Jean-Marc Montanier

Hybrid Societies: Challenges and Perspectives in the Design of Collective Behavior in Self-Organizing Systems

Hybrid societies are self-organizing, collective systems, which are composed of different components, for example, natural and artificial parts (bio-hybrid) or human beings interacting with and through technical systems (socio-technical). Many different disciplines investigate methods and systems closely related to the design of hybrid societies. A stronger collaboration between these disciplines could allow for re-use of methods and create significant synergies. We identify three main areas of challenges in the design of self-organizing hybrid societies. First, we identify the formalization challenge. There is an urgent need for a generic model that allows a description and comparison of collective hybrid societies. Second, we identify the system design challenge. Starting from the formal specification of the system, we need to develop an integrated design process. Third, we identify the challenge of interdisciplinarity. Current research on self-organizing hybrid societies stretches over many different fields and hence requires the re-use and synthesis of methods at intersections between disciplines. We then conclude by presenting our perspective for future approaches with high potential in this area.

Repellent pheromones for effective swarm robot search in unknown environments

In time-critical situations such as rescue missions, effective exploration is essential. Exploration of such unknown environments may be achieved through the dispersion of a swarm of robots. Recent research has turned to biology where pheromone trails provide a form of collective memory of visited areas. Rather than the attractive pheromones that have been the focus of much research, this paper considers locally distributed repellent pheromones. Further, the conditions for maximising search efficiency are investigated.

Behavioral Specialization in Embodied Evolutionary Robotics: Why So Difficult?

Embodied evolutionary robotics is an on-line distributed learning method used in collective robotics where robots are facing open environments. This paper focuses on learning behavioural specialization, as defined by robots being able to demonstrate different kind of behaviours at the same time (e.g. division of labour). Using a foraging task with two resources available in limited quantities, we show that behavioural specialization is unlikely to evolve in the general case, unless very specific conditions are met regarding interactions between robots (a very sparse communication network is required) and the expected outcome of specialization (specialization into groups of similar sizes is easier to achieve). We also show that the population size (the larger the better) as well as the selection scheme used (favoring exploration over exploitation) both play important -- though not always mandatory -- roles. This research sheds light on why existing embodied evolution algorithms are limited with respect to learning efficient division of labour in the general case, ie. where it is not possible to guess before deployment if behavioural specialization is required or not, and gives directions to overcome current limitations.

The ChIRP Robot: A Versatile Swarm Robot Platform

Swarm Robotic experiments are ideally performed on real robots. However, a cost versus versatility trade-off exists between simpler and more advanced swarm robots. The simpler swarm robots provide limited features and thus, although suitable for simpler swarm tasks, lack versatility in the types of tasks that may be approached. On the other hand, advanced swarm robots provide a broader range of features, enabling a wide range of tasks to be approached, from simple to advanced. To address this trade-off, an available and versatile robotic platform is proposed: the Cheap, Interchangeable Robotic Platform — the ChIRP robot. The basic platform implements mandatory features required for most swarm experiments, providing a cheap simple and extendible platform. Further, extensions (including both electronic and mechanical features) enable an advanced specialised swarm robot tailored to the needs of a given research agenda. The design considerations and implementation details are presented herein. Further, an example swarm task for the basic ChIRP robot is presented together with an example task illustrating an extension of the ChIRP robot.

Evolution of Altruism and Spatial Dispersion: an Artificial Evolutionary Ecology Approach

This paper concerns the evolution of altruism in a population of autonomous agents. It explores the relation between altruistic behaviours and spatial dispersion in open-ended evolution whenever energetic constraints must be addressed. A method derived from Embodied Evolution is used to model the spatial interactions between agents from an individual perspective. Firstly, results show that spatial dispersion and levels of altruism are strongly correlated, which confirms theoretical results from biology, but also that this relation may be overshadowed by the complex interactions at work in the ecosystem. Secondly, this paper investigates how robust altruistic behaviours able to cope with various environmental pressures may be evolved. In particular, it is shown that there is a trade-off between efficiency and versatility: the ability to perform well accross a wide range of environmental conditions often comes at the cost of sub-optimal performance in terms of survival, especially when compared to more constrained (and less versatile) evolved strategies.

Modelling the co-evolution of trade and culture in past societies

This paper presents a new framework to study the co-evolution of cultural change and trade. The design aims for a trade-off between the flexibility necessary for the implementation of multiple models and the structure necessary for the comparison between the models implemented. To create this framework we propose an Agent-Based Model relying on agents producing, exchanging and associating values to a list of goods. We present the key concepts of the framework and two examples of its implementation which allow us to show the flexibility of our framework. Moreover, we compare the results obtained by the two models, thus validating the structure of the framework. Finally, we validate the implementation of a trading model by studying the price structure it produces.

Adaptive self-assembly in swarm robotics through environmental bias

A swarm of robots may face challenges in unknown environments where self-assembly is a necessity e.g. crossing difficult areas. When exploring such environments, the self-assembly process has to be triggered only where needed and only for those robots required, leaving other robots to continue exploration. Further, self-assembled robots should dis-assemble when assembled structures are no longer required. Strategies have thus to be learned to trigger self-assembly and dis-assembly so as to meet the needs of the environment. Research has focused on the learning of strategies where all robots of the swarm had to adopt one common strategy: either self-assembly or dis-assembly. The work herein studies how strategies using both self-assembly and dis-assembly can be learned within the same swarm. Further, the effect of the different environments on this challenge is presented.

Benefits of proportionate selection in embodied evolution: a case study with behavioural specialization

We, as well as others, have already shown in previous works that reproductive isolation and a large population size are critical to achieve behavioral specialization in embodied evolutionary robotics. Here, we extend our previous work from [3] by experimentally demonstrating that fitness-proportionate and rank-based selection operators largely outperform other selection operators when it comes to evolving behavioral specialization.

Evolution of altruism: spatial dispersion and consumption strategies

The work presented here is concerned with the evolution of altruistic behaviour in a population of agents subject to an open-ended evolutionary process. In this context, it is well known that genotypic relatedness plays a key role with respect to the level of altruism that can be observed. Such relatedness may be enforced through particular selection mechanism (e.g. kin-recognition) as well as particular dispersion strategies (e.g. low dispersion favours local interactions). This paper presents results on the importance of the evolution of particular dispersion strategies whenever consumption strategies are enforced. A key result from this paper is that whenever altruism is difficult to display when consuming food (i.e. being unable to share while eating), higher dispersion behaviour are evolved, which is a counterintuitive result at first sight.