scholarly journals Graph Abstraction in Real-time Heuristic Search

2007 ◽  
Vol 30 ◽  
pp. 51-100 ◽  
Author(s):  
V. Bulitko ◽  
N. Sturtevant ◽  
J. Lu ◽  
T. Yau
Keyword(s):  
Real Time ◽  
Heuristic Search ◽  
Search Space ◽  
Problem Size ◽  
Heuristic Function ◽  
Planning Time ◽  
Learning Speed ◽  
Heuristic Search Methods ◽  
State Abstraction ◽  
The Impact

Real-time heuristic search methods are used by situated agents in applications that require the amount of planning per move to be independent of the problem size. Such agents plan only a few actions at a time in a local search space and avoid getting trapped in local minima by improving their heuristic function over time. We extend a wide class of real-time search algorithms with automatically-built state abstraction and prove completeness and convergence of the resulting family of algorithms. We then analyze the impact of abstraction in an extensive empirical study in real-time pathfinding. Abstraction is found to improve efficiency by providing better trading offs between planning time, learning speed and other negatively correlated performance measures.

scholarly journals Dynamic Control in Real-Time Heuristic Search

2008 ◽  
Vol 32 ◽  
pp. 419-452 ◽  
Author(s):  
V. Bulitko ◽  
M. Lustrek ◽  
J. Schaeffer ◽  
Y. Bjornsson ◽  
S. Sigmundarson
Keyword(s):  
Real Time ◽  
Computer Games ◽  
Heuristic Search ◽  
Dynamic Control ◽  
Computer Game ◽  
Action Planning ◽  
Search Algorithms ◽  
Problem Size ◽  
Planning Time ◽  
Large Computer

Real-time heuristic search is a challenging type of agent-centered search because the agent's planning time per action is bounded by a constant independent of problem size. A common problem that imposes such restrictions is pathfinding in modern computer games where a large number of units must plan their paths simultaneously over large maps. Common search algorithms (e.g., A*, IDA*, D*, ARA*, AD*) are inherently not real-time and may lose completeness when a constant bound is imposed on per-action planning time. Real-time search algorithms retain completeness but frequently produce unacceptably suboptimal solutions. In this paper, we extend classic and modern real-time search algorithms with an automated mechanism for dynamic depth and subgoal selection. The new algorithms remain real-time and complete. On large computer game maps, they find paths within 7% of optimal while on average expanding roughly a single state per action. This is nearly a three-fold improvement in suboptimality over the existing state-of-the-art algorithms and, at the same time, a 15-fold improvement in the amount of planning per action.

A Study on Darwinian Crow Search Algorithm for Multilevel Thresholding

2021 ◽  
pp. 2250012
Author(s):  
Ehsan Ehsaeyan ◽  
Alireza Zolghadrasli
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Search Space ◽  
Energy Curve ◽  
Local Optimum ◽  
Multilevel Thresholding ◽  
Curve Method ◽  
Heuristic Search Methods ◽  
Multi Level ◽  
Computationally Expensive

Multilevel thresholding is a basic method in image segmentation. The conventional image multilevel thresholding algorithms are computationally expensive when the number of decomposed segments is high. In this paper, a novel and powerful technique is suggested for Crow Search Algorithm (CSA) devoted to segmentation applications. The main contribution of our work is to adapt Darwinian evolutionary theory with heuristic CSA. First, the population is divided into specified groups and each group tries to find better location in the search space. A policy of encouragement and punishment is set on searching agents to avoid being trapped in the local optimum and premature solutions. Moreover, to increase the convergence rate of the proposed method, a gray-scale map is applied to out-boundary agents. Ten test images are selected to measure the ability of our algorithm, compared with the famous procedure, energy curve method. Two popular entropies i.e. Otsu and Kapur are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are implemented and compared to the introduced method. The obtained results show that our method, compared with the original CSA, and other heuristic search methods, can extract multi-level thresholding more efficiently.

Improving heuristic function of cost-based abduction system using real-time heuristic search

2004 ◽  
Vol 35 (6) ◽  
pp. 89-97
Author(s):  
Makoto Koshino ◽  
Tadashi Okamine ◽  
Haruhiko Kimura ◽  
Sadaki Hirose
Keyword(s):  
Real Time ◽  
Heuristic Search ◽  
Heuristic Function

scholarly journals Distributional Metareasoning for Heuristic Search

2021 ◽  
Author(s):  
Tianyi Gu
Keyword(s):  
State Space ◽  
Real Time ◽  
Real World ◽  
Heuristic Search ◽  
Time Pressure ◽  
Autonomous Systems ◽  
Optimal Solutions ◽  
Algorithmic Approach ◽  
Heuristic Search Methods ◽  
Computational Resources

Heuristic search methods are widely used in many real-world autonomous systems. Yet, people always want to solve search problems that are larger than time allows. To address these challenging problems, even suboptimally, a planning agent should be smart enough to intelligently allocate its computational resources, to think carefully about where in the state space it should spend time searching. For finding optimal solutions, we must examine every node that is not provably too expensive. In contrast, to find suboptimal solutions when under time pressure, we need to be very selective about which nodes to examine. In this work, we will demonstrate that estimates of uncertainty, represented as belief distributions, can be used to drive search effectively. This type of algorithmic approach is known as metareasoning, which refers to reasoning about which reasoning to do. We will provide examples of improved algorithms for real-time search, bounded-cost search, and situated planning.

scholarly journals Real-Time Planning as Decision-Making under Uncertainty

2019 ◽  
Vol 33 ◽  
pp. 2338-2345
Author(s):  
Andrew Mitchell ◽  
Wheeler Ruml ◽  
Fabian Spaniol ◽  
Jorg Hoffmann ◽  
Marek Petrik
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Heuristic Search ◽  
Fixed Time ◽  
Decision Making Under Uncertainty ◽  
Uncertain Evidence ◽  
Heuristic Search Methods ◽  
Optimal Action ◽  
Time Planning ◽  
Planning Problems

In real-time planning, an agent must select the next action to take within a fixed time bound. Many popular real-time heuristic search methods approach this by expanding nodes using time-limited A* and selecting the action leading toward the frontier node with the lowest f value. In this paper, we reconsider real-time planning as a problem of decision-making under uncertainty. We propose treating heuristic values as uncertain evidence and we explore several backup methods for aggregating this evidence. We then propose a novel lookahead strategy that expands nodes to minimize risk, the expected regret in case a non-optimal action is chosen. We evaluate these methods in a simple synthetic benchmark and the sliding tile puzzle and find that they outperform previous methods. This work illustrates how uncertainty can arise even when solving deterministic planning problems, due to the inherent ignorance of time-limited search algorithms about those portions of the state space that they have not computed, and how an agent can benefit from explicitly metareasoning about this uncertainty.

A REAL-TIME SCHEDULING ALGORITHM FOR TASKS WITH RESOURCE CONTENTION ON A MULTIPROCESSOR

1994 ◽  
Vol 04 (03) ◽  
pp. 243-253
Author(s):  
DAL-SOO RYANG ◽  
KYU HO PARK
Keyword(s):  
Real Time ◽  
General Model ◽  
Resource Constraints ◽  
Scheduling Algorithm ◽  
Search Space ◽  
Real Time Scheduling ◽  
Multiple Processors ◽  
Time Scheduling ◽  
Two Measures ◽  
The Impact

Our scheduling algorithm is based on a general model with timing and resource constraints which permits OR requests. In order to keep run-time costs low, we propose an algorithm that does not search the whole search space. This paper defines two measures, survivability and impact, for scheduling tasks conflicted for some resources. The survivability is a metric to show how urgent a task is, and how constrained it is by its resources. The impact of a resource for a task measures how much other tasks are influenced by the allocation of the resource to the task. Our scheduling algorithm uses the survivability to schedule tasks on multiple processors. After a task is picked out to be run in a time slice using the survivability, the least impact resources are allocated from several alternative resources.

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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