixed cost-bound and the task is to find a solution (if one exists) with a cost lower than the given bound. A bounded suboptimal search problem is similar to bounded suboptimal search in the manner that it also has an upper bound on the desired solution, but here the bound is given relative to the optimal solution and is not fixed, hence bounded suboptimal. we give a general rule on how to migrate algorithms that were designed to solve bounded cost search problems into ones that can solve bounded suboptimal search problems and vice versa. We show that this can be done for most of the known algorithms and thus improve our understanding of their relation and difference. In this book, we modify PS to work within the framework of bounded suboptimal search and introduce Dynamic Potential Search (DPS). DPS uses the idea of PS but modifies the cost-bound to be the product of the minimal f-value in OPEN and the required suboptimal bound.
Über den Autor
Daniel Gilon holds a M.Sc degree in Information System Engineering from the Ben-Gurion University. His research interests are Algorithm Development for combinatoric problems and Games, especially when looking for a solution that is not optimal.
Klappentext
Potential Search (PS) is an algorithm that is designed to solve bounded cost search problems. In bounded cost search we are given a fixed cost-bound and the task is to find a solution (if one exists) with a cost lower than the given bound. A bounded suboptimal search problem is similar to bounded suboptimal search in the manner that it also has an upper bound on the desired solution, but here the bound is given relative to the optimal solution and is not fixed, hence bounded suboptimal. we give a general rule on how to migrate algorithms that were designed to solve bounded cost search problems into ones that can solve bounded suboptimal search problems and vice versa. We show that this can be done for most of the known algorithms and thus improve our understanding of their relation and difference. In this book, we modify PS to work within the framework of bounded suboptimal search and introduce Dynamic Potential Search (DPS). DPS uses the idea of PS but modifies the cost-bound to be the product of the minimal f-value in OPEN and the required suboptimal bound.
