Dynamic Game Difficulty Balancing in Real Time Using Evolutionary Fuzzy Cognitive Maps

2015 ◽  
Author(s):  
Lizeth Joseline Fuentes Perez ◽  
Luciano Arnaldo Romero Calla ◽  
Luis Valente ◽  
Anselmo Antunes Montenegro ◽  
Esteban Walter Gonzalez Clua
Keyword(s):  
Real Time ◽  
Dynamic Game ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps

scholarly journals Dynamic game difficulty balancing in real time using Evolutionary Fuzzy Cognitive Maps with automatic calibration

2016 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
Lizeth Joseline Fuentes Perez ◽  
Luciano Arnaldo Romero Calla ◽  
Anselmo Antunes Montenegro ◽  
Luis Valente ◽  
Esteban Walter Gonzales Clua
Keyword(s):  
Real Time ◽  
Medical Diagnosis ◽  
Dynamic Game ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Decision Problems ◽  
Automatic Calibration ◽  
Research Fields ◽  
Automated Method

Fuzzy Cognitive Maps (FCM) is a paradigm used to represent knowledge in a simple and concise way, expressing the grade of relation that exists between concepts and causal relationships. Due to its flexibility, FCM has been successfully applied in numerous applications in diverse research fields, such as, robotics, medical diagnosis, decision problems in information technology, games, and so forth. However, one critical drawback is the determination of the weights in the representation graph, which is generally done by an expert. The present paper proposes a semi-automated method for calibrating the weights in a solution for the problem of dynamic game difficulty balancing (DGB) using Evolutionary Fuzzy Cognitive Maps (E-FCM). The proposed algorithm adjusts the weights in real time, ensuring an equilibrium between the values generated according to the expert’s contribution (based on a static analysis) and the changes produced in the values of the concepts by the calibration process during the simulation (a dynamic analysis).

scholarly journals Fuzzy Cognitive Maps Based Game Balancing System in Real Time

2018 ◽  
Vol 9 (2) ◽  
pp. 335 ◽  
Author(s):  
Patel Kalpana Dhanji ◽  
Santhosh Kumar Singh
Keyword(s):  
Real Time ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Causal Connections ◽  
Ability Levels ◽  
The Way

Players may stop playing a picked amusement sooner than anticipated for some reasons. A standout amongst the most vital is identified with the way amusement planners and designers adjust diversion challenge levels. Practically speaking, players have distinctive ability levels and may discover common foreordained troublesome levels as too simple or too hard, getting to be noticeably disappointed or exhausted. The outcome might be diminished inspiration to continue playing the diversion, which implies decreased engagement. A way to deal with alleviate this issue is dynamic amusement trouble adjusting, which is a procedure that alters diversion play parameters progressively as indicated by the present player aptitude level. In this paper we propose a constant answer for DGB utilizing Evolutionary Fuzzy Cognitive Maps, for progressively adjusting a diversion trouble, giving a very much adjusted level of test to the player. Transformative Fuzzy Cognitive Maps depend on ideas that speak to setting diversion factors and are connected by fluffy and probabilistic causal connections that can be refreshed progressively. We talk about a few re-enactment tries that utilization our answer in a runner sort amusement to make all the more captivating and dynamic diversion encounters.

FUZZY COGNITIVE MAPS EMBEDDED IN A LOW COST AUTONOMOUS MOBILE ROBOT

2019 ◽  
Author(s):  
Márcio Mendonça ◽  
Guilherme Bender Sartori ◽  
Lucas Botoni de Souza ◽  
Giovanni Bruno Marquini Ribeiro
Keyword(s):  
Mobile Robot ◽  
Low Cost ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Autonomous Mobile Robot
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