scholarly journals Front-to-End Bidirectional Heuristic Search with Near-Optimal Node Expansions

2017 ◽  
Author(s):  
Jingwei Chen ◽  
Robert C. Holte ◽  
Sandra Zilles ◽  
Nathan R. Sturtevant
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Algorithms ◽  
Worst Case ◽  
Heuristic Search Algorithm ◽  
Bidirectional Search ◽  
Optimal Node ◽  
Minimum Number ◽  
Better Than

It is well-known that any admissible unidirectional heuristic search algorithm must expand all states whose f-value is smaller than the optimal solution cost when using a consistent heuristic. Such states are called “surely expanded” (s.e.). A recent study characterized s.e. pairs of states for bidirectional search with consistent heuristics: if a pair of states is s.e. then at least one of the two states must be expanded. This paper derives a lower bound, VC, on the minimum number of expansions required to cover all s.e. pairs, and present a new admissible front-to-end bidirectional heuristic search algorithm, Near-Optimal Bidirectional Search (NBS), that is guaranteed to do no more than 2VC expansions. We further prove that no admissible front-to-end algorithm has a worst case better than 2VC. Experimental results show that NBS competes with or outperforms existing bidirectional search algorithms, and often outperforms A* as well.

scholarly journals Anytime Heuristic Search

2007 ◽  
Vol 28 ◽  
pp. 267-297 ◽  
Author(s):  
E. A. Hansen ◽  
R. Zhou
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Search Time ◽  
Optimal Solution ◽  
Search Problem ◽  
A Algorithm ◽  
Solution Quality ◽  
Multiple Sequence ◽  
Anytime Algorithm ◽  
Heuristic Search Algorithm

We describe how to convert the heuristic search algorithm A* into an anytime algorithm that finds a sequence of improved solutions and eventually converges to an optimal solution. The approach we adopt uses weighted heuristic search to find an approximate solution quickly, and then continues the weighted search to find improved solutions as well as to improve a bound on the suboptimality of the current solution. When the time available to solve a search problem is limited or uncertain, this creates an anytime heuristic search algorithm that allows a flexible tradeoff between search time and solution quality. We analyze the properties of the resulting Anytime A* algorithm, and consider its performance in three domains; sliding-tile puzzles, STRIPS planning, and multiple sequence alignment. To illustrate the generality of this approach, we also describe how to transform the memory-efficient search algorithm Recursive Best-First Search (RBFS) into an anytime algorithm.

scholarly journals Scrubbing During Learning In Real-time Heuristic Search

2016 ◽  
Vol 57 ◽  
pp. 307-343 ◽  
Author(s):  
Nathan R. Sturtevant ◽  
Vadim Bulitko
Keyword(s):  
State Space ◽  
Real Time ◽  
Lower Bounds ◽  
Heuristic Search ◽  
Search Algorithm ◽  
The State ◽  
Worst Case ◽  
Heuristic Search Algorithm ◽  
Practical Performance ◽  
Theoretical Results

Real-time agent-centered heuristic search is a well-studied problem where an agent that can only reason locally about the world must travel to a goal location using bounded computation and memory at each step. Many algorithms have been proposed for this problem and theoretical results have also been derived for the worst-case performance with simple examples demonstrating worst-case performance in practice. Lower bounds, however, have not been widely studied. In this paper we study best-case performance more generally and derive theoretical lower bounds for reaching the goal using LRTA*, a canonical example of a real-time agent-centered heuristic search algorithm. The results show that, given some reasonable restrictions on the state space and the heuristic function, the number of steps an LRTA*-like algorithm requires to reach the goal will grow asymptotically faster than the state space, resulting in ``scrubbing'' where the agent repeatedly visits the same state. We then show that while the asymptotic analysis does not hold for more complex real-time search algorithms, experimental results suggest that it is still descriptive of practical performance.

scholarly journals Machine Learning Based Heuristic Search Algorithms to Solve Birds of a Feather Card Game

2019 ◽  
Vol 33 ◽  
pp. 9656-9661
Author(s):  
Bryon Kucharski ◽  
Azad Deihim ◽  
Mehmet Ergezer
Keyword(s):  
Machine Learning ◽  
Undergraduate Students ◽  
Heuristic Search ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Perfect Information ◽  
Monte Carlo Tree Search ◽  
Heuristic Search Algorithms ◽  
Heuristic Search Algorithm

This research was conducted by an interdisciplinary team of two undergraduate students and a faculty to explore solutions to the Birds of a Feather (BoF) Research Challenge. BoF is a newly-designed perfect-information solitaire-type game. The focus of the study was to design and implement different algorithms and evaluate their effectiveness. The team compared the provided depth-first search (DFS) to heuristic algorithms such as Monte Carlo tree search (MCTS), as well as a novel heuristic search algorithm guided by machine learning. Since all of the studied algorithms converge to a solution from a solvable deal, effectiveness of each approach was measured by how quickly a solution was reached, and how many nodes were traversed until a solution was reached. The employed methods have a potential to provide artificial intelligence enthusiasts with a better understanding of BoF and novel ways to solve perfect-information games and puzzles in general. The results indicate that the proposed heuristic search algorithms guided by machine learning provide a significant improvement in terms of number of nodes traversed over the provided DFS algorithm.

scholarly journals Heuristic Search Algorithm for Dimensionality Reduction Optimally Combining Feature Selection and Feature Extraction

2019 ◽  
Vol 33 ◽  
pp. 2280-2287 ◽  
Author(s):  
Baokun He ◽  
Swair Shah ◽  
Crystal Maung ◽  
Gordon Arnold ◽  
Guihong Wan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Heuristic Search ◽  
Search Algorithm ◽  
A Priori ◽  
Optimal Solution ◽  
Approximate Solutions ◽  
Combinatorial Search ◽  
Heuristic Search Algorithm

The following are two classical approaches to dimensionality reduction: 1. Approximating the data with a small number of features that exist in the data (feature selection). 2. Approximating the data with a small number of arbitrary features (feature extraction). We study a generalization that approximates the data with both selected and extracted features. We show that an optimal solution to this hybrid problem involves a combinatorial search, and cannot be trivially obtained even if one can solve optimally the separate problems of selection and extraction. Our approach that gives optimal and approximate solutions uses a “best first” heuristic search. The algorithm comes with both an a priori and an a posteriori optimality guarantee similar to those that can be obtained for the classical weighted A* algorithm. Experimental results show the effectiveness of the proposed approach.

scholarly journals Enriching Non-Parametric Bidirectional Search Algorithms

2019 ◽  
Vol 33 ◽  
pp. 2379-2386
Author(s):  
Shahaf S. Shperberg ◽  
Ariel Felner ◽  
Nathan R. Sturtevant ◽  
Solomon E. Shimony ◽  
Avi Hayoun
Keyword(s):  
Search Algorithm ◽  
Search Algorithms ◽  
Optimal Solutions ◽  
Worst Case ◽  
Bidirectional Search ◽  
Algorithmic Framework ◽  
Special Case ◽  
Non Parametric

NBS is a non-parametric bidirectional search algorithm proven to expand at most twice the number of node expansions required to verify the optimality of a solution. We introduce new variants of NBS that are aimed at finding all optimal solutions. We then introduce an algorithmic framework that includes NBS as a special case. Finally, we introduce DVCBS, a new algorithm in this framework that aims to further reduce the number of expansions. Unlike NBS, DVCBS does not have any worst-case bound guarantees, but in practice it outperforms NBS in verifying the optimality of solutions.

scholarly journals A Meta-Heuristic Search Algorithm based on Infrasonic Mating Displays in Peafowls

2021 ◽  
pp. 98-106
Author(s):  
Kenekayoro Patrick
Keyword(s):  
Search Algorithm ◽  
Mating Behaviour ◽  
Gravitational Search Algorithm ◽  
Solution Space ◽  
Search Algorithms ◽  
Heuristic Search Algorithm ◽  
Intractable Problems ◽  
Gravitational Search ◽  
Optimization Search ◽  
Better Than

Meta-heuristic techniques are important as they are used to find solutions to computationally intractable problems. Simplistic methods such as exhaustive search become computationally expensive and unreliable as the solution space for search algorithms increase. As no method is guaranteed to perform better than all others in all classes of optimization search problems, there is a need to constantly find new and/or adapt old search algorithms. This research proposes an Infrasonic Search Algorithm, inspired from the Gravitational Search Algorithm and the mating behaviour in peafowls. The Infrasonic Search Algorithm identified competitive solutions to 23 benchmark unimodal and multimodal test functions compared to the Genetic Algorithm, Particle Swarm Optimization Algorithm and the Gravitational Search Algorithm.

scholarly journals Optimal Solution of Structural Engineering Design Problems using Crow Search Algorithm

2019 ◽  
Vol 4 (4) ◽  
pp. 968-981
Author(s):  
Madhur Agarwal
Keyword(s):  
Engineering Design ◽  
Design Problem ◽  
Structural Engineering ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Worst Case ◽  
Design Problems ◽  
Beam Design ◽  
Engineering Design Problems ◽  
Better Than

In real world, the structural engineering design problems are large scale non-linear constrained problems. In the present study, crow search algorithm (CSA) is applied to find the optimal solution of structural engineering design problems such as pressure vessel design problem, welded beam design problem and tension/ compression string design problem. The numerical results are compared with the existing results reported in the literature including metaheuristic algorithms and it is found that the results obtained by the crow search algorithm are better than other existing algorithms. Further, the effectiveness of the algorithm is verified to be better than the existing algorithms by statistical analysis using mean, median, best case, and worst case scenarios. The present study confirms that the crow search algorithm may be easily and effectively applied to various structural design problems.

scholarly journals Two New Beetle Antennae Search (BAS) Algorithms and Their Comparative Investigation

2018 ◽  
Vol 2 (1) ◽  
pp. 9 ◽  
Author(s):  
Zongyuan Lin ◽  
Sile Ma ◽  
Xiaojing Ma ◽  
Xiangyuan Jiang ◽  
Shuai Li
Keyword(s):  
Heuristic Search ◽  
Search Strategy ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Comparative Investigation ◽  
Test Results ◽  
Heuristic Search Algorithm ◽  
Fitness Value ◽  
Stability And Accuracy ◽  
Environment Parameters

The Beetle Antennae Search (BAS) algorithm is a meta-heuristic search algorithm, which has efficient search capabilities. This paper presents two different variant algorithms based on the BAS algorithm, which are the BAS with fitness value (BASF) algorithm and BAS with local fast search (BASL) algorithm. The test results of 23 benchmark functions will be used to verify the reliability and accuracy of these algorithm. These benchmark functions include unimodal and multimodal high-dimensional functions, as well as fixed-dimensional multimodal functions. The test results show that the improved algorithm can search for the optimal solution globally and accurately with its own search strategy without environment parameters. Stability and accuracy are significantly improved while the calculation time does not change much.

scholarly journals Multi-Directional Heuristic Search

2020 ◽  
Author(s):  
Dor Atzmon ◽  
Jiaoyang Li ◽  
Ariel Felner ◽  
Eliran Nachmani ◽  
Shahaf Shperberg ◽  
...  
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Cost Functions ◽  
Multiple Agents ◽  
Admissible Heuristics ◽  
Heuristic Search Algorithm ◽  
Bidirectional Search ◽  
Multi Agent

In the Multi-Agent Meeting problem (MAM), the task is to find a meeting location for multiple agents, as well as a path for each agent to that location. In this paper, we introduce MM*, a Multi-Directional Heuristic Search algorithm that finds the optimal meeting location under different cost functions. MM* generalizes the Meet in the Middle (MM) bidirectional search algorithm to the case of finding an optimal meeting location for multiple agents. Several admissible heuristics are proposed, and experiments demonstrate the benefits of MM*.

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