Learning Avatar's Locomotion Patterns Through Spatial Analysis in FPS Video Games

2018 ◽  
Vol 8 (1) ◽  
pp. 28-45 ◽  
Author(s):  
Luis Alberto Casillas Santillan ◽  
Johor Ismael Jara Gonzalez
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Spatial Analysis ◽  
Video Games ◽  
Geometrical Model ◽  
Specific Pattern ◽  
Active Response ◽  
3D Game ◽  
Current Proposal

This article describes how current video games offer an extreme use of media fusion. Such construction implies a novel form of complexity regarding game control and active response from game to player. All of these elements produce deeper immersion effect in players. In order to perform a detailed supervision over this kind of game, additional controls should be included in game. Some of these controls are the moving and decision schemes. Authors believe that players move around virtual scenarios following some sort of pattern. Every player would have a specific pattern, according to his/her experience and capability to manage the gamepad layout. Current proposal consists in a 3D geometrical model surrounding player's avatar. Data unwittingly provided by the player, have elements to discover and, eventually, learn some gamers' patterns. The availability of these patterns would allow an improved game response and even the possibility of machine learning, as well as other artificial intelligence strategies. Every 3D game may include the model proposed in this paper, due to its noninvasive operation.

scholarly journals Review on foreground artificial intelligence in games

2018 ◽  
Vol 7 (2.8) ◽  
pp. 453
Author(s):  
Rajjeshwar Ganguly ◽  
Dubba Rithvik Reddy ◽  
Revathi Venkataraman ◽  
Sharanya S
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Video Games ◽  
Decision Trees ◽  
Learning Agents ◽  
Player Experience

Artificial Intelligence (AI) is applied in almost every field existing in today's world and video games prove to be an excellent ground due to its responsive and intelligent behaviour. The games can be put to use model human- level AI, machine learning and scripting behaviour. This work deals with AI used in games to create more complicated and human like behaviour in the non player characters. Unlike most commercial games, games involving AI don’t use the AI in the background rather it is used in the foreground to enhance player experience. An analysis of use of the AI in a number of existing games is made to identify patterns for AI in games which include decision trees, scripted behaviour and learning agents.

Artificial Intelligence and the Temporality of Machine Images

2021 ◽  
Author(s):  
Andrew R. Johnston
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Video Games ◽  
Learning Strategies ◽  
Learning Algorithms ◽  
Computational Time ◽  
Artificial Intelligence Technology ◽  
Learning Test ◽  
The Relationship

DeepMind, a recent artificial intelligence technology created at Google, references in its name the relationship in AI between models of cognition used in this technology‘s development and its new deep learning algorithms. This chapter shows how AI researchers have been attempting to reproduce applied learning strategies in humans but have difficulty accessing and visualizing the computational actions of their algorithms. Google created an interface for engaging with computational temporalities through the production of visual animations based on DeepMind machine-learning test runs of Atari 2600 video games. These machine play animations bear the traces of not only DeepMind‘s operations, but also of contemporary shifts in how computational time is accessed and understood.

scholarly journals Use of Artificial Intelligence and Machine Learning in Games

2020 ◽  
pp. 114-118
Author(s):  
Nachiket Jadhav ◽  
Aniket Matodkar ◽  
Anish Mandhare ◽  
Sujata Bhairnallykar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Video Games ◽  
The Other ◽  
Hardware Support ◽  
Learning In Games ◽  
Game Play ◽  
Cost Efficient

With modern video games surpassing every set of expectations in terms of graphics, game play, mechanics and hardware support, Artificial Intelligence in video games has also come a long way, from when it was first implemented in 1951. Although every set of games has an AI unique to itself, many of the algorithms are now developed such that they can be implemented in various games without any major changes in coding. But this could lead to the players exploiting AI in a single game to break the other games as well. Though this could be easily fixed by changing some minor fragments of algorithms, it would very well be an efficient way of developing complex AI for many games at once. This paper focuses on providing a cost-efficient way to implement AI algorithms that would benefit most of the upcoming and future games that will depend on AI to make themselves more dynamic to the players. This is done by taking the examples of various AI algorithms implemented in games like Pacman, Dota2, Tom Clancy's- The Division and many more.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

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