Benchmark for Coalitions at Multiagent Systems in a Robotic Soccer Simulation Environment

2018 ◽  
pp. 184-196
Author(s):  
Eder Mateus Nunes Gonçalves ◽  
Diana Adamatti ◽  
Telmo dos Santos Klipp
Keyword(s):  
Multiagent Systems ◽  
Simulation Environment ◽  
Robotic Soccer

Santos: A physics-based digital human simulation environment

2006 ◽  
Author(s):  
Karim Abdel-Malek ◽  
Jasbir Arora ◽  
Jingzhou Yang ◽  
Timothy Marler ◽  
Steve Beck ◽  
...  
Keyword(s):  
Simulation Environment ◽  
Digital Human ◽  
Human Simulation

Co-evolution of Hetero Multiagent Systems using Genetic Network Programming.

2003 ◽  
Vol 123 (3) ◽  
pp. 544-551 ◽  
Author(s):  
Kotaro Hirasawa ◽  
Masafumi Okubo ◽  
Jinglu Hu ◽  
Junichi Murata ◽  
Yuko Matsuya
Keyword(s):  
Multiagent Systems ◽  
Genetic Network ◽  
Network Programming ◽  
Genetic Network Programming

A Simulation Environment for Analyzing the Impact of Volcanic Ash on Trajectory-Based Air Traffic Management

2013 ◽  
Author(s):  
Angela Schmitt ◽  
Ruzica Vujasinovic ◽  
Christiane Edinger ◽  
Julia Zillies ◽  
Vilmar Mollwitz
Keyword(s):  
Traffic Management ◽  
Volcanic Ash ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Simulation Environment ◽  
The Impact

F1_Tenth_Course_Report_Group_C

2018 ◽  
Author(s):  
Yi Chen ◽  
Sagar Manglani ◽  
Roberto Merco ◽  
Drew Bolduc
Keyword(s):  
Operating System ◽  
Autonomous Driving ◽  
Software Tools ◽  
Simulation Environment ◽  
Robot Operating System ◽  
Available Information

In this paper, we discuss several of major robot/vehicle platforms available and demonstrate the implementation of autonomous techniques on one such platform, the F1/10. Robot Operating System was chosen for its existing collection of software tools, libraries, and simulation environment. We build on the available information for the F1/10 vehicle and illustrate key tools that will help achieve properly functioning hardware. We provide methods to build algorithms and give examples of deploying these algorithms to complete autonomous driving tasks and build 2D maps using SLAM. Finally, we discuss the results of our findings and how they can be improved.

An Intuitionistic Fuzzy Based Novel Approach to CPU Scheduler

2020 ◽  
Vol 16 (4) ◽  
pp. 316-328
Author(s):  
Supriya Raheja
Keyword(s):  
Fuzzy Inference ◽  
The Other ◽  
Simulation Environment ◽  
Inference System ◽  
Intuitionistic Fuzzy ◽  
Novel Approach ◽  
Continuous Feedback ◽  
Simulation Results ◽  
Effectiveness And Efficiency ◽  
Unique Capability

Background: The extension of CPU schedulers with fuzzy has been ascertained better because of its unique capability of handling imprecise information. Though, other generalized forms of fuzzy can be used which can further extend the performance of the scheduler. Objectives: This paper introduces a novel approach to design an intuitionistic fuzzy inference system for CPU scheduler. Methods: The proposed inference system is implemented with a priority scheduler. The proposed scheduler has the ability to dynamically handle the impreciseness of both priority and estimated execution time. It also makes the system adaptive based on the continuous feedback. The proposed scheduler is also capable enough to schedule the tasks according to dynamically generated priority. To demonstrate the performance of proposed scheduler, a simulation environment has been implemented and the performance of proposed scheduler is compared with the other three baseline schedulers (conventional priority scheduler, fuzzy based priority scheduler and vague based priority scheduler). Results: Proposed scheduler is also compared with the shortest job first CPU scheduler as it is known to be an optimized solution for the schedulers. Conclusion: Simulation results prove the effectiveness and efficiency of intuitionistic fuzzy based priority scheduler. Moreover, it provides optimised results as its results are comparable to the results of shortest job first.

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