In this paper, we examine the problem of controlling multiple behaviour-based autonomous robots. Based on observations made from the study of social insects, we propose ve simple mechanisms used to invoke group behaviour in simple sensor-based mobile robots. The proposed mechanisms allow populations of behaviour-based robots to perform tasks without centralized control or use of explicit communication. We have veri ed our collective control strategies by designing a robot population simulator called SimbotCity. Wehave also constructed a system of ve homogeneous sensor-based mobile robots, capable of achieving simple collective tasks, to demonstrate the feasibility of some of the control mechanisms.

Foragers of the ant Lasius niger exploiting a 1 M sugar source were found to lay 43 % more trail marks than those exploiting a O. 05 or a O. 1 M source. The trail laying per forager decreased during the course of individual recruitment episodes, and the mean lifetime of the trail pheromone was estimated to be 47 min. A mathematical function describing the probability that a forager chooses one of two paths in relation to the amount of trail pheromone on them closely fitted experimental data. These results were incorporated into a model describing the recruitment dynamics of L. niger. Simulations of this model showed that the observed modulation of trail laying with respect to food source quality is sufficient in itself to account for the systematic selection of the richer source seen in the experiments. KEY WORDS: Lasius niger; trail following; trail laying; pheromone evaporation; mathematical model.

This Chapter summarizes some of the well known stigmergic computational techniques inspired by nature, mainly for optimization problems developed by mimicking social insects’ behavior. Some facts about social insects namely ants, bees and termites are presented with an emphasis on how they could interact and self organize for solving real world problems. We focused on ant colony optimization algorithm, bees behavior inspired algorithms, particle swarm optimization algorithm and bacterial foraging algorithm.

Abstract—We present examples of agent-based and stochastic models of competition and business processes in economics and finance. We start from as simple as possible models, which have microscopic, agent-based, versions and macroscopic treatment in behavior. Microscopic and macroscopic versions of herding model proposed by Kirman and Bass diffusion of new products are considered in this contribution as two basic ideas. Further we demonstrate that general herding behavior can be considered as a background of nonlinear stochastic model of financial fluctuations. Keywords-agent based modeling; stochastic modeling; business models; financial market models. I.

Collective decision and foraging patterns in ants and honeybees

Ant algorithms and flocking algorithms are the two main programming paradigms in swarm intelligence. They are built on stochastic models, widely used in optimization problems. However, though this modeling leads to highperformance algorithms, some mechanisms, like the symmetry break in ant decision, are still not well understood at the local ant level. Moreover, there is currently no modeling approach which joins the two paradigms. This paper proposes an entirely novel approach to the mathematical foundations of swarm algorithms: contrary to the current stochastic approaches, we show that an alternative deterministic model exists, which has its origin in deterministic chaos theory. We establish a reactive multi-agent system, based on logistic nonlinear decision maps, and designed according to the influence-reaction scheme. The rewriting of the decision functions leads to a new way of understanding the swarm phenomena in terms of state synchronization, and enables the analysis of their convergence behavior through bifurcation diagrams. We apply our approach on two concrete examples of each algorithm class, in order to demonstrate its general applicability. 1

In this paper we investigate the foraging activity of an invasive ant species, the big headed ant Pheidole megacephala. We establish that the ants ’ behavior is consistent with the use of two different pheromone signals, both of which recruit nestmates. Our experiments suggest that during exploration the ants deposit a long-lasting pheromone that elicits a weak recruitment of nestmates, while when exploiting food the ants deposit a shorter lasting pheromone eliciting a much stronger recruitment. We further investigate experimentally the role of these pheromones under both static and dynamic conditions and develop a mathematical model based on the hypothesis that exploration locally enhances exploitation, while exploitation locally suppresses exploration. The model and the experiments indicate that exploratory pheromone allows the colony to more quickly mobilize foragers when food is discovered. Furthermore, the combination of two pheromones allows colonies to track changing foraging conditions more effectively than would a single pheromone. In addition to the already known causes for the ecological success of invasive ant species, our study suggests that their opportunistic strategy of rapid food discovery and ability to react to changes in the environment may have strongly contributed to their dominance over native species.