Jean-Louis Deneubourg

Collective patterns and decision-making

Autocatalytic interactions between the members of an animal group or society, and particularly chemically or visually mediated allelomimesis, can be an important factor in the organisation of their collective activity. Furthermore, the interactions between the individuals and the environment allow different collective patterns and decisions to appear under different conditions, with the same individual behaviour. While most clearly demonstrable in social insects, these principles are fundamental to schools of fishes, flocks of birds, groups of mammals, and many other social aggregates. The analysis of collective behaviour in these terms implies detailed observation of both individual and collective behaviour, combined with mathematical modelling to link the two.

From local actions to global tasks: stigmergy and collective robotics

This paper presents a series of experiments where a group of mobile robots gather 81 randomly distributed objects and cluster them into one pile. Coordination of the agents’ movements is achieved through stigmergy. This principle, originally developed for the description of termite building behaviour, allows indirect communication between agents through sensing and modification of the local environment which determines the agents’ behaviour. The efficiency of the work was measured for groups of one to five robots working together. Group size is a critical factor. The mean time to accomplish the task decreases for one, two, and three robots respectively, then increases again for groups of four and five agents, due to an exponential increase in the number of interactions between robots which are time consuming and may eventually result in the destruction of existing clusters. We compare our results with those reported by Deneubourg et al. (1990) where similar clusters are observed in ant colonies, generated by the probabilistic behaviour of workers.

Trails and U-turns in the selection of a path by the ant Lasius niger

A series of laboratory experiments show how colonies of the ant Lasius niger systematically select the shorter of two paths of varying length and form between nest and foraging area, and do so with a large majority of foragers. Three different mechanisms were considered to contribute to this selection, and were evaluated by comparing mathematical models with experimental data. Bi-directional trail laying was shown to contribute much less than U-turns, where the proportion of ants that turned back depended on the paths geometry and length, and also on the trail strength. A strong modulation of the amount of pheromone deposited per ant as a function of the paths geometry was found. This modulation can also contribute to the selection of the shorter branch in the experiments where the rate of U-turns is initially the same. The selection of the path is shown to be a collective process whereby trail laying and following amplifies small initial differences in the traffic on each path caused by these three mechanisms. The foragers show no significant tendency to follow the path they used previously.

Information processing in social insects.

The book provides a first comprehensive overview of both experimental and theoretical research on information processing in insect societies. Its purpose is to make the reader familiar with the methodology and ways of thinking followed by scientists at the leading edge of the field. The book is aimed at postgraduate students and researchers working on social insects and insects that live in groups as well as any reader interested in behavioural ecology, communication and social organization.

Social Integration of Robots into Groups of Cockroaches to Control Self-Organized Choices

Collective behavior based on self-organization has been shown in group-living animals from insects to vertebrates. These findings have stimulated engineers to investigate approaches for the coordination of autonomous multirobot systems based on self-organization. In this experimental study, we show collective decision-making by mixed groups of cockroaches and socially integrated autonomous robots, leading to shared shelter selection. Individuals, natural or artificial, are perceived as equivalent, and the collective decision emerges from nonlinear feedbacks based on local interactions. Even when in the minority, robots can modulate the collective decision-making process and produce a global pattern not observed in their absence. These results demonstrate the possibility of using intelligent autonomous devices to study and control self-organized behavioral patterns in group-living animals.

Response threshold reinforcements and division of labour in insect societies.

A model of division of labour in insect societies, based on variable response thresholds is introduced. Response thresholds refer to the likelihood of reacting to task–associated stimuli. Low–threshold individuals perform tasks at a lower level of stimulus than high–threshold individuals. Within individual workers, performing a given task induces a decrease in the corresponding threshold, and not performing the task induces an increase in the threshold. This combined reinforcement process leads to the emergence of specialized workers, i.e. workers that are more responsive to stimuli associated with particular task requirements, from a group of initially identical individuals. Predictions of the dynamics of task specialization resulting from this model are presented. Predictions are also made as to what should be observed when specialists of a given task are removed from the colony and reintroduced after a varying amount of time: the colony does not recover the same state as that prior to the perturbation, and the difference between before and after the perturbation is more strongly marked as the time between separation and reintroduction increases.

Swarm-Bot: A New Distributed Robotic Concept

The swarm intelligence paradigm has proven to have very interesting properties such as robustness, flexibility and ability to solve complex problems exploiting parallelism and self-organization. Several robotics implementations of this paradigm confirm that these properties can be exploited for the control of a population of physically independent mobile robots.The work presented here introduces a new robotic concept called swarm-bot in which the collective interaction exploited by the swarm intelligence mechanism goes beyond the control layer and is extended to the physical level. This implies the addition of new mechanical functionalities on the single robot, together with new electronics and software to manage it. These new functionalities, even if not directly related to mobility and navigation, allow to address complex mobile robotics problems, such as extreme all-terrain exploration.The work shows also how this new concept is investigated using a simulation tool (swarmbot3d) specifically developed for quickly designing and evaluating new control algorithms. Experimental work shows how the simulated detailed representation of one s-bot has been calibrated to match the behaviour of the real robot.

Evolving Self-Organizing Behaviors for a Swarm-Bot

In this paper, we introduce a self-assembling and self-organizing artifact, called a swarm-bot, composed of a swarm of s-bots, mobile robots with the ability to connect to and to disconnect from each other. We discuss the challenges involved in controlling a swarm-bot and address the problem of synthesizing controllers for the swarm-bot using artificial evolution. Specifically, we study aggregation and coordinated motion of the swarm-bot using a physics-based simulation of the system. Experiments, using a simplified simulation model of the s-bots, show that evolution can discover simple but effective controllers for both the aggregation and the coordinated motion of the swarm-bot. Analysis of the evolved controllers shows that they have properties of scalability, that is, they continue to be effective for larger group sizes, and of generality, that is, they produce similar behaviors for configurations different from those they were originally evolved for. The portability of the evolved controllers to real s-bots is tested using a detailed simulation model which has been validated against the real s-bots in a companion paper in this same special issue.

Probabilistic behaviour in ants: A strategy of errors?

Animal behaviour is probabilistic. This is exemplified by the communication behaviour of ants during food-searching. Experimental evidence demonstrates that species differ in the accuracy of their recruitment. We show here, with the help of a very simple mathematical model, that the randomness of behaviour can have an adaptative advantage for ants. The model demonstrates that the degree of randomness could be optimally “tuned” to particular ecological conditions, such as food quantity and distribution.

Quantitative Study of the Fixed Threshold Model for the Regulation of Division of Labour in Insect Societies

A simple model of regulation of division of labour in insect societies is introduced and studied. Individuals are assumed to respond to task-related stimuli with response thresholds. When the intensity of a particular stimulus exceeds an individual’s response threshold, the individual engages in task performance with high probability, and successful task performance reduces the intensity of the stimulus. If individuals belonging to different (physical or behavioural) castes have different response thresholds, and if thresholds are assumed to remain fixed over the timescales of experiments, this model can account for some observations on ant species of Pheidole (Wilson 1984).