scholarly journals Exploiting Obstacle Geometry to Reduce Search Time in Grid-Based Pathfinding

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1186
Author(s):  
Fahed Jubair ◽  
Mohammed Hawa
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Search Time ◽  
Search Tree ◽  
Search Algorithms ◽  
Binary Search ◽  
Binary Search Tree ◽  
Grid Map ◽  
Heuristic Search Algorithms ◽  
The Impact

Pathfinding is the problem of finding the shortest path between a pair of nodes in a graph. In the context of uniform-cost undirected grid maps, heuristic search algorithms, such as A ★ and weighted A ★ ( W A ★ ), have been dominantly used for pathfinding. However, the lack of knowledge about obstacle shapes in a gird map often leads heuristic search algorithms to unnecessarily explore areas where a viable path is not available. We refer to such areas in a grid map as blocked areas (BAs). This paper introduces a preprocessing algorithm that analyzes the geometry of obstacles in a grid map and stores knowledge about blocked areas in a memory-efficient balanced binary search tree data structure. During actual pathfinding, a search algorithm accesses the binary search tree to identify blocked areas in a grid map and therefore avoid exploring them. As a result, the search time is significantly reduced. The scope of the paper covers maps in which obstacles are represented as horizontal and vertical line-segments. The impact of using the blocked area knowledge during pathfinding in A ★ and W A ★ is evaluated using publicly available benchmark set, consisting of sixty grid maps of mazes and rooms. In mazes, the search time for both A ★ and W A ★ is reduced by 28 % , on average. In rooms, the search time for both A ★ and W A ★ is reduced by 30 % , on average. This is achieved while preserving the search optimality of A ★ and the search sub-optimality of W A ★ .

High Performance CGM-based Parallel Algorithms for the Optimal Binary Search Tree Problem

2016 ◽  
Vol 8 (4) ◽  
pp. 55-77 ◽  
Author(s):  
Vianney Kengne Tchendji ◽  
Jean Frederic Myoupo ◽  
Gilles Dequen
Keyword(s):  
Parallel Algorithms ◽  
Execution Time ◽  
High Performance ◽  
Scheduling Algorithm ◽  
Search Tree ◽  
Binary Search ◽  
Coarse Grained ◽  
Binary Search Tree ◽  
Parameter Dependent ◽  
The Impact

In this paper, the authors highlight the existence of close relations between the execution time, efficiency and number of communication rounds in a family of CGM-based parallel algorithms for the optimal binary search tree problem (OBST). In this case, these three parameters cannot be simultaneously improved. The family of CGM (Coarse Grained Multicomputer) algorithms they derive is based on Knuth's sequential solution running in time and space, where n is the size of the problem. These CGM algorithms use p processors, each with local memory. In general, the authors show that each algorithms runs in with communications rounds. is the granularity of their model, and is a parameter that depends on and . The special case of yields a load-balanced CGM-based parallel algorithm with communication rounds and execution steps. Alternately, if , they obtain another algorithm with better execution time, say , the absence of any load-balancing and communication rounds, i.e., not better than the first algorithm. The authors show that the granularity has a crucial role in the different techniques they use to partition the problem to solve and study the impact of each scheduling algorithm. To the best of their knowledge, this is the first unified method to derive a set of parameter-dependent CGM-based parallel algorithms for the OBST problem.

Quantum dialogue protocol based on Grover’s search algorithms

2019 ◽  
Vol 34 (21) ◽  
pp. 1950169
Author(s):  
Aihan Yin ◽  
Kemeng He ◽  
Ping Fan
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Quantum Dialogue ◽  
Quantum Search ◽  
Decoy State ◽  
Quantum Search Algorithm ◽  
Heuristic Search Algorithms ◽  
Security Information

Among many classic heuristic search algorithms, the Grover quantum search algorithm (QSA) can play a role of secondary acceleration. Based on the properties of the two-qubit Grover QSA, a quantum dialogue (QD) protocol is proposed. In addition, our protocol also utilizes the unitary operations and single-particle measurements. The transmitted quantum state (except for the decoy state used for detection) can transmit two-bits of security information simultaneously. Theoretical analysis shows that the proposed protocol has high security.

scholarly journals Beliefs We Can Believe in: Replacing Assumptions with Data in Real-Time Search

2020 ◽  
Vol 34 (06) ◽  
pp. 9827-9834
Author(s):  
Maximilian Fickert ◽  
Tianyi Gu ◽  
Leonhard Staut ◽  
Wheeler Ruml ◽  
Joerg Hoffmann ◽  
...  
Keyword(s):  
Real Time ◽  
Heuristic Search ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Experimental Results ◽  
Data Driven ◽  
Heuristic Search Algorithms ◽  
Original Presentation ◽  
Performance Statistics ◽  
The Cost

Suboptimal heuristic search algorithms can benefit from reasoning about heuristic error, especially in a real-time setting where there is not enough time to search all the way to a goal. However, current reasoning methods implicitly or explicitly incorporate assumptions about the cost-to-go function. We consider a recent real-time search algorithm, called Nancy, that manipulates explicit beliefs about the cost-to-go. The original presentation of Nancy assumed that these beliefs are Gaussian, with parameters following a certain form. In this paper, we explore how to replace these assumptions with actual data. We develop a data-driven variant of Nancy, DDNancy, that bases its beliefs on heuristic performance statistics from the same domain. We extend Nancy and DDNancy with the notion of persistence and prove their completeness. Experimental results show that DDNancy can perform well in domains in which the original assumption-based Nancy performs poorly.

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.

Statically Optimal Binary Search Tree Computation Using Non-Serial Polyadic Dynamic Programming on GPU's

2019 ◽  
Vol 11 (1) ◽  
pp. 49-70
Author(s):  
Mohsin Altaf Wani ◽  
Manzoor Ahmad
Keyword(s):  
Dynamic Programming ◽  
Dynamic Programming Algorithm ◽  
Search Time ◽  
Search Tree ◽  
Binary Search ◽  
High Rate ◽  
Binary Search Tree ◽  
Programming Algorithm ◽  
Optimal Arrangement ◽  
Speedup Factor

Modern GPUs perform computation at a very high rate when compared to CPUs; as a result, they are increasingly used for general purpose parallel computation. Determining if a statically optimal binary search tree is an optimization problem to find the optimal arrangement of nodes in a binary search tree so that average search time is minimized. Knuth's modification to the dynamic programming algorithm improves the time complexity to O(n2). We develop a multiple GPU-based implementation of this algorithm using different approaches. Using suitable GPU implementation for a given workload provides a speedup of up to four times over other GPU based implementations. We are able to achieve a speedup factor of 409 on older GTX 570 and a speedup factor of 745 is achieved on a more modern GTX 1060 when compared to a conventional single threaded CPU based implementation.

A phylogenetic analysis of the isopod family Janiridae (Crustacea)

1994 ◽  
Vol 8 (3) ◽  
pp. 749 ◽  
Author(s):  
GDF Wilson
Keyword(s):  
Heuristic Search ◽  
Cladistic Analysis ◽  
Tail Probability ◽  
Search Algorithms ◽  
Data Matrix ◽  
Data Set ◽  
New Family ◽  
Heuristic Search Algorithms ◽  
Family Level ◽  
The Impact

A phylogeny of the isopod family Janiridae and genera from presumptive outgroups, Acanthaspidiidae, Joeropsididae and Microparasellidae is estimated. Characters were gathered from the published literature, and assembled into a data matrix for cladistic analysis. The data, when evaluated with heuristic search algorithms, yielded eight most-parsimonious trees, none of which supported the monophyly of the Janiridae. To evaluate the impact of homoplasy, characters with a rescaled consistency less than 0.1 were deleted, resulting in four somewhat different trees that were non-monophyletic for the janirids. With the smaller data set, trees supporting janirid monophyly were 10 steps longer. A permutation tail probability test found substantially more hierarchical information in the janirid data set than in randomised data. Internal topologies of the shortest trees were evaluated as hypotheses for new family-level groups, although new family-level classifications cannot be proposed at this time owing to insufficient evidence. The Janiridae therefore cannot be considered monophyletic.

scholarly journals Goal Probability Analysis in Probabilistic Planning: Exploring and Enhancing the State of the Art

2016 ◽  
Vol 57 ◽  
pp. 229-271 ◽  
Author(s):  
Marcel Steinmetz ◽  
Jörg Hoffmann ◽  
Olivier Buffet
Keyword(s):  
Heuristic Search ◽  
Resource Constraints ◽  
State Of The Art ◽  
Search Algorithm ◽  
Search Algorithms ◽  
The State ◽  
Probabilistic Planning ◽  
Large Space ◽  
Heuristic Search Algorithms ◽  
Wide Range

Unavoidable dead-ends are common in many probabilistic planning problems, e.g. when actions may fail or when operating under resource constraints. An important objective in such settings is MaxProb, determining the maximal probability with which the goal can be reached, and a policy achieving that probability. Yet algorithms for MaxProb probabilistic planning are severely underexplored, to the extent that there is scant evidence of what the empirical state of the art actually is. We close this gap with a comprehensive empirical analysis. We design and explore a large space of heuristic search algorithms, systematizing known algorithms and contributing several new algorithm variants. We consider MaxProb, as well as weaker objectives that we baptize AtLeastProb (requiring to achieve a given goal probabilty threshold) and ApproxProb (requiring to compute the maximum goal probability up to a given accuracy). We explore both the general case where there may be 0-reward cycles, and the practically relevant special case of acyclic planning, such as planning with a limited action-cost budget. We design suitable termination criteria, search algorithm variants, dead-end pruning methods using classical planning heuristics, and node selection strategies. We design a benchmark suite comprising more than 1000 instances adapted from the IPPC, resource-constrained planning, and simulated penetration testing. Our evaluation clarifies the state of the art, characterizes the behavior of a wide range of heuristic search algorithms, and demonstrates significant benefits of our new algorithm variants.

scholarly journals Avoiding and Escaping Depressions in Real-Time Heuristic Search

2012 ◽  
Vol 43 ◽  
pp. 523-570 ◽  
Author(s):  
C. Hernandez ◽  
J. A. Baier
Keyword(s):  
Real Time ◽  
Heuristic Search ◽  
Search Algorithm ◽  
Search Space ◽  
Search Algorithms ◽  
Search Spaces ◽  
Heuristic Search Algorithms ◽  
Order Of Magnitude ◽  
Improved Performance ◽  
Hard Real Time

Heuristics used for solving hard real-time search problems have regions with depressions. Such regions are bounded areas of the search space in which the heuristic function is inaccurate compared to the actual cost to reach a solution. Early real-time search algorithms, like LRTA*, easily become trapped in those regions since the heuristic values of their states may need to be updated multiple times, which results in costly solutions. State-of-the-art real-time search algorithms, like LSS-LRTA* or LRTA*(k), improve LRTA*'s mechanism to update the heuristic, resulting in improved performance. Those algorithms, however, do not guide search towards avoiding depressed regions. This paper presents depression avoidance, a simple real-time search principle to guide search towards avoiding states that have been marked as part of a heuristic depression. We propose two ways in which depression avoidance can be implemented: mark-and-avoid and move-to-border. We implement these strategies on top of LSS-LRTA* and RTAA*, producing 4 new real-time heuristic search algorithms: aLSS-LRTA*, daLSS-LRTA*, aRTAA*, and daRTAA*. When the objective is to find a single solution by running the real-time search algorithm once, we show that daLSS-LRTA* and daRTAA* outperform their predecessors sometimes by one order of magnitude. Of the four new algorithms, daRTAA* produces the best solutions given a fixed deadline on the average time allowed per planning episode. We prove all our algorithms have good theoretical properties: in finite search spaces, they find a solution if one exists, and converge to an optimal after a number of trials.

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