Game-theoretic algorithm for interdependent infrastructure network restoration in a decentralized environment

Having reliable interdependent infrastructure networks is vital for well-being of a safe and productive society. Systems are vulnerable to failure or performance loss due to their interdependence among various networks, as each failure can propagate through the whole system. Although the conventional view has concentrated on optimizing the restoration of critical interdependent infrastructure networks using a centralized approach, having a lone actor as a decision-maker in the system is substantially different from the actual restoration decision environment, wherein infrastructure utilities make their own decisions about how to restore their network service. In a decentralized environment, the definition of whole system optimality does not apply as each decision-maker's interest may not converge with the others. Subsequently, this results in each decision-maker developing its own reward functions. Therefore, in this study, we address the concern of having multiple decision-makers with various payoff functions in interdependent networks by proposing a decentralized game theory algorithm for finding Nash equilibria solutions for network restoration in postdisaster situations.

Steering herds away from dangers in dynamic environments

Shepherding, the task of guiding a herd of autonomous individuals in a desired direction, is an essential skill to herd animals, enable crowd control and rescue from danger. Equipping robots with the capability of shepherding would allow performing such tasks with increased efficiency and reduced labour costs. So far, only single-robot or centralized multi-robot solutions have been proposed. The former is unable to observe dangers at any place surrounding the herd, and the latter does not generalize to unconstrained environments. Therefore, we propose a decentralized control algorithm for multi-robot shepherding, where the robots maintain a caging pattern around the herd to detect potential nearby dangers. When danger is detected, part of the robot swarm positions itself in order to repel the herd towards a safer region. We study the performance of our algorithm for different collective motion models of the herd. We task the robots to shepherd a herd to safety in two dynamic scenarios: (i) to avoid dangerous patches appearing over time and (ii) to remain inside a safe circular enclosure. Simulations show that the robots are always successful in shepherding when the herd remains cohesive, and enough robots are deployed.

Different bees, different needs: how nest-site requirements have shaped the decision-making processes in homeless honeybees (Apis spp.)

During reproductive swarming, a honeybee swarm needs to decide on a new nest site and then move to the chosen site collectively. Most studies of swarming and nest-site selection are based on one species, Apis mellifera Natural colonies of A. mellifera live in tree cavities. The quality of the cavity is critical to the survival of a swarm. Other honeybee species nest in the open, and have less strict nest-site requirements, such as the open-nesting dwarf honeybee Apis floreaApis florea builds a nest comprised of a single comb suspended from a twig. For a cavity-nesting species, there is only a limited number of potential nest sites that can be located by a swarm, because suitable sites are scarce. By contrast, for an open-nesting species, there is an abundance of equally suitable twigs. While the decision-making process of cavity-nesting bees is geared towards selecting the best site possible, open-nesting species need to coordinate collective movement towards areas with potential nest sites. Here, we argue that the nest-site selection processes of A. florea and A. mellifera have been shaped by each species' specific nest-site requirements. Both species use the same behavioural algorithm, tuned to allow each species to solve their species-specific problem.This article is part of the theme issue 'Collective movement ecology'.