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Swarm behavior using Boids-like models has been studied primarily using close-proximity spatial sensory information (e.g., vision range). In this study, we propose a novel approach in which the classic definition of boids' neighborhood that relies on sensory perception and Euclidian space locality is replaced with graph-theoretic network-based proximity mimicking communication and social networks. We demonstrate that networking the boids leads to faster swarming and higher quality of the formation. We further investigate the effect of adversarial learning, whereby an observer attempts to reverse engineer the dynamics of the swarm through observing its behavior. The results show that networking the swarm demonstrated a more robust approach against adversarial learning than local-proximity neighborhood structure.

Swarm behavior using Boids-like models has been studied primarily using close-proximity spatial sensory information (e.g., vision range). In this study, we propose a novel approach in which the classic definition of boids' neighborhood that relies on sensory perception and Euclidian space locality is replaced with graph-theoretic network-based proximity mimicking communication and social networks. We demonstrate that networking the boids leads to faster swarming and higher quality of the formation. We further investigate the effect of adversarial learning, whereby an observer attempts to reverse engineer the dynamics of the swarm through observing its behavior. The results show that networking the swarm demonstrated a more robust approach against adversarial learning than local-proximity neighborhood structure.

This paper deals with an approach to path planning by obstacle avoidancefor multi-agent systems. An effective framework is presented based on theParticle Swarm Optimization (PSO) method; an evolutionary computation(EC) technique that uses the dynamics of the swarm to search the solutions for theoptimization problems. It describes the path replanning technique and obstacleavoidance for autonomous multi-agent systems. A simultaneous replanning conceptis incorporated into the path planning to avoid both static and dynamic obstacles.This proposed algorithm reduces the computational time of the path planning. In thedynamic environment, the numerical results show that the Simultaneous ReplanningVectorized Particle Swarm Optimization (SRVPSO) algorithm is effective andalso efficient for multi-agent systems.

In this paper, we present an evolutionary optimization based path planning algorithm at Terminal Airspace (TAS) that provides a near optimal aircraft arrival sequence at Final Approach Fix (FAF). The sequence obtained minimizes the inter-arrival time as well as provides conflict free path planning to an Air Traffic Controller (ATC). A classic Genetic Algorithm (GA) based optimization technique with conflict detection and resolution is developed. Conflict between any two aircraft is detected based on their future arrival time at the waypoint and resolved by stretching the gap between those two aircraft. The proposed algorithm is compared with the traditional GA. Results indicate that the proposed approach obtains a near optimal solution compared to the traditional GA based algorithm which does not consider TAS constraints.

Google Machine Reassignment Problem (GMRP) is a real world problem proposed at ROADEF/EURO challenge 2012 competition which must be solved within 5 min. GMRP consists in reassigning a set of services into a set of machines for which the aim is to improve the machine usage while satisfying numerous constraints. This paper proposes an evolutionary simulating annealing (ESA) algorithm for solving this problem. Simulating annealing (SA) is a single solution based heuristic, which has been successfully used in various optimisation problems. The proposed ESA uses a population of solutions instead of a single solution. Each solution has its own SA algorithm and all SAs work in parallel manner. Each SA starts with different initial solution which can lead to a different search path with distinct local optima. In addition, mutation operators are applied once the solution cannot be improved for a certain number of iterations. This will not only help the search avoid being trapped in a local optima, but also reduce computation time. Because new solutions are not generated from scratch but based on existing ones. This study shows that the proposed ESA method can outperform state of the art algorithms on GMRP.

This paper offers a multi-disciplinary review of knowledge acquisition methods in human activity systems. The review captures the degree of involvement of various types of agencies in the knowledge acquisition process, and proposes a classification with three categories of methods: the human agent, the human-inspired agent, and the autonomous machine agent methods. In the first two categories, the acquisition of knowledge is seen as a cognitive task analysis exercise, while in the third category knowledge acquisition is treated as an autonomous knowledge-discovery endeavour. The motivation for this classification stems from the continuous change over time of the structure, meaning and purpose of human activity systems, which are seen as the factor that fuelled researchers' and practitioners' efforts in knowledge acquisition for more than a century. We show through this review that the KA field is increasingly active due to the higher and higher pace of change in human activity, and conclude by discussing the emergence of a fourth category of knowledge acquisition methods, which are based on red-teaming and co-evolution.

Working memory accounts for the ability of humans to perform cognitive processing, by handling both the representation of information (the mental picture forming the situation awareness) and the space required for processing these information (skill processing). The more complex the skills are, the more processing space they require, the less space becomes available for storage of information. This interplay between situation awareness and skills is critical in many applications. Theoretically, it is less understood in cognition and neuroscience. In the meantime, and practically, it is vital when analysing the mental processes involved in safety-critical domains.

For real-time optical image storage and processing, we have systematically studied self-pumped phase conjugation (SPPC) in barium titanate (BaTio3) crystals (from Sanders Associates Inc., Merrimack, NH) in terms of the incident angle theta, the impact position 1 on the crystal surface, the distance L between tile imaging lens and the impact point on the crystal face the argon-ion laser power P (p-wave with various wavelengths), and the ambient temperature T. We found that the stability of the temporal SPPC intensity I sub pc(t) strongly depends on these geometrical parameters, theta, l, and L. The objective in this paper is to identify optimal conditions in order to stabilize and maximize the temporal evolution of the SPPC reflectivity R(t) which is defined as the ratio of IPC(t) to the intensity I sub M retro-reflected by a mirror at the position of the crystal to the detector. We have defined the optimal (or stable) condition for the SPPC as Rmax - R(t)/Rmax, < 0.1,'where Rmax, is the maximum value of R(t) (recorded within 10 minutes to 1 hour) and also the maximum value in terms of the above geometrical and physical parameters.