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This paper surveys the literature over a period of the last decades in the field of self-organising multi-agent systems. Self-organisation has been extensively studied and applied in multi-agent systems and other fields, e.g., sensor networks and grid systems. Self-organisation mechanisms in other fields have been thoroughly surveyed. However, there has not been a survey of self-organisation mechanisms developed for use in multiagent systems. The aim of this paper is to provide a survey of existing literature on self-organisation mechanisms in multiagent systems. This paper also highlights the future work on the key research issues in multi-agent systems. This paper serves as a guide and a starting point for anyone who will conduct research on self-organisation in multi-agent systems. Also, this paper complements existing survey studies of self-organisation in multi-agent systems.

Sleep/wake-up scheduling is one of the fundamental problems in wireless sensor networks, since the energy of sensor nodes is limited and they are usually unrechargeable. The purpose of sleep/wake-up scheduling is to save the energy of each node by keeping nodes in sleep mode as long as possible (without sacrificing packet delivery efficiency) and thereby maximizing their lifetime. In this paper, a self-adaptive sleep/wake-up scheduling approach is proposed. Unlike most existing studies that use the duty cycling technique, which incurs a tradeoff between packet delivery delay and energy saving, the proposed approach, which does not us duty cycling, avoids such a tradeoff. The proposed approach, based on the reinforcement learning technique, enables each node to autonomously decide its own operation mode (sleep, listen, or transmission) in each time slot in a decentralized manner. Simulation results demonstrate the good performance of the proposed approach in various circumstances.

Abstract-This paper presents a hybrid multi-agent framework with a Q-learning algorithm to support rapid restoration of power grid systems following catastrophic disturbances involving loss of generators. This framework integrates the advantages of both centralised and decentralised architectures to achieve accurate decision making and quick responses when potential cascading failures are detected in power systems. By using this hybrid framework, which does not rely on a centralised controller, the single point of failure in power grid systems can be avoided. Further, the use of the Q-learning algorithm developed in conjunction with the restorative framework can help the agents to make accurate decisions to protect against cascading failures in a timely manner without requiring a global reward signal. Simulation results demonstrate the effectiveness of the proposed approach in comparison with the typical centralised and decentralised approaches based on several evaluation attributes.

Wireless sensor networks (WSNs) have been widely investigated in recent years. One of the fundamental issues in WSNs is packet routing, because in many application domains, packets have to be routed from source nodes to destination nodes as soon and as energy efficiently as possible. To address this issue, a large number of routing approaches have been proposed. Although every existing routing approach has advantages, they also have some disadvantages. In this paper, a multi-agent framework is proposed that can assist existing routing approaches to improve their routing performance. This framework enables each sensor node to build a cooperative neighbour set based on past routing experience. Such cooperative neighbours, in turn, can help the sensor to effectively relay packets in the future. This framework is independent of existing routing approaches and can be used to assist many existing routing approaches. Simulation results demonstrate the good performance of this framework in terms of four metrics: average delivery latency, successful delivery ratio, number of live nodes and total sensing coverage.

Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However, alongside all its advancements, problems have also emerged, such as privacy violations, security issues and model fairness. Differential privacy, as a promising mathematical model, has several attractive properties that can help solve these problems, making it quite a valuable tool. For this reason, differential privacy has been broadly applied in AI but to date, no study has documented which differential privacy mechanisms can or have been leveraged to overcome its issues or the properties that make this possible. In this paper, we show that differential privacy can do more than just privacy preservation. It can also be used to improve security, stabilize learning, build fair models, and impose composition in selected areas of AI. With a focus on regular machine learning, distributed machine learning, deep learning, and multi-agent systems, the purpose of this article is to deliver a new view on many possibilities for improving AI performance with differential privacy techniques.

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