Behavior Trees for Modelling Artificial Intelligence in Games: A Tutorial

Marcotte, Ryan; Hamilton, Howard J.

doi:10.1007/s40869-017-0040-9

“…where N is the maximum distance in the game scenario. A = (small; medium; large) g: This function is constructed using three labels that are represented with trapezoidal membership functions: small (0,0,2,6), medium (2,6,6,10), and large (6,10, N, N).…”

Section: First-order Computational Perceptionsmentioning

confidence: 99%

Teach me to play, gamer! Imitative learning in computer games via linguistic description of complex phenomena and decision trees

Rubio-Manzano

¹

,

Lermanda

²

,

Martínez-Araneda

³

et al. 2021

Preprint

0

View full text Add to dashboard Cite

In this article, we present a new machine learning model by imitation based on the linguistic description of complex phenomena. The idea consists of, first, capturing the behaviour of human players by creating a computational perception network based on the execution traces of the games and, second, representing it using fuzzy logic (linguistic variables and if-then rules). From this knowledge, a set of data (dataset) is automatically created to generate a learning model based on decision trees. This model will be used later to automatically control the movements of a bot. The result is an artificial agent that mimics the human player. We have implemented, tested and evaluated this technology. The results obtained are interesting and promising, showing that this method can be a good alternative to design and implement the behaviour of intelligent agents in video game development.

show abstract

“…g: This function is constructed using three labels that are represented with trapezoidal membership functions: small (0,0,2,6), medium (2,6,6,10), and large (6,10, N, N).…”

Section: First-order Computational Perceptionsmentioning

confidence: 99%

“…On the other hand, although more techniques have been incorporated into advances, such as behaviour trees (BTs) [6], the main limitation of FSMs or BTs is that their design is top down and not bottom up. Including a bottomup phase (data-driven) can improve the FSMs and BTs and the knowledge of the designers about their game.…”

Section: Introductionmentioning

confidence: 99%

Teach me to play, gamer! Imitative learning in computer games via linguistic description of complex phenomena and decision tree

Rubio-Manzano,

Lermanda,

Martinez

et al. 2021

Preprint

0

View full text Add to dashboard Cite

In this article, we present a new machine learning model by imitation based on the linguistic description of complex phenomena. The idea consists of, first, capturing the behaviour of human players by creating a computational perception network based on the execution traces of the games and, second, representing it using fuzzy logic (linguistic variables and if-then rules). From this knowledge, a set of data (dataset) is automatically created to generate a learning model based on decision trees. This model will be used later to automatically control the movements of a bot. The result is an artificial agent that mimics the human player. We have implemented, tested and evaluated this technology. The results obtained are interesting and promising, showing that this method can be a good alternative to design and implement the behaviour of intelligent agents in video game development.

show abstract

“…Ryan Marcotte and Howard J. Hamilton provide an introduction to behaviour trees [6]. Behaviour Trees provide utility in engineering or designing AI in games and is a plan of execution.…”

Section: Reviewmentioning

confidence: 99%

Review on foreground artificial intelligence in games

Ganguly¹,

Reddy²,

Venkataraman³

et al. 2018

IJET

1

0

View full text Add to dashboard Cite

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.

show abstract

Behavior Trees for Modelling Artificial Intelligence in Games: A Tutorial

Cited by 18 publications

References 9 publications

Teach me to play, gamer! Imitative learning in computer games via linguistic description of complex phenomena and decision trees

Teach me to play, gamer! Imitative learning in computer games via linguistic description of complex phenomena and decision trees

Teach me to play, gamer! Imitative learning in computer games via linguistic description of complex phenomena and decision tree

Review on foreground artificial intelligence in games

Contact Info

Product

Resources

About