Robustness in the long run: Auto-teaching vs Anticipation in Evolutionary Robotics

In Evolutionary Robotics, auto-teaching networks, neural networks that modify their own weights during the life-time of the robot, have been shown to be powerful architectures to develop adaptive controllers. Unfortunately, when run for a longer period of time than that used during evolution, the long-term behavior of such networks can become unpredictable. This paper gives an example of such dangerous behavior, and proposes an alternative solution based on anticipation: as in auto-teaching networks, a secondary network is evolved, but its outputs try to predict the next state of the robot sensors. The weights of the action network are adjusted using some back-propagation procedure based on the errors made by the anticipatory network. First results -- in simulated environments -- show a tremendous increase in robustness of the long-term behavior of the controller

Novelty Search in Competitive Coevolution

One of the main motivations for the use of competitive coevolution systems is their ability to capitalise on arms races between competing species to evolve increasingly sophisticated solutions. Such arms races can, however, be hard to sustain, and it has been shown that the competing species often converge prematurely to certain classes of behaviours. In this paper, we investigate if and how novelty search, an evolutionary technique driven by behavioural novelty, can overcome convergence in coevolution. We propose three methods for applying novelty search to coevolutionary systems with two species: (i) score both populations according to behavioural novelty; (ii) score one population according to novelty, and the other according to fitness; and (iii) score both populations with a combination of novelty and fitness. We evaluate the methods in a predator-prey pursuit task. Our results show that novelty-based approaches can evolve a significantly more diverse set of solutions, when compared to traditional fitness-based coevolution.Comment: To appear in 13th International Conference on Parallel Problem Solving from Nature (PPSN 2014

¿Derecho al comercio o imposición del libre mercado?

Si bien existe un acuerdo considerable acerca de los efectos benéficos del libre comercio (siempre y cuando la libertad sea recíproca y no arreglada a favor de los poderosos), cada vez se dan más discusiones acerca de la pretensión de evitar cualquier regulación, como lo evidencia la oposición a que se introduzca el Acuerdo Multilateral sobre la Inversión y la inquietud con las Medidas de Inversión Relacionadas con el Comercio. En este contexto, los reclamos por un derecho al comercio deben ser analizados con cuidado para determinar sus parámetros. ¿Se trata solo de que las empresas transnacionales realicen sus negocios sin someterse al control del estado

Analysing co-evolution among artificial 3D creatures

This paper is concerned with the analysis of coevolutionary dynamics among 3D artificial creatures, similar to those introduced by Sims (1). Coevolution is subject to complex dynamics which are notoriously difficult to analyse. We introduce an improved analysis method based on Master Tournament matrices [2], which we argue is both less costly to compute and more informative than the original method. Based on visible features of the resulting graphs, we can identify particular trends and incidents in the dynamics of coevolution and look for their causes. Finally, considering that coevolutionary progress is not necessarily identical to global overall progress, we extend this analysis by cross-validating individuals from different evolutionary runs, which we argue is more appropriate than single-record analysis method for evaluating the global performance of individuals

Cyclic Incrementality in Competitive Coevolution: Evolvability through Pseudo-Baldwinian Switching-Genes

Coevolving systems are notoriously difficult to understand. This is largely due to the Red Queen effect that dictates heterospecific fitness interdependence. In simulation studies of coevolving systems, master tournaments are often used to obtain more informed fitness measures by testing evolved individuals against past and future opponents. However, such tournaments still contain certain ambiguities. We introduce the use of a phenotypic cluster analysis to examine the distribution of opponent categories throughout an evolutionary sequence. This analysis, adopted from widespread usage in the bioinformatics community, can be applied to master tournament data. This allows us to construct behavior-based category trees, obtaining a hierarchical classification of phenotypes that are suspected to interleave during cyclic evolution. We use the cluster data to establish the existence of switching-genes that control opponent specialization, suggesting the retention of dormant genetic adaptations, that is, genetic memory. Our overarching goal is to reiterate how computer simulations may have importance to the broader understanding of evolutionary dynamics in general. We emphasize a further shift from a component-driven to an interaction-driven perspective in understanding coevolving systems. As yet, it is unclear how the sudden development of switching-genes relates to the gradual emergence of genetic adaptability. Likely, context genes gradually provide the appropriate genetic environment wherein the switching-gene effect can be exploite

Co-evolving predator and prey robots: Do 'arm races' arise in artificial evolution?

Co-evolution (i.e. the evolution of two or more competing populations with coupled fitness) has several features that may potentially enhance the power of adaptation of artificial evolution. In particular, as discussed by Dawkins and Krebs [3], competing populations may reciprocally drive one another to increasing levels of complexity by producing an evolutionary “arms race”. In this paper we will investigate the role of co-evolution in the context of evolutionary robotics. In particular, we will try to understand in what conditions co-evolution can lead to “arms races”. Moreover, we will show that in some cases artificial co-evolution has a higher adaptive power than simple evolution. Finally, by analyzing the dynamics of co-evolved populations, we will show that in some circumstances well adapted individuals would be better advised to adopt simple but easily modifiable strategies suited for the current competitor strategies rather than incorporate complex and general strategies that may be effective against a wide range of opposing counter-strategies

Adaptive Behavior in Competitive Co-Evolutionary Robotics

Co-evolution of competitive species provides an interesting testbed to study the role of adaptive behavior because it provides unpredictable and dynamic environments. In this paper we experimentally investigate some arguments for the co-evolution of different adaptive protean behaviors in competing species of predators and preys. Both species are implemented as simulated mobile robots (Kheperas) with infrared proximity sensors, but the predator has an additional vision module whereas the prey has a maximum speed set to twice that of the predator. Different types of variability during life for neurocontrollers with the same architecture and genetic length are compared. It is shown that simple forms of proteanism affect co-evolutionary dynamics and that preys rather exploit noisy controllers to generate random trajectories, whereas predators benefit from directional-change controllers to improve pursuit behavior