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 Optimasi Sistem Informasi Penjadwalan Kuliah Berbasis Heuristic Search yang Dikombinasikan dengan Teknik Smart Back Tracking dan Look Ahead (Studi Kasus pada STMI –Kementerian Perindustrian)

2012 ◽  
Vol 9 (1) ◽  
pp. 53-60
Author(s):  
Dedy Trisanto ◽  
Muhamad Agus
Keyword(s):  
Artificial Intelligence ◽  
Constraint Satisfaction ◽  
Heuristic Search ◽  
Constraint Satisfaction Problem ◽  
Mathematical Problem ◽  
Optimal Scheduling ◽  
Search Techniques ◽  
Intelligent Search ◽  
Course Scheduling ◽  
Look Ahead

Scheduling lecture is scheduled number of components consisting of courses, lecturer, students, classrooms, and time with a number of restrictions and requirements (constraints) certain to get optimal results and the best. In this paper will be discussed and created scheduling lecture with a problem-solving approach to the science of Artificial Intelligence (Artificial Intelligence), by using an approximation of the mathematical problem that is aiming to find a situation or object that meets a number of requirements or specific criteria (Constraint Satisfaction Problem) to get the optimal scheduling and the best. To solve these problems the solution search techniques used by an algorithm that will result in optimal scheduling and the best (heuristic search) techniques combined with Smart Backtracking and Look Ahead called Intelligent Search to find and resolve problems when encountered a condition where no there is a solution in due course scheduling constraints and requirements are not met (deadlock). The application of these methods and techniques in the course scheduling information system is built, using the PHP programming language and MySQL database to solve the problem of scheduling to get optimal results and the best.

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.

FITNESS LANDSCAPES AND THE ANDREWS–CURTIS CONJECTURE

2012 ◽  
Vol 22 (02) ◽  
pp. 1250009 ◽  
Author(s):  
JERRY SWAN ◽  
GABRIELA OCHOA ◽  
GRAHAM KENDALL ◽  
MARTIN EDJVET
Keyword(s):  
Genetic Algorithms ◽  
Combinatorial Optimization ◽  
Landscape Metrics ◽  
Heuristic Search ◽  
Fitness Landscape ◽  
Optimization Methods ◽  
Fitness Landscapes ◽  
Breadth First Search ◽  
Successful Method ◽  
Blind Search

Attempts have been made to eliminate some potential counterexamples to the Andrews–Curtis conjecture using the combinatorial optimization methods of blind-search and the genetic algorithms meta-heuristic. Breadth-first search with secondary storage is currently the most successful method, which raises questions regarding the inferior performance of heuristic search. In order to understand the underlying reasons we obtain fitness landscape metrics for a number of balanced presentations and draw conclusions regarding the likely effectiveness of other meta-heuristics.

scholarly journals Finding Combined L1 and Link Metric Shortest Paths in the Presence of Orthogonal Obstacles: A Heuristic Approach

VLSI Design ◽  
1999 ◽  
Vol 9 (1) ◽  
pp. 91-104 ◽  
Author(s):  
Joon Shik Lim ◽  
S. Sitharama Iyengar ◽  
Si-Qing Zheng
Keyword(s):  
Shortest Path ◽  
Heuristic Search ◽  
Line Search ◽  
Shortest Paths ◽  
Search Algorithms ◽  
Search Method ◽  
Line Segments ◽  
Line Extensions ◽  
Heuristic Search Algorithms ◽  
Number Of Bends

This paper presents new heuristic search algorithms for searching combined rectilinear (L1) and link metric shortest paths in the presence of orthogonal obstacles. The Guided Minimum Detour (GMD) algorithm for L1 metric combines the best features of mazerunning algorithms and line-search algorithms. The Line-by-Line Guided Minimum Detour (LGMD) algorithm for L1 metric is a modification of the GMD algorithm that improves on efficiency using line-by-line extensions. Our GMD and LGMD algorithms always find a rectilinear shortest path using the guided A* search method without constructing a connection graph that contains shortest paths. The GMD and the LGMD algorithms can be implemented in O(m+eloge+NlogN) and O(eloge+NlogN) time, respectively, and O(e+N) space, where m is the total number of searched nodes, e is the number of boundary sides of obstacles, and N is the total number of searched line segments. Based on the LGMD algorithm, we consider not only the problems of finding a link metric shortest path in terms of the number of bends, but also the combined L1 metric and link metric shortest path in terms of the length and the number of bends.

scholarly journals Artificial Intelligence based Pacman Game

2020 ◽  
Vol 9 (9) ◽  
pp. 140-144
Keyword(s):  
Artificial Intelligence ◽  
Video Game ◽  
Environmental Conditions ◽  
Search Algorithms ◽  
Breadth First Search ◽  
Artificial Intelligence Research ◽  
Depth First Search ◽  
Optimal Paths ◽  
Alpha Beta ◽  
Multi Agents

This paper is about implementing pacman game with AI.The Game Pac-Man is a very challenging video game that can be useful in conducting AI(Artificial Intelligence) research. Here,the reason we have implemented various AI algorithms for pacman game is that it helps us to study AI by using visualizations through which we can understand AI more ef- fectively.The main aim is to build an intelligent pacman agent which finds optimal paths through the maze to find a particular goal such as a particular food position,escaping from ghosts.For that, we have implemented AI search algorithms like Depth first search,Breadth first search,A*search,Uniform cost search.We have also implemented multi-agents like Reflex agent,Minimax agent,Alpha-beta agent.Through these multiagent algorithms,we can make pacman to react from its environmental conditions and escape from ghosts to get high score.We have also done the visualization part of the above AI algorithms by which anyone can learn and understand AI algorithms easily.For visualisation of algorithms,we have used python libraries matplotlib and Networkx.

scholarly journals List Point Marker Path Finding for Artificial Intelligence Movement in 3D Games

2021 ◽  
Vol 9 (10) ◽  
pp. 1336-1340
Keyword(s):  
Artificial Intelligence ◽  
Shortest Path ◽  
Target Point ◽  
Path Finding ◽  
Breadth First Search ◽  
C Language ◽  
3D Game ◽  
Shortest Distance ◽  
Marker Point ◽  
Selection Of

In making a path finding algorithm in a 3D game to determine the direction of the NPC agent towards the destination, the Djiksra algorithm, Depth First Search, Breadth First Search and so on, usually the shortest distance is directly proportional to the travel duration to the target point. In this study, a test will be made using a list marker point such as the Djiksra algorithm to get the shortest distance and fastest time to reach the destination, in making this algorithm the C# language is used and the Unity software is used. After experimenting with various list points in different places in two directions, it was found that the distance traveled is always directly proportional to duration. So the selection of the fastest or shortest path can be done with this list point marker algorithm.

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.

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