Artificial Immune Systems

2008 ◽  
pp. 150-179
Author(s):  
Darryl Charles ◽  
Colin Fyfe ◽  
Daniel Livingstone ◽  
Stephen McGlinchey
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Artificial Immune Systems ◽  
Effective Model ◽  
Simple Games ◽  
Artificial Immune ◽  
Immune Systems ◽  
Strategy Games ◽  
Rts Game

We now consider the problem of introducing more intelligence into the artificial intelligence’s responses in real-time strategy games (RTS). We discuss how the paradigm of artificial immune systems (AIS) gives us an effective model to improve the AI’s responses and demonstrate with simple games how the AIS work. We further discuss how the AIS paradigm enables us to extend current games in ways which make the game more sophisticated for both human and AI. In this chapter, we show how strategies may be dynamically created and utilised by an artificial intelligence in a real-time strategy (RTS) game. We develop as simple as possible RTS games in order to display the power of the method we use.

scholarly journals Artificial Immune Systems: An Overview for Faulting Actuators

Actuators ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 53
Author(s):  
Kidd
Keyword(s):  
Real Time ◽  
Artificial Immune Systems ◽  
Adaptive Algorithms ◽  
Actuator Faults ◽  
Artificial Immune ◽  
Immune Systems ◽  
Limited Success ◽  
Fault Compensation

This paper reviews Artificial Immune Systems (AIS) that can be implemented to compensate for actuators that are in a faulted state or operating abnormally. Eventually, all actuators will fail or wear out, and these actuator faults must be managed if a system is to operate safely. The AIS are adaptive algorithms which are inherently well-suited to these situations by treating these faults as infections that must be combated. However, the computational intensity of these algorithms has caused them to have limited success in real-time situations. With the advent of distributed and cloud-based computing these algorithms have begun to be feasible for diagnosing faulted actuators and then generating compensating controllers in near-real-time. To encourage the application of AIS to these situations, this work presents research for the fundamental operating principles of AIS, their applications, and a brief case-study on their applicability to fault compensation by considering an overactuated rover with four independent drive wheels and independent front and rear steering.

Applications of Computational Intelligence in Computing Security

2020 ◽  
pp. 1-22
Author(s):  
Yousif Abdullatif Albastaki
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Swarm Intelligence ◽  
Computational Intelligence ◽  
Fuzzy Systems ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Immune Systems

This chapter is an introductory chapter that attempts to highlight the concept of computational intelligence and its application in the field of computing security; it starts with a brief description of the underlying principles of artificial intelligence and discusses the role of computational intelligence in overcoming conventional artificial intelligence limitations. The chapter then briefly introduces various tools or components of computational intelligence such as neural networks, evolutionary computing, swarm intelligence, artificial immune systems, and fuzzy systems. The application of each component in the field of computing security is highlighted.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

Developing an Algorithm for the Application of Bayesian Method to Software Using Artificial Immune Systems

2021 ◽  
Author(s):  
Shafagat Mahmudova
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Bayesian Method ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Computational System ◽  
Malicious Software ◽  
Immune Systems ◽  
Advantages And Disadvantages ◽  
Protection Systems

Abstract This study provides information on artificial immune systems. The artificial immune system is an adaptive computational system that uses models, principles, mechanisms and functions to describe and solve the problems in theoretical immunology. Its application in various fields of science is explored. The theory of natural immune systems and the key features and algorithms of artificial immune system are analyzed. The advantages and disadvantages of protection systems based on artificial immune systems are shown. The methods for malicious software detection are studied. Some works in the field of artificial immune systems are analyzed, and the problems to be solved are identified. A new algorithm is developed for the application of Bayesian method in software using artificial immune systems, and experiments are implemented. The results of the experiment are estimated to be good. The advantages and disadvantages of AIS were shown. To eliminate the disadvantages, perfect AISs should be developed to enable the software more efficient and effective.

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