Mathematical proofs as graph search problems in theory courses

1999 ◽  
Vol 31 (1) ◽  
pp. 110-113
Author(s):  
Jose L. Cordova
Keyword(s):  
Graph Search ◽  
Search Problems ◽  
Mathematical Proofs ◽  
As Graph

scholarly journals Development of an AI-based bot to Tibia MMORPG

2021 ◽  
Vol 2 (2) ◽  
pp. 1-9
Author(s):  
Thiago Castanheira Retes de Sousa ◽  
Rafael Lima de Carvalho
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Template Matching ◽  
Search Algorithm ◽  
Real Life ◽  
Input Image ◽  
Graph Search ◽  
As Graph ◽  
Simulation Results ◽  
And Performance

Artificial Intelligence has always been used in designing of automated agents for playing games such as Chess, Go, Defense of the Ancients 2, Snake Game, billiard and many others. In this work, we present the development and performance evaluation of an automated bot that mimics a real life player for the RPG Game Tibia. The automated bot is built using a combination of AI techniques such as graph search algorithm A* and computer vision tools like template matching. Using four algorithms to get global position of player in game, handle its health and mana, target monsters and walk through the game, we managed to develop a fully automated Tibia bot based in raw input image. We evaluated the performance of the agent in three different scenarios, collecting and analyzing metrics such as XP Gain, Supplies Usage and Balance. The simulation results shows that the developed bot is capable of producing competitive results according to in-game metrics when compared to human players.

Efficient Algorithms for Geometric Graph Search Problems

1986 ◽  
Vol 15 (2) ◽  
pp. 478-494 ◽  
Author(s):  
Hiroshi Imai ◽  
Takao Asano
Keyword(s):  
Geometric Graph ◽  
Efficient Algorithms ◽  
Graph Search ◽  
Search Problems

scholarly journals Incremental Symmetry Breaking Constraints for Graph Search Problems

2020 ◽  
Vol 34 (02) ◽  
pp. 1536-1543
Author(s):  
Avraham Itzhakov ◽  
Michael Codish
Keyword(s):  
Problem Solving ◽  
Symmetry Breaking ◽  
Special Property ◽  
Graph Search ◽  
Search Problem ◽  
Search Problems ◽  
Canonical Order ◽  
Parallel Graph ◽  
Canonical Graph

This paper introduces incremental symmetry breaking constraints for graph search problems which are complete and compact. We show that these constraints can be computed incrementally: A symmetry breaking constraint for order n graphs can be extended to one for order n + 1 graphs. Moreover, these constraints induce a special property on their canonical solutions: An order n canonical graph contains a canonical subgraph on the first k vertices for every 1 ≤ k ≤ n. This facilitates a “generate and extend” paradigm for parallel graph search problem solving: To solve a graph search problem φ on order n graphs, first generate the canonical graphs of some order k < n. Then, compute canonical solutions for φ by extending, in parallel, each canonical order k graph together with suitable symmetry breaking constraints. The contribution is that the proposed symmetry breaking constraints enable us to extend the order k canonical graphs to order n canonical solutions. We demonstrate our approach through its application on two hard graph search problems.

scholarly journals A Case of Pathology in Multiobjective Heuristic Search

2013 ◽  
Vol 48 ◽  
pp. 717-732 ◽  
Author(s):  
J.L. Pérez de la Cruz ◽  
L. Mandow ◽  
E. Machuca
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Simple Graph ◽  
Graph Search ◽  
Problem Size ◽  
Search Problems ◽  
Heuristic Information ◽  
Blind Search

This article considers the performance of the MOA* multiobjective search algorithm with heuristic information. It is shown that in certain cases blind search can be more efficient than perfectly informed search, in terms of both node and label expansions. A class of simple graph search problems is defined for which the number of nodes grows linearly with problem size and the number of nondominated labels grows quadratically. It is proved that for these problems the number of node expansions performed by blind MOA* grows linearly with problem size, while the number of such expansions performed with a perfectly informed heuristic grows quadratically. It is also proved that the number of label expansions grows quadratically in the blind case and cubically in the informed case.

Best Neighbor Heuristic Search for Finding Minimum Paths in Transportation Networks

1998 ◽  
Vol 1651 (1) ◽  
pp. 48-52 ◽  
Author(s):  
Jeffrey L. Adler
Keyword(s):  
Heuristic Search ◽  
Transportation Networks ◽  
Transportation Network ◽  
Search Problems ◽  
A Algorithm ◽  
Running Time ◽  
Search Effort ◽  
Wide Range ◽  
Path Search ◽  
Sparse Networks

For a wide range of transportation network path search problems, the A* heuristic significantly reduces both search effort and running time when compared to basic label-setting algorithms. The motivation for this research was to determine if additional savings could be attained by further experimenting with refinements to the A* approach. We propose a best neighbor heuristic improvement to the A* algorithm that yields additional benefits by significantly reducing the search effort on sparse networks. The level of reduction in running time improves as the average outdegree of the network decreases and the number of paths sought increases.

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