The evolution of opinions in collectives is influenced by, and simultaneously influences, the interaction network. Simple rules like conformity and homophily drive the co-evolution of network and opinions, leading to the emergence of complex collective behaviors. Studying these behaviors gives insight into complex social dynamics, including the formation of echo chambers. This paper highlights how spatial information sources, and network connectivity shape echo chambers. We propose a potential solution to overcome the local trap of echo chambers by leveraging the mobility of agents.

Several anecdotal experimental observations suggest that physical constraints (e.g., in physics-based simulations of evolutionary robotics) can considerably increase the diversity of results obtained by evolutionary computation methods and can even yield surprises.

Our minimize surprise method evolves swarm robot controllers using a task-independent reward for prediction accuracy. Since no specific task is rewarded during optimization, various collective behaviors can emerge, as has also been shown in previous work. But so far, all generated behaviors were static or repetitive allowing for easy sensor predictions due to mostly constant sensor input. Our goal is to generate more dynamic behaviors that vary behavior based on changes in sensor input. We modify environment and agent capabilities, and extend the minimize surprise reward with additional components rewarding homing or curiosity. In preliminary experiments, we were able to generate first dynamic behaviors through our modifications, providing a promising basis for future work.

Inter-individual differences are studied in natural systems, such as fish, bees, and humans, as they contribute to the complexity of both individual and collective behaviors. However, individuality in artificial systems, such as robotic swarms, is undervalued or even overlooked. Agent-specific deviations from the norm in swarm robotics are usually understood as mere noise that can be minimized, for example, by calibration. We observe that robots have consistent deviations and argue that awareness and knowledge of these can be exploited to serve a task. We measure heterogeneity in robot swarms caused by individual differences in how robots act, sense, and oscillate. Our use case is Kilobots and we provide example behaviors where the performance of robots varies depending on individual differences. We show a non-intuitive example of phototaxis with Kilobots where the non-calibrated Kilobots show better performance than the calibrated supposedly “ideal” one. We measure the inter-individual variations for heterogeneity in sensing and oscillation, too. We briefly discuss how these variations can enhance the complexity of collective behaviors. We suggest that by recognizing and exploring this new perspective on individuality, and hence diversity, in robotic swarms, we can gain a deeper understanding of these systems and potentially unlock new possibilities for their design and implementation of applications.

Growing cities are a world-wide phenomenon and simultaneously awareness about potential dangers due to air pollution, heat, and pathogens is increasing. Integrated and permanent monitoring of environmental features in cities can help to establish an early warning system and to provide data for policy makers. In our new project ‘WatchPlant,’ we propose a green approach for urban monitoring by a network of sensors tightly coupled with natural plants. We want to develop a sustainable, energy-efficient bio-hybrid system that harvests energy from living plants and utilizes methods of phytosensing, that is, using natural plants as sensors. We present our concept, here with focus on Alife-related methods operating on the gathered plant data and the bio-hybrid network. With a self-organizing network of sensors, that are alive, we hope to contribute to our future of livable green cities.

Robot-plant bio-hybrid systems are getting increasing attention due to the wide range of applications they offer. Such synergies between robots and natural plants will allow, for example, establishing highly reliable environmental monitoring systems or growing the architecture of our future cities. We explore the latter application where robots exploit the plants’ ability to produce construction material, and plants exploit the robots’ sensing and computational capabilities. In our previous work, we used machine learning techniques to model plant behavior in their early life stages. We collected a 10-point plant stem description dataset and used it to train an LSTM as a forward model that predicts plant dynamics and drives the evolution of plant shaping controllers. Here, we show our vision to model plant behaviors in later stages, where full-plant morphology will be used to train state-of-the-art sequence modeling networks capable of simulating more complex plant dynamics.

Complementary to machine learning, controllers for swarm robotics can also be evolved using methods of evolutionary computation. Approaches such as novelty search and MAP-Elites go beyond mere fitness-based optimization by increasing the time spent on exploration. Instead of optimizing a fitness function, selective pressure towards unexplored behavior space is generated by forcing behavioral distance to previously seen behaviors. Ideally, we would like to define a generic behavioral distance function; however, effective distance functions are usually domain specific. Our minimize surprise approach concurrently evolves two artificial neural networks: one for action selection and one as world model. Selective pressure is implemented by rewarding good predictions of the world model. As an effect, the evolutionary dynamics push towards swarm behaviors that are easy to predict, that is, the robots virtually try to minimize surprise in their environment. Here, we compare minimize surprise to novelty search and, as baseline, a genetic algorithm in simulations of swarm robots. We observe a diversity of collective behaviors, such as aggregation, dispersion, clustering, line formation, etc. We find that minimize surprise is competitive to novelty search for the investigated swarm scenario, although it does not require a cleverly crafted domain-specific behavioral distance function.

In a collaborative society, sharing information is advantageous for each individual as well as for the whole community. Maximizing the number of agent-to-agent interactions per time becomes an appealing behavior due to fast information spreading that maximizes the overall amount of shared information. However, if malicious agents are part of society, then the risk of interacting with one of them increases with an increasing number of interactions. In this paper, we investigate the roles of interaction rates and times (aka edge life) in artificial societies of simulated robot swarms. We adapt their social networks to form proper trust sub-networks and to contain attackers. Instead of sophisticated algorithms to build and administrate trust networks, we focus on simple control algorithms that locally adapt interaction times by changing only the robots’ motion patterns. We successfully validate these algorithms in collective decision-making showing improved time to convergence and energy-efficient motion patterns, besides impeding the spread of undesired opinions.

Robot swarms are known to be robust to individual robot failures. However, a reduced swarm size causes a reduced swarm density. A too low swarm density may then decrease swarm performance, that should be compensated by adapting the individual behavior. Similarly, swarm behaviors can also be adapted to changes in the environment, such as dynamic light conditions. We study aggregation of swarm robots controlled by an extended variant of the BEECLUST algorithm. The robots are asked to aggregate at the brightest spot in their environment. Our approach efficiently adapts this swarm aggregation behavior to variability in swarm density and light conditions. First, each robot individually monitors its environment continuously by sampling its local swarm density and perceived light condition. Second, we exploit the collaboration of robots by letting them share features of these measurements with their neighbors by communication. In extensive robot swarm experiments with ten robots we validate our approach with dynamically changing swarm densities and under dynamic light conditions. We find an improved performance compared to robot swarms without communication and without awareness of the swarm density.