Showing papers on "Admissible heuristic published in 2008"

Accuracy of admissible heuristic functions in selected planning domains

Abstract: The efficiency of optimal planning algorithms based on heuristic search crucially depends on the accuracy of the heuristic function used to guide the search. Often, we are interested in domain-independent heuristics for planning. In order to assess the limitations of domain-independent heuristic planning, we analyze the (in)accuracy of common domain-independent planning heuristics in the IPC benchmark domains. For a selection of these domains, we analytically investigate the accuracy of the h+ heuristic, the hm family of heuristics, and certain (additive) pattern database heuristics, compared to the perfect heuristic h*. Whereas h+and additive pattern database heuristics usually return cost estimates proportional to the true cost, non-additive hm and nonadditive pattern-database heuristics can yield results underestimating the true cost by arbitrarily large factors.

Partial Symbolic Pattern Databases for Optimal Sequential Planning

Abstract: This paper investigates symbolic heuristic search with BDDs for solving domain-independent action planning problems cost-optimally. By distributing the impact of operators that take part in several abstractions, multiple partial symbolic pattern databases are added for an admissible heuristic, even if the selected patterns are not disjoint. As a trade-off between symbolic bidirectional and heuristic search with BDDs on rather small pattern sets, partial symbolic pattern databases are applied.

A Heuristic Search Algorithm for Flow-Shop Scheduling

Abstract: This article describes the development of a new intelligent heuristic search algorithm (IHSA * ) which guarantees an optimal solution for flow-shop problems with an arbitrary number of jobs and machines provided the job sequence is constrained to be the same on each machine. The development is described in terms of 3 modifications made to the initial version of IHSA * . The first modification concerns the choice of an admissible heuristic function. The second concerns the calculation of heuristic estimates as the search for an optimal solution progresses, and the third determines multiple optimal solutions when they exist. The first 2 modifications improve performance characteristics of the algorithm and experimental evidence of these improvements is presented as well as instructive examples which illustrate the use of initial and final versions of IHSA * . *

Learning heuristic functions through approximate linear programming

Abstract: Planning problems are often formulated as heuristic search. The choice of the heuristic function plays a significant role in the performance of planning systems, but a good heuristic is not always available. We propose a new approach to learning heuristic functions from previously solved problem instances in a given domain. Our approach is based on approximate linear programming, commonly used in reinforcement learning. We show that our approach can be used effectively to learn admissible heuristic estimates and provide an analysis of the accuracy of the heuristic. When applied to common heuristic search problems, this approach reliably produces good heuristic functions.

A new admissible heuristic for the job shop scheduling problem with total flow time

Abstract: This work has been supported by the Spanish Ministry of Science and Education under research project MEC-FEDER TIN2007-67466-C02-01

LRTA* Works Much Better with Pessimistic Heuristics

Abstract: Recently we showed that under very reasonable conditions, incomplete, real-time search methods like RTA* work better with pessimistic heuristic functions than with optimistic, admissible heuristic functions of equal quality. The use of pessimistic heuristic functions results in higher percentage of correct decisions and in shorter solution lengths. We extend this result to learning RTA* (LRTA*) and demonstrate that the use of pessimistic instead of optimistic (or mixed) heuristic functions of equal quality results in much faster learning process at the cost of just marginally worse quality of converged solutions.