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We present an approach for change-based (as opposed to state-based) model persistence that can facilitate high-performance incremental model processing (e.g. validation, transformation) by minimising the cost of change identification when models evolve. We illustrate a prototype that implements the proposed approach on top of the Eclipse Modelling Framework and we present a roadmap for further work in this direction.

Iterated learning takes place when the input into a particular individual’s learning process is itself the output of another individual’s learning process. This is an important feature to capture when investigating human language change, or the dynamics of culturally learned behaviours in general. Over the last fifteen years, the Iterated Learning Model (ILM) has been used to shed light on how the population-level characteristics of learned communication arise. However, until now each iteration of the model has tended to feature a single immature language user learning from their interactions with a single mature language user. Here, the ILM is extended to include a population of immature and mature language users. We demonstrate that the structure and make-up of this population influences the dynamics of language change that occur over generational time. In particular, we show that, by increasing the number of trainers from which an agent learns, the agent in question learns a fully compositional language at a much faster rate, and with less training data. It is also shown that, so long as the number of mature agents is large enough, this finding holds even if a learner’s trainers include other agents that do not yet posses full linguistic competence.

Experiments with worker termites constructing a royal chamber around a termite queen in species Macrotermes subhyalinus (Rambur) have shown that both trail and cement pheromones are involved and necessary for the successful formation of pillars during the building process. However, earlier models of the construction were able to demonstrate stigmergic pillar formation with cement pheromone alone. We present results from a new three-dimensional agent-based model, developed to investigate the role of trail pheromone in the construction process. The model is able to demonstrate how, if the properties of the cement pheromone are altered so that its attractive influence is more localised than in earlier models, termites are unable to produce significant pillar formation. The model shows how the addition of trail deposition and following effectively increases the range of the stigmergic effect so that pillar formation is restored. The presence of trail pheromone also results in pillars which are narrower than those produced by cement pheromone alone, and which show more pronounced lateral extensions. Additionally the paths that the termites take from the termite queen to building sites become more directed with time. These features are in keeping with observation and have not been previously modelled.

In this paper we address the automatic synthesis of controllers for the coordinated movement of multiple mobile robots. We use a noise-resistant version of Particle Swarm Optimization to learn in simulation a set of 50 weights of a plastic artificial neural network. Two learning strategies are applied: homogeneous centralized learning, in which every robot runs the same controller and the performance is evaluated externally with a global metric, and heterogeneous distributed learning, in which robots run different controllers and the performance is evaluated independently on each robot with a local metric. The two sets of metrics enforce Reynolds’ flocking rules, resulting in a good correspondence between the metrics and the flocking behaviors obtained. Results demonstrate that it is possible to learn the collective task using both learning approaches. The solutions from the centralized learning have higher fitness and lower standard deviation than those learned in a distributed manner. We test the learned controllers in real robot experiments and also show in simulation the performance of the controllers with increasing number of robots.