Improving the adaptability of simulated evolutionary swarm robots in dynamically changing environments

One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store 'good behaviour' and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment

RESILIENCE OF SOCIAL-ECOLOGICAL SYSTEMS IN EUROPEAN RURAL AREAS: THEORY AND PROSPECTS

In today’s world, rural areas are confronted with a spectrum of changes. These changes have multiple characters, varying from changes in ecosystem conditions to socioeconomic impacts, such as food- and financial crises. They present serious problems to rural management and largely affect future perspectives of rural areas. Rural resilience refers to the capacity of a rural region to adapt to changing external circumstances in such a way that a satisfactory standard of living is maintained, while coping with its inherent ecological, economic and social vulnerability. Rural resilience describes how rural areas are affected by external shocks and how it influences system dynamics. This paper further eradicates on this concept, by exploring in detail what the importance is of resilience theory within rural areas. An answer is tried to be given to the question how to detect resilience in rural areas, by reviewing the existing literature and to the question how to enhance resilient rural development. Finally questions are formulated for further research within the field of rural resilience.Resilience, social-ecological systems, rural development, complex adaptive systems, system dynamics, Agribusiness, Agricultural and Food Policy, Environmental Economics and Policy,

Visualizing Coevolution With CIAO Plots

In a previous paper [2], we introduced a number of visualization techniques that we had developed for monitoring the dynamics of artificial competitive co-evolutionary systems. One of these techniques involves evaluating the performance of an individual from the current population in a series of trials against opponents from all previous generations, and visualizing the results as a 2-d grid of shaded cells or pixels: qualitative patterns in the shading can indicate different classes of co-evolutionary dynamic. As this technique involves pitting a Current Individual against Ancestral Opponents, we referred to the visualizations as CIAO plots. Since then, a number of other authors studying the dynamics of competitive co-evolutionary systems have used CIAO plots or close derivatives to help illuminate the dynamics of their systems, and it has become something of a de facto standard visualization technique. In this very brief paper we summarise the rationale for CIAO plots, explain the method of constructing a CIAO plot, and review important recent results that identify significant limitations of this technique

An experimental study on evolutionary reactive behaviors for mobile robots navigation

Mobile robot's navigation and obstacle avoidance in an unknown and static environment is analyzed in this paper. From the guidance of position sensors, artificial neural network (ANN) based controllers settle the desired trajectory between current and a target point. Evolutionary algorithms were used to choose the best controller. This approach, known as Evolutionary Robotics (ER), commonly resorts to very simple ANN architectures. Although they include temporal processing, most of them do not consider the learned experience in the controller's evolution. Thus, the ER research presented in this article, focuses on the specification and testing of the ANN based controllers implemented when genetic mutations are performed from one generation to another. Discrete-Time Recurrent Neural Networks based controllers were tested, with two variants: plastic neural networks (PNN) and standard feedforward (FFNN) networks. Also the way in which evolution was performed was also analyzed. As a result, controlled mutation do not exhibit major advantages against over the non controlled one, showing that diversity is more powerful than controlled adaptation.Facultad de Informátic

Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent

Oscillatory activity is ubiquitous in nervous systems, with solid evidence that synchronisation mechanisms underpin cognitive processes. Nevertheless, its informational content and relationship with behaviour are still to be fully understood. In addition, cognitive systems cannot be properly appreciated without taking into account brain–body– environment interactions. In this paper, we developed a model based on the Kuramoto Model of coupled phase oscillators to explore the role of neural synchronisation in the performance of a simulated robotic agent in two different minimally cognitive tasks. We show that there is a statistically significant difference in performance and evolvability depending on the synchronisation regime of the network. In both tasks, a combination of information flow and dynamical analyses show that networks with a definite, but not too strong, propensity for synchronisation are more able to reconfigure, to organise themselves functionally and to adapt to different behavioural conditions. The results highlight the asymmetry of information flow and its behavioural correspondence. Importantly, it also shows that neural synchronisation dynamics, when suitably flexible and reconfigurable, can generate minimally cognitive embodied behaviour