We propose a general mathematical methodology for studying the dynamics of multiagent systems in which complex collective behavior arises out of local interactions between many simple agents. The mathematical model is composed of a system of coupled differential equations describing the macroscopic, or collective, dynamics of an agent-based system. We illustrate our approach by applying it to a...

This paper presents the novel use of the Neural-endocrine architecture for swarm robotic systems. We make use of a number of behaviours to give rise to emergent swarm behaviour to allow a swarm of robots to collaborate in the task of foraging. Results show that the architecture is amenable to such a task, with the swarm being able to successfully complete the required task.

In this paper, we study how a swarm of robots adapts over time to solve a collaborative task using a distributed Embodied Evolutionary approach, where each robot runs an evolutionary algorithm and they locally exchange genomes and fitness values. Particularly, we study a collaborative foraging task, where the robots are rewarded for collecting food items that are too heavy to be collected indiv...

Reliable information is a crucial factor influencing decision-making and, thus, fitness in all animals. A common source of information comes from inadvertent cues produced by the behavior of conspecifics. Here we use a system of experimental evolution with robots foraging in an arena containing a food source to study how communication strategies can evolve to regulate information provided by su...

In current robotics research there is a vast body of work on algorithms and control methods for groups of decentralized cooperating robots, called a swarm or collective. These algorithms are generally meant to control collectives of hundreds or even thousands of robots; however, for reasons of cost, time, or complexity, they are generally validated in simulation only, or on a group of a few ten...

searching efficiency and handling time are two major components of functional response and are usually used to evaluate effectiveness of natural enemies. the effect of different foraging periods on the functional response of larval aphidoletes aphidimyza (rondani) (dip.: cecidomyiidae) feeding on third instar nymphs of aphis craccivora was studied. the experiment was conducted in terms of time-...

Reinforcement learning (RL) is a fundamental process by which organisms learn to achieve a goal from interactions with the environment. Using Artificial Life techniques we derive (near-)optimal neuronal learning rules in a simple neural network model of decision-making in simulated bumblebees foraging for nectar. The resulting networks exhibit efficient RL, allowing the bees to respond rapidly ...

One of the key innovations during the evolution of life on earth has been the emergence of efficient communication systems, yet little is known about the causes and consequences of the great diversity within and between species. By conducting experimental evolution in 20 independently evolving populations of cooperatively foraging simulated robots, we found that historical contingency in the oc...

Collective behavior of active elements inspired by mass of biological organisms is addressed. Especially, two topics are focused on among amazing behaviors performed by colony of ants. First, task allocation phenomena are treated from the viewpoint of proportion regulation of population between different states. Using a dynamical model consisting of elements and external “stock materials”, adap...

Abstract We present a biologically inspired design for swarm foraging based on ant’s pheromone deployment, where the is assumed to have very restricted capabilities. The robots do not require global or relative position measurements and fully decentralized needs no infrastructure in place. Additionally, system only requires one-hop communication over robot network, we make any assumptions about...