scholarly journals Hardness measures for gridworld benchmarks and performance analysis of real-time heuristic search algorithms

2008 ◽  
Vol 16 (1) ◽  
pp. 23-36
Author(s):  
Masataka Mizusawa ◽  
Masahito Kurihara
Keyword(s):  
Performance Analysis ◽  
Real Time ◽  
Heuristic Search ◽  
Search Algorithms ◽  
Heuristic Search Algorithms ◽  
And Performance

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 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.

scholarly journals Case-Based Subgoaling in Real-Time Heuristic Search for Video Game Pathfinding

2010 ◽  
Vol 39 ◽  
pp. 269-300 ◽  
Author(s):  
V. Bulitko ◽  
Y. Björnsson ◽  
R. Lawrence
Keyword(s):  
Video Games ◽  
Real Time ◽  
Video Game ◽  
Heuristic Search ◽  
Scale Up ◽  
Computation Time ◽  
Search Algorithms ◽  
Problem Size ◽  
Solution Quality ◽  
Heuristic Search Algorithms

Real-time heuristic search algorithms satisfy a constant bound on the amount of planning per action, independent of problem size. As a result, they scale up well as problems become larger. This property would make them well suited for video games where Artificial Intelligence controlled agents must react quickly to user commands and to other agents' actions. On the downside, real-time search algorithms employ learning methods that frequently lead to poor solution quality and cause the agent to appear irrational by re-visiting the same problem states repeatedly. The situation changed recently with a new algorithm, D LRTA*, which attempted to eliminate learning by automatically selecting subgoals. D LRTA* is well poised for video games, except it has a complex and memory-demanding pre-computation phase during which it builds a database of subgoals. In this paper, we propose a simpler and more memory-efficient way of pre-computing subgoals thereby eliminating the main obstacle to applying state-of-the-art real-time search methods in video games. The new algorithm solves a number of randomly chosen problems off-line, compresses the solutions into a series of subgoals and stores them in a database. When presented with a novel problem on-line, it queries the database for the most similar previously solved case and uses its subgoals to solve the problem. In the domain of pathfinding on four large video game maps, the new algorithm delivers solutions eight times better while using 57 times less memory and requiring 14% less pre-computation time.

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.

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