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Stefano Nolfi
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Journal Articles
Publisher: Journals Gateway
Artificial Life (2024) 30 (3): 323–336.
Published: 01 August 2024
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Several simulation models have demonstrated how flocking behavior emerges from the interaction among individuals that react to the relative orientation of their neighbors based on simple rules. However, the precise nature of these rules and the relationship between the characteristics of the rules and the efficacy of the resulting collective behavior are unknown. In this article, we analyze the effect of the strength with which individuals react to the orientation of neighbors located in different sectors of their visual fields and the benefit that could be obtained by using control rules that are more elaborate than those normally used. Our results demonstrate that considering only neighbors located on the frontal side of the visual field permits an increase in the aggregation level of the swarm. Using more complex rules and/or additional sensory information does not lead to better performance.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2020) 26 (4): 409–430.
Published: 01 February 2021
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The possibility of using competitive evolutionary algorithms to generate long-term progress is normally prevented by the convergence on limit cycle dynamics in which the evolving agents keep progressing against their current competitors by periodically rediscovering solutions adopted previously. This leads to local but not to global progress (i.e., progress against all possible competitors). We propose a new competitive algorithm that produces long-term global progress by identifying and filtering out opportunistic variations, that is, variations leading to progress against current competitors and retrogression against other competitors. The efficacy of the method is validated on the coevolution of predator and prey robots, a classic problem that has been used in related researches. The accumulation of global progress over many generations leads to effective solutions that involve the production of articulated behaviors. The complexity of the behavior displayed by the evolving robots increases across generations, although progress in performance is not always accompanied by behavior complexification.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2018) 24 (4): 277–295.
Published: 01 March 2019
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Previous evolutionary studies demonstrated how robust solutions can be obtained by evaluating agents multiple times in variable environmental conditions. Here we demonstrate how agents evolved in environments that vary across generations outperform agents evolved in environments that remain fixed. Moreover, we demonstrate that best performance is obtained when the environment varies at a moderate rate across generations, that is, when the environment does not vary every generation but every N generations. The advantage of exposing evolving agents to environments that vary across generations at a moderate rate is due, at least in part, to the fact that this condition maximizes the retention of changes that alter the behavior of the agents, which in turn facilitates the discovery of better solutions. Finally, we demonstrate that moderate environmental variations are advantageous also from an evolutionary computation perspective, that is, from the perspective of maximizing the performance that can be achieved within a limited computational budget.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2016) 22 (3): 319–352.
Published: 01 August 2016
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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 exploited.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2015) 21 (4): 395–397.
Published: 01 November 2015
Journal Articles
Publisher: Journals Gateway
Artificial Life (2011) 17 (3): 183–202.
Published: 01 July 2011
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Evolutionary robotics (ER) is a powerful approach for the automatic synthesis of robot controllers, as it requires little a priori knowledge about the problem to be solved in order to obtain good solutions. This is particularly true for collective and swarm robotics, in which the desired behavior of the group is an indirect result of the control and communication rules followed by each individual. However, the experimenter must make several arbitrary choices in setting up the evolutionary process, in order to define the correct selective pressures that can lead to the desired results. In some cases, only a deep understanding of the obtained results can point to the critical aspects that constrain the system, which can be later modified in order to re-engineer the evolutionary process towards better solutions. In this article, we discuss the problem of engineering the evolutionary machinery that can lead to the desired result in the swarm robotics context. We also present a case study about self-organizing synchronization in a swarm of robots, in which some arbitrarily chosen properties of the communication system hinder the scalability of the behavior to large groups. We show that by modifying the communication system, artificial evolution can synthesize behaviors that scale properly with the group size.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2006) 12 (3): 289–311.
Published: 01 July 2006
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Distributed coordination of groups of individuals accomplishing a common task without leaders, with little communication, and on the basis of self-organizing principles, is an important research issue within the study of collective behavior of animals, humans, and robots. The article shows how distributed coordination allows a group of evolved, physically linked simulated robots (inspired by a robot under construction) to display a variety of highly coordinated basic behaviors such as collective motion, collective obstacle avoidance, and collective approach to light, and to integrate them in a coherent fashion. In this way the group is capable of searching and approaching a lighted target in an environment scattered with obstacles, furrows, and holes, where robots acting individually fail. The article shows how the emerged coordination of the group relies upon robust self-organizing principles (e.g., positive feedback) based on a novel sensor that allows the single robots to perceive the group's “average” motion direction. The article also presents a robust solution to a difficult coordination problem, which might also be encountered by some organisms, caused by the fact that the robots have to be capable of moving in any direction while being physically connected. Finally, the article shows how the evolved distributed coordination mechanisms scale very well with respect to the number of robots, the way in which robots are assembled, the structure of the environment, and several other aspects.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2003) 9 (3): 255–267.
Published: 01 July 2003
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We present a set of experiments in which simulated robots are evolved for the ability to aggregate and move together toward a light target. By developing and using quantitative indexes that capture the structural properties of the emerged formations, we show that evolved individuals display interesting behavioral patterns in which groups of robots act as a single unit. Moreover, evolved groups of robots with identical controllers display primitive forms of situated specialization and play different behavioral functions within the group according to the circumstances. Overall, the results presented in the article demonstrate that evolutionary techniques, by exploiting the self-organizing behavioral properties that emerge from the interactions between the robots and between the robots and the environment, are a powerful method for synthesizing collective behavior.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2000) 6 (1): 69–84.
Published: 01 January 2000
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The evolution of simulated robots with three different architectures is studied in this article. We compare a nonmodular feed-forward network, a hardwired modular, and a duplication-based modular motor control network. We conclude that both modular architectures outperform the non-modular architecture, both in terms of rate of adaptation as well as the level of adaptation achieved. The main difference between the hardwired and duplication-based modular architectures is that in the latter the modules reached a much higher degree of functional specialization of their motor control units with regard to high-level behavioral functions. The hardwired architectures reach the same level of performance, but have a more distributed assignment of functional tasks to the motor control units. We conclude that the mechanism through which functional specialization is achieved is similar to the mechanism proposed for the evolution of duplicated genes. It is found that the duplication of multifunctional modules first leads to a change in the regulation of the module, leading to a differentiation of the functional context in which the module is used. Then the module adapts to the new functional context. After this second step the system is locked into a functionally specialized state. We suggest that functional specialization may be an evolutionary absorption state.
Journal Articles
Publisher: Journals Gateway
Artificial Life (1998) 4 (4): 311–335.
Published: 01 October 1998
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Coevolution (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 article we will investigate the role of coevolution in the context of evolutionary robotics. In particular, we will try to understand in what conditions coevolution can lead to “arms races.” Moreover, we will show that in some cases artificial coevolution has a higher adaptive power than simple evolution. Finally, by analyzing the dynamics of coevolved 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.
Journal Articles
Publisher: Journals Gateway
Artificial Life (1995) 2 (4): 417–434.
Published: 01 July 1995
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The problem of the validity of simulation is particularly relevant for methodologies that use machine learning techniques to develop control systems for autonomous robots, as, for instance, the artificial life approach known as evolutionary robotics. In fact, although it has been demonstrated that training or evolving robots in real environments is possible, the number of trials needed to test the system discourages the use of physical robots during the training period. By evolving neural controllers for a Khepera robot in computer simulations and then transferring the agents obtained to the real environment we show that (a) an accurate model of a particular robot-environment dynamics can be built by sampling the real world through the sensors and the actuators of the robot; (b) the performance gap between the obtained behaviors in simulated and real environments may be significantly reduced by introducing a “conservative” form of noise; (c) if a decrease in performance is observed when the system is transferred to a real environment, successful and robust results can be obtained by continuing the evolutionary process in the real environment for a few generations.