An investigation into dynamic yard crane deployment and comparisons between hill-climbing and best-first-search algorithms

2008 ◽  
Vol 32 (4) ◽  
pp. 254 ◽  
Author(s):  
Wei Yan ◽  
Youfang Huang ◽  
Junliang He
Keyword(s):  
Search Algorithms ◽  
Hill Climbing ◽  
Best First Search

scholarly journals Online Relaxation Refinement for Satisficing Planning: On Partial Delete Relaxation, Complete Hill-Climbing, and Novelty Pruning

2022 ◽  
Vol 73 ◽  
Author(s):  
Maximilian Fickert ◽  
Jörg Hoffmann
Keyword(s):  
Local Minimum ◽  
State Of The Art ◽  
Convergence Property ◽  
Search Space ◽  
Search Algorithms ◽  
Hill Climbing ◽  
Ai Planning ◽  
Best First Search ◽  
Refinement Procedure ◽  
Hill Climbing Algorithms

In classical AI planning, heuristic functions typically base their estimates on a relaxation of the input task. Such relaxations can be more or less precise, and many heuristic functions have a refinement procedure that can be iteratively applied until the desired degree of precision is reached. Traditionally, such refinement is performed offline to instantiate the heuristic for the search. However, a natural idea is to perform such refinement online instead, in situations where the heuristic is not sufficiently accurate. We introduce several online-refinement search algorithms, based on hill-climbing and greedy best-first search. Our hill-climbing algorithms perform a bounded lookahead, proceeding to a state with lower heuristic value than the root state of the lookahead if such a state exists, or refining the heuristic otherwise to remove such a local minimum from the search space surface. These algorithms are complete if the refinement procedure satisfies a suitable convergence property. We transfer the idea of bounded lookaheads to greedy best-first search with a lightweight lookahead after each expansion, serving both as a method to boost search progress and to detect when the heuristic is inaccurate, identifying an opportunity for online refinement. We evaluate our algorithms with the partial delete relaxation heuristic hCFF, which can be refined by treating additional conjunctions of facts as atomic, and whose refinement operation satisfies the convergence property required for completeness. On both the IPC domains as well as on the recently published Autoscale benchmarks, our online-refinement search algorithms significantly beat state-of-the-art satisficing planners, and are competitive even with complex portfolios.

A Computer Simulation Model for Determining Fleet Size and Composition

1977 ◽  
Vol 99 (1) ◽  
pp. 157-161
Author(s):  
G. C. Schultz ◽  
E. E. Enscore
Keyword(s):  
Computer Simulation ◽  
Simulation Model ◽  
Search Algorithms ◽  
The Other ◽  
Hill Climbing ◽  
Computer Simulation Model ◽  
Fleet Size ◽  
Travel Costs ◽  
Total Vehicle ◽  
Vehicle Fleet

A heterogeneous vehicle fleet is one that is composed of several types of vehicles. The number of each type of vehicle in the fleet is called the fleet’s composition. The problem of determining the best fleet size and composition for an in-house heterogeneous company fleet having a known demand was solved in this paper. A computer model was developed which tied a fleet simulation model to two different search algorithms. One of the search algorithms is a complete factorial nonsequential search and the other is a combination of a partial factorial nonsequential search and a heuristic sequential hill-climbing search. The objective of both searches is to select the fleet size and composition which provides the lowest total vehicle travel costs to the company. Several examples were used to demonstrate the use of the model.

scholarly journals Effective Heuristics for Suboptimal Best-First Search

2016 ◽  
Vol 57 ◽  
pp. 273-306 ◽  
Author(s):  
Christopher Wilt ◽  
Wheeler Ruml
Keyword(s):  
Sustained Attention ◽  
Heuristic Search ◽  
Rank Correlation ◽  
Search Algorithms ◽  
Optimal Solutions ◽  
Heuristic Function ◽  
Optimal Search ◽  
Heuristic Search Algorithms ◽  
Best First Search ◽  
Goal Distance

Suboptimal heuristic search algorithms such as weighted A* and greedy best-first search are widely used to solve problems for which guaranteed optimal solutions are too expensive to obtain. These algorithms crucially rely on a heuristic function to guide their search. However, most research on building heuristics addresses optimal solving. In this paper, we illustrate how established wisdom for constructing heuristics for optimal search can fail when considering suboptimal search. We consider the behavior of greedy best-first search in detail and we test several hypotheses for predicting when a heuristic will be effective for it. Our results suggest that a predictive characteristic is a heuristic's goal distance rank correlation (GDRC), a robust measure of whether it orders nodes according to distance to a goal. We demonstrate that GDRC can be used to automatically construct abstraction-based heuristics for greedy best-first search that are more effective than those built by methods oriented toward optimal search. These results reinforce the point that suboptimal search deserves sustained attention and specialized methods of its own.

scholarly journals Characterizing the Nature of Probability-Based Proof Number Search: A Case Study in the Othello and Connect Four Games

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 264
Author(s):  
Anggina Primanita ◽  
Mohd Nor Akmal Khalid ◽  
Hiroyuki Iida
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Game Tree ◽  
Tree Structures ◽  
Best First Search

Variants of best-first search algorithms and their expansions have continuously been introduced to solve challenging problems. The probability-based proof number search (PPNS) is a best-first search algorithm that can be used to solve positions in AND/OR game tree structures. It combines information from explored (based on winning status) and unexplored (through Monte Carlo simulation) nodes from a game tree using an indicator called the probability-based proof number (PPN). In this study, PPNS is employed to solve randomly generated positions in Connect Four and Othello, in which the results are compared with the two well-known best-first search algorithms (proof number search (PNS) and Monte Carlo proof number search). Adopting a simple improvement parameter in PPNS reduces the number of nodes that need to be explored by up to 57%. Moreover, further observation showed the varying importance of information from explored and unexplored nodes in which PPNS relies critically on the combination of such information in earlier stages of the Othello game. Discussion and insights from these findings are provided where the potential future works are briefly described.

scholarly journals Parallel best-first search algorithms for planning problems on multi-core processors

2021 ◽  
Author(s):  
Didier El Baz ◽  
Bilal Fakih ◽  
Romeo Sanchez Nigenda ◽  
Vincent Boyer
Keyword(s):  
Search Algorithms ◽  
Best First Search ◽  
Planning Problems

scholarly journals Finding Optimal Algorithm in Artificial Intelligence

2021 ◽  
Vol 10 (7) ◽  
pp. 84-90
Author(s):  
I.Parvin Begum ◽  
I.Shahina Begam
Keyword(s):  
Artificial Intelligence ◽  
Shortest Path ◽  
Heuristic Search ◽  
Optimal Algorithm ◽  
Search Algorithms ◽  
Search Procedure ◽  
Breadth First Search ◽  
Search Techniques ◽  
Best First Search ◽  
Blind Search

Present days many artificial intelligence search algorithms are plays a important to figure out the problem of shortest path finding. The paper presents the detailed study of heuristic search and blind search techniques. The paper focus additional in the direction of blind search strategies such as Breadth First Search, Depth First Search, and Uniform Cost Search and informed explore strategies like A*, and Best First Search. The paper consist of effective of search procedure, their qualities, and demerits, where these algorithms are applicable, also at last comparison of search techniques based on complexity, optimality and completeness are presented in tabular structure.

scholarly journals Penerapan Metode Steepest Ascent Hill Climb pada Permainan Puzzle

2018 ◽  
Vol 3 (2) ◽  
pp. 36
Author(s):  
Hairul Anam ◽  
Feby Sabilhul Hanafi ◽  
Ahmad Fauzal Adifia ◽  
Ahmad Firdaus Ababil ◽  
Saiful Bukhori
Keyword(s):  
Artificial Intelligence ◽  
Optimization Problems ◽  
Search Algorithms ◽  
Hill Climbing ◽  
Optimal Time ◽  
Hill Climbing Algorithm ◽  
Steepest Ascent ◽  
The Hill

Puzzle is one example of the application of artificial intelligence, in the process of completion there are many search algorithms that can be applied. The 8 puzzle solution will be faster obtained if the array principle is used with a variation of the Steepest-Ascent Hill Climbing (Hill Climbing algorithm by choosing the sharpest / steepest slope) with the correct heuristic parameters and distance heuristics and combined with LogList as the storage state ever passed to overcome the problems in the hill climbing algorithm itself and avoid the looping state that has been passed. Steepest Ascent Hill Climbing is an algorithm method that is widely used for optimization problems. The application of the SAHC (Steepest Ascent Hill Climbing) Algorithm to the puzzle is needed so that the game is completed with optimal time.

scholarly journals Solving Large Problems with Heuristic Search: General-Purpose Parallel External-Memory Search

2018 ◽  
Vol 62 ◽  
pp. 233-268 ◽  
Author(s):  
Matthew Hatem ◽  
Ethan Burns ◽  
Wheeler Ruml
Keyword(s):  
Cost Function ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Heuristic Search ◽  
Memory Search ◽  
Search Algorithms ◽  
General Purpose ◽  
Theory And Practice ◽  
Multiple Sequence ◽  
Best First Search

Classic best-first heuristic search algorithms, like A*, record every unique state they encounter in RAM, making them infeasible for solving large problems. In this paper, we demonstrate how best-first search can be scaled to solve much larger problems by exploiting disk storage and parallel processing and, in some cases, slightly relaxing the strict best-first node expansion order. Some previous disk-based search algorithms abandon best-first search order in an attempt to increase efficiency. We present two case studies showing that A*, when augmented with Delayed Duplicate Detection, can actually be more efficient than these non-best-first search orders. First, we present a straightforward external variant of A*, called PEDAL, that slightly relaxes best-first order in order to be I/O efficient in both theory and practice, even on problems featuring real-valued node costs. Because it is easy to parallelize, PEDAL can be faster than in-memory IDA* even on domains with few duplicate states, such as the sliding-tile puzzle. Second, we present a variant of PEDAL, called PE2A*, that uses partial expansion to handle problems that have large branching factors. When tested on the problem of Multiple Sequence Alignment, PE2A* is the first algorithm capable of solving the entire Reference Set 1 of the standard BAliBASE benchmark using a biologically accurate cost function. This work shows that classic best-first algorithms like A* can be applied to large real-world problems. We also provide a detailed implementation guide with source code both for generic parallel disk-based best-first search and for Multiple Sequence Alignment with a biologically accurate cost function. Given its effectiveness as a general-purpose problem-solving method, we hope that this makes parallel and disk-based search accessible to a wider audience.

scholarly journals Search Progress and Potentially Expanded States in Greedy Best-First Search

2018 ◽  
Author(s):  
Manuel Heusner ◽  
Thomas Keller ◽  
Malte Helmert
Keyword(s):  
Classical Result ◽  
Search Behavior ◽  
Optimal Solution ◽  
Search Space ◽  
High Water ◽  
Search Algorithms ◽  
Clear Understanding ◽  
Optimal Search ◽  
High Water Mark ◽  
Best First Search

A classical result in optimal search shows that A* with an admissible and consistent heuristic expands every state whose f-value is below the optimal solution cost and no state whose f-value is above the optimal solution cost. For satisficing search algorithms, a similarly clear understanding is currently lacking. We examine the search behavior of greedy best-first search (GBFS) in order to make progress towards such an understanding. We introduce the concept of high-water mark benches, which separate the search space into areas that are searched by a GBFS algorithm in sequence. High-water mark benches allow us to exactly determine the set of states that are expanded by at least one GBFS tie-breaking strategy and give us a clearer understanding of search progress.

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