Reactiveness and navigation in computer games: Different needs, different approaches

Pérez, Diego; Nicolau, Miguel; O’Neill, Michael; Brabazon, Anthony

doi:10.1109/cig.2011.6032017

Cited by 8 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is based on the famous video game Super Mario Bros., using a modified version of it called Infinite Mario Bros.. Its gameplay track combines the idea of path planning and reactiveness to enemy movements. An example of an entry to this competition, by D. Perez and M. Nicolau [31], used A* to solve the navigation task and grammatical evolution to evolve behaviour trees to manage the reaction to dynamic enemies. The winner of the 2009 edition, R. Baumgarten [30], also used A* for his controller, optimising the search in order not to violate the real-time requirements stated in the rules.…”

Section: Real-time Games and Competitionsmentioning

confidence: 99%

The physical travelling salesman problem: WCCI 2012 competition

Pérez

Rohlfshagen

Lucas

2012

2012 IEEE Congress on Evolutionary Computation

Self Cite

View full text Add to dashboard Cite

Numerous competitions have emerged in recent years that allow researchers to evaluate their algorithms on a variety of real-time video games with different degrees of complexity. These competitions, which vary from classical arcade games like Ms Pac-Man to racing simulations (Torcs) and realtime strategy games (StarCraft), are essential to establish a uniform testbed that allows practitioners to refine their algorithms over time. In this paper we propose a new competition to be held for the first time at WCCI 2012: the Physical Travelling SalesmanProblem is an open-ended single-player real-time game that removes some of the complexities evident in other video games while preserving some of the most fundamental challenges. This paper motivates and outlines the PTSP and discusses in detail the framework of the competition, including software interfaces, parameter settings, rules and details of submission.

show abstract

Section: Real-time Games and Competitionsmentioning

confidence: 99%

The physical travelling salesman problem: WCCI 2012 competition

Pérez

Rohlfshagen

Lucas

2012

2012 IEEE Congress on Evolutionary Computation

Self Cite

View full text Add to dashboard Cite

show abstract

“…Робота [7] розглядає поєднання еволюційного програмування із моделлю поведінкових дерев. Запропонований підхід реалізується на прикладі гри Mario AI Benchmark.…”

Section: аналіз останніх досліджень та публікаційunclassified

Machine learning methods for control of non-playable characters behaviour in multiplayer RPG

Budnyk,

Yakovyna

2023

Vìsn. Nac. unìv. "Lʹvìv. polìteh.", Ser. Ìnf. sist. merežì

View full text Add to dashboard Cite

This article covers the problem of developing a control system for non-player characters in a multiplayer RPG. Commercial projects in the field of videogames and RPG (Role-Playing Game) projects in particular seldom use machine learning models for the implementation of character behaviour. The most common approach is to use primitive preprogrammed rules, or to implement a finite state machine. Such approaches ruin the immersion of playing with real creatures, since various predefined rules make the characters predictable. A good game AI is supposed to give the player an impression of interacting with real characters, that make various decisions, sometimes unpredictable. To achieve this goal, this article covers an approach with using various machine learning models in conjunction with a traditional finite state machine. A videogame developed earlier is used as the basis for problem solution. The article conducts an analysis of the existing works in the field of videogame AI. Next, the implementation of control system is described. This system utilizes a couple selected and successfully trained machine learning models. A multitude of models were tested, eventually a decision tree model and a neural network were selected, since they yielded the best results. The process of development and implementation of a control system involving machine learning models is then described. The approaches of teaching such models are described, and finally the achieved results are analyzed. To gauge the results, different models were compared against each other in test battles. The decision tree model showed results slightly better than the traditional finite state machine. Meanwhile, the neural network performed significantly better, beating other models far more often. Achieved results can be developed further, utilizing more complex models and improving training methods, which will result in even more sophisticated characters. Presence of such characters in the game will qualitatively improve the gameplay. Obtained system was integrated into the videogame, that may potentially become a commercial product.

show abstract

“…Reflecting this is the wide range of application domains to which GE has been applied: highlights include architecture and engineering design [87,399,481,485], fi-nance [75,122,447], music and art [396,397,444,467,573], computer games [506,507,570,571], animation [428,429], ecosystem modelling [448], and autonomous networks [264,265,266]. The grammar contains the rules governing how the development of the phenotype is conducted, and as such can contain domain knowledge biasing the form a phenotypic solution can take.…”

Section: Grammatical Evolutionmentioning

confidence: 99%