There is a great deal of concern expressed by many in the artificial intelligence (AI) community about the existential risk of this rapidly developing technology. This paper provides a discussion on some issues that need to be addressed to handle potential future risks and provides some new perspectives. The development of artificial intelligence is moving from relatively limited standalone to large-scale, complex distributed systems. However, potential risks such as malfunction, malicious attacks and mismatch of objective can occur from hardware and software failures or design errors. Moreover, a system controlled by high level AI can become unpredictable in its behaviours and thus ethical risks can emerge when such systems have to make a decision related to operational issues. Given that all new, disruptive, technologies have risks associated with them, what we need to do, as practitioners and users, is to find ways of mitigating those risks. We discuss applications of agent-based simulation to illustrate some of the risks and, potentially, how to mitigate them.

Purpose: The purpose of this paper is to review the current state of proceedings in the research area of automatic swarm design and discusses possible solutions to advance swarm robotics research.Design/methodology/approach: First, this paper begins by reviewing the current state of proceedings in the field of automatic swarm design to provide a basic understanding of the field. This should lead to the identification of which issues need to be resolved in order to move forward swarm robotics research. Then, some possible solutions to the challenges are discussed to identify future directions and how the proposed idea of incorporating learning mechanism could benefit swarm robotics design. Lastly, a novel evolutionary-learning framework for swarms based on epigenetic function is proposed with a discussion of its merits and suggestions for future research directions.Findings: The discussion shows that main challenge which is needed to be resolved is the presence of dynamic environment which is mainly caused by agent-to-agent and agent-to-environment interactions. A possible solution to tackle the challenge is by incorporating learning capability to the swarm to tackle dynamic environment.Originality/value: This paper gives a new perspective on how to improve automatic swarm design in order to move forward swarm robotics research. Along with the discussion, this paper also proposes a novel framework to incorporate learning mechanism into evolutionary swarm using epigenetic function.

The demand for transportation has increased significantly in recent decades in line with the increasing demand for passenger and freight mobility, especially in urban areas. One of the most negative impacts is the increasing level of traffic congestion. A possible short-term solution to solve this problem is to utilize a traffic control system. However, most traffic control systems still use classical control algorithms with the green phase sequence determined, based on a specific strategy. Studies have proven that this approach does not provide the expected congestion solution. In this paper, an adaptive traffic controller was developed that uses a reinforcement learning algorithm called deep Q-network (DQN). Since the DQN performance is determined by reward selection, an exponential reward function, based on the macroscopic fundamental diagram (MFD) of the distribution of vehicle density at intersections was considered. The action taken by the DQN is determining traffic phases, based on various rewards, ranging from pressure to adaptive loading of pressure and queue length. The reinforcement learning algorithm was then applied to the SUMO traffic simulation software to assess the effectiveness of the proposed strategy. The DQN-based control algorithm with the adaptive reward mechanism achieved the best performance with a vehicle throughput of 56,384 vehicles, followed by the classical and conventional control methods, such as Webster (50,366 vehicles), max-pressure (50,541 vehicles) and uniform (46,241 vehicles) traffic control. The significant increase in vehicle throughput achieved by the adaptive DQN-based control algorithm with an exponential reward mechanism means that the proposed traffic control could increase the area productivity, implying that the intersections could accommodate more vehicles so that the possibility of congestion was reduced. The algorithm performed remarkably in preventing congestion in a traffic network model of Central Jakarta as one of the world’s most congested cities. This result indicates that traffic control design using MFD as a performance measure can be a successful future direction in the development of reinforcement learning for traffic control systems.

This paper presents the design and implementation of Boid algorithm with proportional - integral controller to control the velocity of quadrotor which uses a camera-based localization method. The algorithm consists of three rules: cohesion, separation, and alignment in order to mimic the behavior of groups of animals such as bird colonies. The control action was applied to achieve a reference velocity and the controller parameters were obtained using Ziegler-Nichols tuning method. The localization method utilized sixteen combinations of cyan, magenta, yellow, and black color markers. These markers provide coordinate information for the quadrotor.

Purpose: This paper aims to propose a novel epigenetic learning (EpiLearn) algorithm, which is designed specifically for a decentralised multi-agent system such as swarm robotics.Design/methodology/approach: First, this paper begins with overview of swarm robotics and the challenges in designing swarm behaviour automatically. This should indicate the direction of improvements required to enhance an automatic swarm design. Second, the evolutionary learning (EpiLearn) algorithm for a swarm system using an epigenetic layer is formulated and discussed. The algorithm is then tested through various test functions to investigate its performance. Finally, the results are discussed along with possible future research directions.Findings: Through various test functions, the algorithm can solve non-local and many local minima problems. This article also shows that by using a reward system, the algorithm can handle the deceptive problem which often occurs in dynamic problems. Moreover, utilization of rewards from the environment in the form of a methylation process on the epigenetic layer improves the performance of traditional evolutionary algorithms applied to automatic swarm design. Finally, this article shows that a regeneration process that embeds an epigenetic layer in the inheritance process performs better than a traditional crossover operator in a swarm system.Originality/value: This paper proposes a novel method for automatic swarm design by taking into account the importance of multi-agent settings and environmental characteristics surrounding the swarm. The novel evolutionary learning (EpiLearn) algorithm using an epigenetic layer gives the swarm the ability to perform co-evolution and co-learning.

Obstacle avoidance in multi-agent systems is a critical area of research driven by advancements in autonomous technology and artificial intelligence. This review examines various approaches to path planning, formation control, and communication architectures, focusing on their effectiveness in static and dynamic environments. The research contribution is a comprehensive analysis of current techniques based on a structured selection process evaluating peer-reviewed studies through computational efficiency, real-time adaptivity, and scalability. The findings highlight the strengths and limitations of classical methods, such as the Improved Artificial Potential Field (IAPF), and modern techniques like Reinforcement Learning (RL) and Model Predictive Control (MPC). Comparative analysis reveals that while these approaches improve adaptivity, they also introduce challenges such as high computational loads, difficulties in large-scale multi-agent coordination, and sensitivity of parameter tuning. Additionally, existing formation control strategies depend highly on stable inter-agent communication, making them vulnerable to delays and failures in decentralized networks. This review identifies key research gaps and suggests future directions, including hybrid RL-MPC formation control, adaptive path planning algorithms, and scalable communication protocols to enhance multi-agent system performance in real-world applications.