Admissible heuristic

About: Admissible heuristic is a research topic. Over the lifetime, 197 publications have been published within this topic receiving 15329 citations. The topic is also known as: admissible heuristics.

Online Learning of Search Heuristics

Abstract: In this paper we learn heuristic functions that efficiently find the shortest path between two nodes in a graph. We rely on the fact that often, several elementary admissible heuristics might be provided, either by human designers or from formal domain abstractions. These simple heuristics are traditionally composed into a new admissible heuristic by selecting the highest scoring elementary heuristic in each distance evaluation. We suggest that learning a weighted sum over the elementary heuristics can often generate a heuristic with higher dominance than the heuristic defined by the highest score selection. The weights within our composite heuristic are trained in an online manner using nodes to which the true distance has already been revealed during previous search stages. Several experiments demonstrate that the proposed method typically finds the optimal path while significantly reducing the search complexity. Our theoretical analysis describes conditions under which finding the shortest path can be guaranteed.

Improved methods for scheduling flexible manufacturing systems based on Petri nets and heuristic search

Abstract: This paper proposes and evaluates two improved Petri net (PN)-based hybrid search strategies and their applications to flexible manufacturing system (FMS) scheduling. The algorithms proposed in some previous papers, which combine PN simulation capabilities with A * heuristic search within the PN reachability graph, may not find an optimum solution even with an admissible heuristic function. To remedy the defects an improved heuristic search strategy is proposed, which adopts a different method for selecting the promising markings and reserves the admissibility of the algorithm. To speed up the search process, another algorithm is also proposed which invokes faster termination conditions and still guarantees that the solution found is optimum. The scheduling results are compared through a simple FMS between our algorithms and the previous methods. They are also applied and evaluated in a set of randomly-generated FMSs with such characteristics as multiple resources and alternative routes.

Section Patterns: Efficiently Solving Narrow Passage Problems in Multilevel Motion Planning

Abstract: Sampling-based planning methods often become inefficient due to narrow passages. Narrow passages induce a higher runtime, because the chance to sample them becomes vanishingly small. In recent work, we showed that narrow passages can be approached by relaxing the problem using admissible lower-dimensional projections of the state space. Those relaxations often increase the volume of narrow passages under projection. Solving the relaxed problem is often efficient and produces an admissible heuristic we can exploit. However, given a base path, i.e. a solution to a relaxed problem, there are currently no tailored methods to efficiently exploit the base path. To efficiently exploit the base path and thereby its admissible heuristic, we develop section patterns, which are solution strategies to efficiently exploit base paths in particular around narrow passages. To coordinate section patterns, we develop the pattern dance algorithm, which efficiently coordinates section patterns to reactively traverse narrow passages. We combine the pattern dance algorithm with previously developed multilevel planning algorithms and benchmark them on challenging planning problems like the Bugtrap, the double L-shape, an egress problem and on four pregrasp scenarios for a 37 degrees of freedom shadow hand mounted on a KUKA LWR robot. Our results confirm that section patterns are useful to efficiently solve high-dimensional narrow passage motion planning problems.

Goal Programming and Heuristic Search

Abstract: The A* algorithm belongs to the class of best first graph-search algorithms and is frequently used to find minimum-cost paths. Recently, A* has been generalised to solve multiple-objective search problems, returning the set of non-dominated solution paths. This paper presents METAL-A*, an admissible heuristic search algorithm for search problems with lexicographic goals that returns the set of non-dominated paths that best achieves a set of lexicographic goals.

An Admissible Heuristic to Improve Convergence in Kinodynamic Planners Using Motion Primitives

Abstract: This letter introduces a new heuristic function that can be incorporated in any kinodynamic planner using motion primitives, to the purpose of increasing its convergence rate. The heuristic function is proven to be admissible and, hence, the optimality properties of the planning algorithm are preserved. Notably, it can be applied to planning problems with generic agent motion models and cost criteria, since it depends only on the database of motion primitives. The proposed heuristic has been integrated into a randomized sampling-based and a deterministic kinodynamic planner, and its effectiveness has been shown in numerical examples with different agent motion models and cost criteria.