DNAgents: Genetically Engineered Intelligent Mobile Agents

Mobile agents are a useful paradigm for network coding providing many advantages and disadvantages. Unfortunately, widespread adoption of mobile agents has been hampered by the disadvantages, which could be said to outweigh the advantages. There is a variety of ongoing work to address these issues, and this is discussed. Ultimately, genetic algorithms are selected as the most interesting potential avenue. Genetic algorithms have many potential benefits for mobile agents. The primary benefit is the potential for agents to become even more adaptive to situational changes in the environment and/or emergent security risks. There are secondary benefits such as the natural obfuscation of functions inherent to genetic algorithms. Pitfalls also exist, namely the difficulty of defining a satisfactory fitness function and the variable execution time of mobile agents arising from the fact that it exists on a network. DNAgents 1.0, an original application of genetic algorithms to mobile agents is implemented and discussed, and serves to highlight these difficulties. Modifications of traditional genetic algorithms are also discussed. Ultimately, a combination of genetic algorithms and artificial life is considered to be the most appropriate approach to mobile agents. This allows the consideration of agents to be organisms, and the network to be their environment. Towards this end, a novel framework called DNAgents 2.0 is designed and implemented. This framework allows the continual evolution of agents in a network without having a seperate training and deployment phase. Parameters for this new framework were defined and explored. Lastly, an experiment similar to DNAgents 1.0 is performed for comparative purposes against DNAgents 1.0 and to prove the viability of this new framework

A hybrid genetic programming based decision making system for multi-agent systems of Robocup Soccer Simulation

In this thesis, a hybrid genetic programming approach is proposed for decision making system in the complex multi-agent domain of RoboCup Soccer Simulation. In the past, genetic programming was rarely used to evolve agents in this domain due to the difficulties and restrictions of the soccer simulation domain. The proposed approach consists of two phases, each of which tries to cover the other's restrictions and limitations. The first phase will produce some evolved individuals based on a GP algorithm with an off-game evaluation system and the second phase will use the best individuals of the first phase as input to run another GP algorithm to evolve players in the simulated game environment where evaluations are done during real-time runs of the simulator. It is observed that the individuals evolved after the second phase are able to outperform the same team with a decision making system which is not evolved

Enhanced Distributed Learning Classifier System For Simulated Mobile Robot Behaviours

The four basic behaviours of mobile robot are chasing, approaching, avoiding and escaping. The main problem in robotic system is in selecting the correct behaviour. The aim of this research is to overcome the behaviour selection problem. This thesis proposes methods that can overcome the problems of good behaviour selection and good behaviour deletion. It also addresses the problem of missing information, solves the problem of oscillating between correct and incorrect behaviours, and addresses the low efficiency in mapping the input to the correct behaviour. A Distributed Learning Classifier System (DLCS) consisting of five Learning Classifier Systems (LCS) with hierarchical architecture of three levels is used. An enhanced Bucket Brigade Algorithm (BBA) is developed to avoid the problem of choosing classifiers with high strength value but with incorrect behaviour. An approach that detects steady state value for calling genetic algorithm (GA) is proposed to overcome the problems of good classifiers deletion and the local minima trap. Finally, efficient solutions for covering detectors, supporting default hierarchies formation and the oscillation between correct and incorrect action are introduced to avoid performance failure, generalisation of classifiers that have the ability to cover the specific and general conditions, and loss of desirable classifiers respectively. Overall, the enhanced approaches performed well and the enhanced learning processes proposed in the current study makes robot learning more effective. The simulated robot is tested and results have shown that it performs better with the four basic behaviours. The simulated robot is also tested on many examples of a complex behaviour which is any combination of the four basic behaviours and the results have shown that it performs better with this type of behaviours as well

Towards Automated Design of Complex Modular Systems Inspired by Nature

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