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2010, Artificial Life and Evolutionary Computation - Proceedings of Wivace 2008
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9 pages
1 file
Proceedings of the 10th European Conference on Advances in Artificial Life Darwin Meets Von Neumann Volume Part I, 2009
Sommario In this paper we describe a model of cooperation in Evolutionary Robotics (ER) derived by animal research on Corvids. In recent years many researchers have proposed models of ER which are bioinspired. The main source of inspiration has come from social insects, such as ants. Inspiration may come also from other representatives in the animal kingdom, that are quite different from insects such as primates or corvids, thus producing different models that can address different issues. The work presented here starts from works inspired by social insects and then describes an ER model which is built on corvids behavior, that addresses the evolution of cooperation, showing how different bio-inspired models can be useful to study different issues.
2012
Evolutionary robotics can be a powerful tool in studies on the evolutionary origins of self-organising behaviours in biological systems. However, these studies are viable only when the behaviour of the evolved artificial system closely corresponds to the one observed in biology, as described by available models. In this paper, we compare the behaviour evolved in a robotic system with the collegial decision making displayed by cockroaches in selecting a resting shelter.
Interdisciplinary Description of Complex Systems, 2011
This paper presents an agent based model of the evolution of cooperation in a complex environment. Anthropoid agents reproduce sexually, and live in a world where food is irregularly distributed in space and seasonally produced. They can share food, form hunting and migrating groups, and are able to build alliances to dispute territory. The agents memorize their interactions with others and their actions are mainly guided by emotions, modelled as propensities to react in specific ways to other agents’ actions and environmental conditions. The results revealed that sexual reproduction is extremely relevant: in the proposed model cooperation was stronger between agents of opposite sex.
Lecture Notes in Computer Science, 2000
We study the evolution of social behaviors within a behavioral framework. To this end, we de ne a \minimal social situation" that is experimented with both humans and simulations based on reinforcement learning algorithms. We analyse the dynamics of behaviors in this situation by way of operant conditioning. We show that the best reinforcement algorithm, based on Staddon-Zhang's equations, has a performance and a variety of behaviors that comes close to that of humans, and clearly outperforms the well-known Q-learning. Though we use here a rather simple, yet rich, situation, we argue that operant conditioning deserves much study in the realm of arti cial life, being too often misunderstood, and confused with classical conditioning.
Cooperative behaviour can be found in many insect societies. A form of this cooperation known as Eusociality is only found in 2% of known insect species, however, these species compose most of the world’s insect biomass. This study investigated the origins of cooperation in eusocial species by analysing the e↵ects of individual relatedness and patch richness on the levels of cooperation observed. These results allowed commentary on the validity of the game theoretic model by Reeve and Ho ̈lldobler and provided insight into the origins of cooperation. A custom Agent Based System (ABS) simulation was developed, with results providing in- sight into multiple aspects leading to the evolution of coop- eration. By comparing the results to Reeve and Ho ̈lldobler’s game theoretic model, the study was able to agree with two of the organizational trends (hypotheses) they mentioned, indicating that higher levels of agent relatedness and higher levels of patch richness (resource density) both lead to higher levels of within-group cooperation. The study was therefore able to comment on the origins of cooperation in biological societies and proved that relat- edness has a strong e↵ect on the cooperation experienced. Finally it would be possible to extend this method of study to address the other hypotheses mentioned by Reeve and Ho ̈lldobler.
1993
This paper can be summarized by the following three statements: (1) in the modeling of life and cognition, principles are more important than performance; (2) there is a structural similarity between the foundational problems that arise in ALife and AI/cognitive science (therefore, the problems can be treated simultaneously); (3) a non-computational yet rationally definable mechanism can help exploit the difference between what I will call organizational or "principleoriented" and purely behavioral or "performance-oriented" approaches in both fields.
Biological Cybernetics
Living organisms are far superior to state-of-the-art robots as they have evolved a wide number of capabilities that far encompass our most advanced technologies. The merging of biological and artificial world, both physically and cognitively, represents a new trend in robotics that provides promising prospects to revolutionize the paradigms of conventional bio-inspired design as well as biological research. In this review, a comprehensive definition of animal-robot interactive technologies is given. They can be at animal level, by augmenting physical or mental capabilities through an integrated technology, or at group level, in which real animals interact with robotic conspecifics. Furthermore, an overview of the current state of the art and the recent trends in this novel context is provided. Bio-hybrid organisms represent a promising research area allowing us to understand how a biological apparatus (e.g. muscular and/or neural) works, thanks to the interaction with the integrated technologies. Furthermore, by using artificial agents, it is possible to shed light on social behaviours characterizing mixed societies. The robots can be used to manipulate groups of living organisms to understand self-organization and the evolution of cooperative behaviour and communication. Keyword Animal-robot interaction • Ethorobotics • Bio-hybrid organism • Mixed society 1 Introduction Animal-robot interactive technologies represent a relatively novel research field of bio-robotics and are opening up to new opportunities for multidisciplinary studies, including biological investigations, as well as bio-inspired engineering design. This new field introduces the possibility to have, beyond the traditional bioinspiration, the merging of natural and artificial worlds in synergistic systems (Webb 2000;
This paper considers the role of AI in understanding biological behaviour. It is argued that the potential role is in testing hypothesised mechanisms by constructing and testing models, but that the iLctUal contribution is dependent upon the verifi-ability of the models as adequate representations of the biological problem under study. That is, to carryover conclusions drawn from AI to biological problems, those conclusions must not be based on unfounded assumptions about the problem. One general assumption that seems to char-acterise current research is that the physical interaction of the agent with its environment can be greatly simplified in the representation without 1 hereby affecting the viability of the mechanisms of sensory-motor control being investigated. We contrast this assumption with the problems that arise in building a real robot that models possible mechanisms of cricket phonotaxis: a proper understanding of the physical agent-environment interaction is essential to build a robot that usefully lests hypotheses about the mechanisms that un-derly this task-and is equally essential in making sense of the current results in biological research for this behaviour.
Science Robotics
Self-organized collective behavior has been analyzed in diverse types of gregarious animals. Such collective intelligence emerges from the synergy between individuals, which behave at their own time and spatial scales and without global rules. Recently, robots have been developed to collaborate with animal groups in the pursuit of better understanding their decision-making processes. These biohybrid systems make cooperative relationships between artificial systems and animals possible, which can yield new capabilities in the resulting mixed group. However, robots are currently tailor-made to successfully engage with one animal species at a time. This limits the possibilities of introducing distinct species-dependent perceptual capabilities and types of behaviors in the same system. Here, we show that robots socially integrated into animal groups of honeybees and zebrafish, each one located in a different city, allowing these two species to interact. This interspecific information tr...
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