Tightest Admissible Shortest Path: Optimizing Edge-Weight Uncertainty in Graph Search

Core Concepts

The tightest admissible shortest path (TASP) problem aims to find the path with the tightest suboptimality bound on the optimal cost in a graph with uncertain edge weights.

Abstract

The content introduces the TASP problem, which is a generalization of the classic shortest path problem to settings with bounded edge-weight uncertainty. In this framework, each edge has multiple weight estimators that provide lower and upper bounds on the true weight, with increasing accuracy and computational cost. The key insights are: TASP can be solved by optimally solving two related problems: the shortest path tightest lower bound (SLB) and the shortest path tightest upper bound (SUB). The BEAST algorithm is presented to optimally solve the SUB problem, by dynamically applying cost estimators during a best-first search. BEAUTY&BEAST combines the solutions of SLB and SUB to efficiently solve TASP, taking advantage of the coupling between the two problems. Empirical evaluation on diverse planning benchmark problems demonstrates the effectiveness of the proposed algorithms in reducing the number of expensive edge-weight computations compared to a baseline.

Stats

The optimal cost C* is unknown, but the tightest lower bound L* and tightest upper bound U* can be computed. The tightest admissibility factor B* is given by U*/L*.

Quotes

"The tightest admissible shortest path (TASP) problem is to find B* as well as a solution π that its B-admissibility factor is B*." "If L* > 0 for P, then a solution path π for P is a tightest admissible shortest path iff it is a shortest path tightest upper bound (i.e. a solution that achieves U*)."

Key Insights Distilled From

Tightest Admissible Shortest Path

by Eyal Weiss,A... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2308.08453.pdf

Deeper Inquiries

How can the proposed algorithms be extended to handle state-dependent edge-weight estimates

To extend the proposed algorithms to handle state-dependent edge-weight estimates, we can modify the cost estimators function $\Theta$ to incorporate state information. Instead of having a single set of estimators for all edges, we can have different sets of estimators for each edge based on the state in which the edge is being evaluated. This way, the estimators can adapt to the specific characteristics of the state, providing more accurate and relevant cost estimates. By incorporating state information into the estimation process, the algorithms can make more informed decisions during the search, leading to better performance and more accurate solutions.

What are the theoretical and practical implications of having L* = 0 and U* > 0, where B* = ∞ and no finite-bounded solution exists

Having $L^* = 0$ and $U^* > 0$ implies that there is a discrepancy between the tightest lower bound and the tightest upper bound on the optimal cost. In this scenario, where the lower bound is zero and the upper bound is positive, it indicates that there is uncertainty in the cost estimates, and the true optimal cost lies somewhere between these bounds. The theoretical implication of $B^* = \infty$ in this case is that there is no finite suboptimality factor that can guarantee an admissible solution. This means that no finite-bounded solution exists within the uncertainty bounds provided by the estimators. From a practical standpoint, this situation poses a challenge in finding a valid solution within the given uncertainty constraints. It indicates that the estimators used may not be accurate enough to provide a reliable estimate of the optimal cost. In such cases, additional refinement of the estimators or the search strategy may be necessary to improve the accuracy of the cost estimates and find a feasible solution.

Can the coupling between SLB and SUB be further exploited to develop even more efficient algorithms for solving TASP problems

The coupling between SLB and SUB can be further exploited to develop more efficient algorithms for solving TASP problems by leveraging the information obtained from solving both problems. One approach could be to use the insights gained from the solutions of SLB and SUB to guide the search process more effectively. For example, the algorithm could prioritize expanding nodes that have shown promising results in terms of tight upper and lower bounds, leading to a more focused exploration of the search space. Additionally, the coupling between SLB and SUB can be used to refine the cost estimation process iteratively. By iteratively updating the estimators based on the solutions obtained from SLB and SUB, the algorithms can adapt and improve the accuracy of the cost estimates over time. This adaptive approach can lead to better-informed decisions during the search, ultimately resulting in more efficient and effective solutions to TASP problems.