scispace - formally typeset
Search or ask a question

Showing papers on "Admissible heuristic published in 2015"


Proceedings Article
25 Jul 2015
TL;DR: This paper takes into account the weighted user preferences in route search, and presents a keyword coverage problem, which finds an optimal route from a source location to a target location such that the keyword coverage is optimized and that the budget score satisfies a specified constraint.
Abstract: The preferences of users are important in route search and planning. For example, when a user plans a trip within a city, their preferences can be expressed as keywords shopping mall, restaurant, and museum, with weights 0.5, 0.4, and 0.1, respectively. The resulting route should best satisfy their weighted preferences. In this paper, we take into account the weighted user preferences in route search, and present a keyword coverage problem, which finds an optimal route from a source location to a target location such that the keyword coverage is optimized and that the budget score satisfies a specified constraint. We prove that this problem is NP-hard. To solve this complex problem, we propose an optimal route search based on an A* variant for which we have defined an admissible heuristic function. The experiments conducted on real-world datasets demonstrate both the efficiency and accuracy of our proposed algorithms.

38 citations


Proceedings Article
07 Jun 2015
TL;DR: Objectives are explored that attempt to maximize heuristic estimates for all states (reachable and unreachable), maximize heuristics for a sample of reachable states, maximize the number of detected dead ends, or minimize search effort.
Abstract: Potential heuristics, recently introduced by Pommerening et al., characterize admissible and consistent heuristics for classical planning as a set of declarative constraints. Every feasible solution for these constraints defines an admissible heuristic, and we can obtain heuristics that optimize certain criteria such as informativeness by specifying suitable objective functions. The original paper only considered one such objective function: maximizing the heuristic value of the initial state. In this paper, we explore objectives that attempt to maximize heuristic estimates for all states (reachable and unreachable), maximize heuristic estimates for a sample of reachable states, maximize the number of detected dead ends, or minimize search effort. We also search for multiple heuristics with complementary strengths that can be combined to obtain even better heuristics.

36 citations


Journal ArticleDOI
TL;DR: This paper provides an admissible heuristic function which also considers the available time of shared machine resources and subparts during calculating the lower bound of the remaining time for unprocessed operations and proves it is more effective than prior ones.
Abstract: In flexible manufacturing system (FMS) scheduling problems, the production process and constraints can be modeled by Petri nets concisely. However, the search space for an optimal or suboptimal schedule will exponentially grow with the increase of problem size. In this paper, we consider the scheduling problems of FMS in the framework of timed-transition Petri nets. In the framework, each token owns a individual timestamp which faciliates the analysis of the concurrency characteristic of modeled system. To save search effort, we provide an admissible heuristic function which also considers the available time of shared machine resources and subparts during calculating the lower bound of the remaining time for unprocessed operations. We prove the heuristic function is more effective than prior ones. Thus, an optimal scheduling strategy can be obtained at much less effort. Several numerical experiments are provided to demonstrate the effect of the improved heuristic function.

22 citations


Journal ArticleDOI
TL;DR: It is shown that weighted lookahead outperforms an existing approach by Shimbo and Ishida but that it does not improve over existing approaches that do not use weights, and the generality of weighted update is incorporated in two other well-known real-time heuristic search algorithms: LRTA*-LS and daLSS-LRTA*, and it is proved solutions are w-optimal, and additional bounds on solution quality that in practice are tighter than w-optimality.

18 citations


Proceedings Article
07 Jun 2015
TL;DR: A distributed version of a state-of-the-art LM-Cut heuristic is proposed and it is shown that the distributed algorithm provides estimates provably equal to estimates of the centralized version computed on the global problem.
Abstract: Heuristics are a crucial component in modern planning systems. In optimal multiagent planning the state of the art is to compute the heuristic locally using only information available to a single agent. This approach has a major deficiency as the local shortest path can arbitrarily underestimate the true shortest path cost in the global problem. As a solution, we propose a distributed version of a state-of-the-art LM-Cut heuristic. We show that our distributed algorithm provides estimates provably equal to estimates of the centralized version computed on the global problem. We also evaluate the algorithm experimentally and show that on a number of domains, the distributed algorithm can significantly improve performance of a multiagent planner.

14 citations


Proceedings ArticleDOI
03 Nov 2015
TL;DR: An algorithm for optimal processing of time-dependent sequenced route queries in road networks that finds the fastest route between an origin and destination that passes through a sequence of points of interest belonging to each of the specified categories of interest.
Abstract: In this paper we present an algorithm for optimal processing of time-dependent sequenced route queries in road networks, i.e., given a road network where the travel time over an edge is time-dependent and a given ordered list of categories of interest, we find the fastest route between an origin and destination that passes through a sequence of points of interest belonging to each of the specified categories of interest. Our approach uses the A* search paradigm equipped with an admissible heuristic function, thus guaranteed to yield the optimal solution, along with a pruning scheme for further reducing the search space. Our experiments using a real data set have shown our proposed solution to be up to two orders of magnitude faster than a previous solution extended to handle time-dependency.

13 citations


Posted Content
TL;DR: In this paper, the authors present an algorithm for optimal processing of time-dependent sequenced route queries in road networks, i.e., given a road network where the travel time over an edge is timedependent and a given ordered list of categories of interest, they find the fastest route between an origin and destination that passes through a sequence of points of interest belonging to each of the specified categories.
Abstract: In this paper we present an algorithm for optimal processing of time-dependent sequenced route queries in road networks, i.e., given a road network where the travel time over an edge is time-dependent and a given ordered list of categories of interest, we find the fastest route between an origin and destination that passes through a sequence of points of interest belonging to each of the specified categories of interest. For instance, considering a city road network at a given departure time, one can find the fastest route between one's work and his/her home, passing through a bank, a supermarket and a restaurant, in this order. The main contribution of our work is the consideration of the time dependency of the network, a realistic characteristic of urban road networks, which has not been considered previously when addressing the optimal sequenced route query. Our approach uses the A* search paradigm that is equipped with an admissible heuristic function, thus guaranteed to yield the optimal solution, along with a pruning scheme for further reducing the search space. In order to compare our proposal we extended a previously proposed solution aimed at non-time dependent sequenced route queries, enabling it to deal with the time-dependency. Our experiments using real and synthetic data sets have shown our proposed solution to be up to two orders of magnitude faster than the temporally extended previous solution.

4 citations


Journal Article
TL;DR: It is proved that the proposed techniques are very efficient in reducing the computational time of the search to a reasonable amount and admissible heuristic length estimation helps to early detection of partial cycles which lead to unreasonable solutions.
Abstract: This paper elaborates the routing of cable cycle through available routes in a building in order to link a set of devices, in a most reasonable way. Despite of the similarities to other NP-hard routing problems, the only goal is not only to minimize the cost (length of the cycle) but also to increase the reliability of the path (in case of a cable cut) which is assessed by a risk factor. Since there is often a trade-off between the risk and length factors, a criterion for ranking candidates and deciding the most reasonable solution is defined. A set of techniques is proposed to perform an efficient and exact search among candidates. A novel graph is introduced to reduce the search-space, and navigate the search toward feasible and desirable solutions. Moreover, admissible heuristic length estimation helps to early detection of partial cycles which lead to unreasonable solutions. The results show that the method provides solutions which are both technically and financially reasonable. Furthermore, it is proved that the proposed techniques are very efficient in reducing the computational time of the search to a reasonable amount.

2 citations


Journal ArticleDOI
TL;DR: An alternative approach is suggested: to model the accuracy of admissible heuristic functions as the probability of the heuristic hill-climbing search algorithm to find the goal state in a number of steps that equals theHeuristic value of an arbitrary node.
Abstract: Comparing heuristics has been a major issue from the early days of heuristic search. From the very beginning, measures of the accuracy of heuristic functions were strongly based on the number of nodes generated, and they are often still based on it. In this work, an alternative approach is suggested: to model the accuracy of admissible heuristic functions as the probability of the heuristic hill-climbing search algorithm to find the goal state in a number of steps that equals the heuristic value of an arbitrary node. The resulting method serves to assess on the accuracy of both consistent and inconsistent heuristic functions. Comparisons with different sizes of the sliding-tile puzzle show that the model suggested predicts the accuracy of the heuristic function with a good precision. The resulting procedure is used to derive figures on the accuracy of a large number of heuristics for the 15-Puzzle with different variants of the search algorithm IDA?.

2 citations


Journal ArticleDOI
TL;DR: A linear programming based multivalued landmark heuristic hlpml which extracts and exploits multi-valued landmarks using a linear programming solver and is guaranteed to be admissible and can be computed in polynomial time is devised.
Abstract: Landmark based heuristics are among the most accurate current known admissible heuristics for cost optimal planning. A disjunctive action landmark can be seen as a form of at-least-one constraint on the actions it contains. In many domains, there are many critical propositions which have to be established for a number of times. Previous landmarks are too weak to express this kind of general cardinality constraints. In this paper, we propose to generalize landmarks to multi-valued landmarks to model general cardinality constraints in cost optimal planning. We show existence of complete multi-valued landmark sets by explicitly constructing complete multi-valued action landmark sets for general planning tasks. Because exact lower bounds of general multi-valued action landmarks are intractable to extract and exploit, we introduce multi-valued proposition landmarks from which multi-valued action landmarks can be efficiently induced. Finally, we devise a linear programming based multivalued landmark heuristic hlpml which extracts and exploits multi-valued landmarks using a linear programming solver. hlpml is guaranteed to be admissible and can be computed in polynomial time. Experimental evaluation on benchmark domains shows hlpml beats state-of-the-art admissible heuristic in terms of heuristic accuracy and achieves better overall coverage performance at the cost of using more CPU time.

2 citations