Catálogo de publicaciones - libros

It is well known that the evolution of cooperative behaviour is dependant upon certain environmental conditions. One such condition that has been extensively studied is the use of a spatially structured population, whereby cooperation is favoured by a reduced number of interactions between cooperators and selfish cheaters. However, models that address the role of spatial structure typically use an individual-based approach, which can make analysis unnecessarily complicated. By contrast, non-spatial population genetics models usually consist entirely of a set of replicator equations, thereby simplifying analysis. Unfortunately, these models cannot traditionally be used to take account of spatial structure, since they assume that interaction between any pair of individuals in a population is equally likely. In this paper, we construct as model that is still based on replicator equations, but where spatial localisation with respect to the number of interactions between individuals is incorporated. Using this model, we are able to successfully reproduce the dynamics seen in more complex individual-based models.

Multi-agent organizations in dynamic environments, need to have the ability to adapt to environmental changes to ensure a continuation of proper functioning. Such adaptations can be made through a centralized decision process or come from the individuals within the organization. In the domain of social insects, such as honeybees and wasps, organizations are known to adapt in a decentralized fashion to environmental changes. An organizational model for decentralized organizational change is presented that can aid in analyzing and designing such organizations. The model is specified by dynamic properties at different aggregation levels. At the lowest level such properties characterize the behavior of individual roles, which can be related to higher level properties that express important elements such as survival of an organization. A honeybee colony is used as a case study.

In this paper we propose an adaptive algorithm based on a combination of selective reproduction, individual learning, and social learning. Social learning consists of a simple facilitation process that regulates the strength of individual learning on the basis of the number of individuals located nearby. By testing this model in an experimental scenario, in which a population of 10 mobile robots has to develop a simple foraging behavior, we demonstrate how the model proposed produces effective results. By comparing the results obtained in different experimental conditions we also show how the method proposed outperforms other alternative algorithms based on genetic evolution or individual learning. Finally, we briefly discuss how the model proposed can help us to understand the role of social learning in biological organisms.

This paper introduces a novel stochastic and population-based binary optimization method inspired by social psychology. It is called Social Impact Theory based Optimization (SITO). The method has been developed with the use of some simple modifications of simulations of Latané’s Dynamic Social Impact Theory. The usability of the algorithm is demonstrated via experimental testing on some test problems. The results showed that the initial version of SITO performs comparably to the simple Genetic Algorithm (GA) and the binary Particle Swarm Optimization (bPSO).

To look for an answer to the puzzle of why complexity may increase, this paper looks to the major evolutionary transitions – a recurring pattern where individuals give up their rights to reproduce individually and instead reproduce as part of a super-organism. A simple model of collective reproduction is presented and discussed in light of this topic. The model finds that collective reproduction is actually to the benefit of the individual, not to the group. The cost of reproduction is shown to be an important factor and different scenarios are presented which show individual, sexual reproduction and collective reproduction (with larger numbers of parents) as optimal.

The paper investigates the role of an affective system as part of an ethologically-inspired action-selection mechanism for virtual animals in a 3D interactive graphics environment. It discusses the integration of emotion with flocking and grazing behaviours and a mechanism for communicating emotion between animals; develops a metric for analyzing the collective behaviour of the animals and its complexity and shows that emotion reduces the complexity of behaviour and thus mediates between individual and collective behaviour.

This paper describes and compares mono- and multi-objective Ant Colony System approaches designed to solve the problem of finding the path that minimizes resources while maximizing safety for a military unit in realistic battlefields. Several versions of the previously presented CHAC algorithm, with two different state transition rules are tested. Two of them are cases, which only consider one of the objectives; these are taken as baseline. These algorithms, along with the Multi-Objective Ant Colony Optimization algorithm, have been tested in maps with different difficulty. hCHAC, an approach proposed by the authors, has yielded the best results.

We present new methods of decentralized control and interactive design for artificial swarms of a large number of agents that can spontaneously organize and maintain non-trivial heterogeneous formations. Our model assumes no elaborate sensing, computation, or communication capabilities for each agent; the self-organization is achieved solely by simple kinetic interactions among agents. Specifications of the final formations are indirectly and implicitly woven into a list of different kinetic parameter settings and their proportions, which would be hard to obtain with a conventional top-down design method but may be designed heuristically through interactive design processes.

We propose a model for simulating group-foraging behaviour with the use of a Particle Swarm system. Traditionally, the main field of application for Particle Swarm systems has been the optimisation of non-linear functions: with our research, we intend to position Particle Swarm systems in the field of Artificial Life. The EcoPS model we present shows some interesting behaviours. In particular, it seems that grouping is the key to finding the right balance between exploiting resources and surviving.

In the multi-foraging task studied in this paper, a group of robots has to efficiently retrieve two different types of prey to a nest. Robots have to decide when they leave the nest to forage and which prey to retrieve.

The goal of this study is to identify an efficient multi-foraging behaviour, where efficiency is defined as a function of the that is spent by the robots during exploration and gained when a prey is retrieved to the nest. We design and validate a mathematical model that is used to predict the optimal behaviour. We introduce a decision algorithm and use simulations to study its performance in a wide range of experimental situations with respect to the predictions of the mathematical model.