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I present criteria or benchmarks of personhood that robotic agents must pass for them to match us in social intelligence and indicate some current successes and difficulties for the future with respect to these criteria. I focus on five problematic areas: 1) How can inorganic material substances mimic organic processes?; 2) How can robots evolve adaptations similar to those of human persons?; 3) How can robots develop into adult personhood?; 4) How can social identities of robots transform in an ever evolving robotic society?; 5) How can robots display intellectual and moral ideals that can advance knowledge and social goals? [ABSTRACT FROM AUTHOR]

Neuropsychological phenomena have been modelized mainly, by the mainstream approach, by attempting to reproduce their neural substrate whereas sensory-motor contingencies have attracted less attention. In this work, we introduce a simulator based on the evolutionary robotics platform Evorobot* in order to setting up in silico neuropsychological tasks. Moreover, in this study we trained artificial embodied neurorobotic agents equipped with a pan/tilt camera, provided with different neural and motor capabilities, to solve a well-known neuropsychological test: the cancellation task in which an individual is asked to cancel target stimuli surrounded by distractors. Results showed that embodied agents provided with additional motor capabilities (a zooming/attentional actuator) outperformed simple pan/tilt agents, even those equipped with more complex neural controllers and that the zooming ability is exploited to correctly categorising presented stimuli. We conclude that since the sole neural computational power cannot explain the (artificial) cognition which emerged throughout the adaptive process, such kind of modelling approach can be fruitful in neuropsychological modelling where the importance of having a body is often neglected. [ABSTRACT FROM AUTHOR]

This work addresses the evolution of an artificial neural network (ANN) to assist in the problem of indoor robotic localization. We investigate the design and building of an autonomous localization system based on information gathered from wireless networks (WN). The article focuses on the evolved ANN, which provides the position of a robot in a space, as in a Cartesian coordinate system, corroborating with the evolutionary robotic research area and showing its practical viability. The proposed system was tested in several experiments, evaluating not only the impact of different evolutionary computation parameters but also the role of the transfer functions on the evolution of the ANN. Results show that slight variations in the parameters lead to significant differences on the evolution process and, therefore, in the accuracy of the robot position. [ABSTRACT FROM PUBLISHER]

This article is concerned with a fixed-size population of autonomous agents facing unknown, possibly changing, environments. The motivation is to design an embodied evolutionary algorithm that can cope with the implicit fitness function hidden in the environment so as to provide adaptation in the long run at the level of population. The proposed algorithm, termed mEDEA, is shown to be both efficient in unknown environments and robust to abrupt and unpredicted changes in the environment. The emergence of consensus towards specific behavioural strategies is examined, with a particular focus on algorithmic stability. Finally, a real-world implementation of the algorithm is described with a population of 20 real-world e-puck robots. [ABSTRACT FROM PUBLISHER]

Ad hoc mreže specifične su po svojim karakteristikama (necentraliziranost, samoorganiziranje I višeskokovnost). Zbog toga usmjerni protokoli u ad hoc mrežama moraju biti prilagođeni pojedinim primjenama reže i zadovolja- vati glavne zahtjeve -- propusnosti, skalabilnost i sigurnost. U radu je prikazan pregled sigurnosti usmjeravanja u ad hoc mrežama. Navedena su svojstva ad hoc mreža I izazovi razvoja protokola usmjeravanja u njima. Opisani su glavni sigurnosni problemi u usmjeravanju i metode ispitivanja sigurnosti usmjernih protokola. [ABSTRACT FROM AUTHOR]

This paper continues efforts to establish a mutually informative dialogue between psychology and evolutionary robotics in order to investigate the dynamics of social interaction. We replicate a recent simulation model of a minimalist experiment in perceptual crossing and confirm the results with significantly simpler artificial agents. A series of psycho-physical tests of their behaviour informs a hypothetical circuit model of their internal operation. However, a detailed study of the actual internal dynamics reveals this circuit model to be unfounded, thereby offering a tale of caution for those hypothesising about sub-personal processes in terms of behavioural observations. In particular, it is shown that the behaviour of the agents largely emerges out of the interaction process itself rather than being an individual achievement alone. We also extend the original simulation model in two novel directions in order to test further the extent to which perceptual crossing between agents can self-organise in a robust manner. These modelling results suggest new hypotheses that can become the basis for further psychological experiments. [ABSTRACT FROM AUTHOR]

Constructing musculoskeletal models of extinct vertebrates requires subjective assumptions about soft tissue parameters rarely preserved in the fossil record. Despite these necessary assumptions about fundamental input values, paleobiologists rarely perform objective tests of best-estimate models before reaching conclusions based on predicted results. The extent to which lack of knowledge on soft tissue anatomy limits the accuracy of running speed estimates of extinct dinosaurs is therefore poorly understood. In this study, a sensitivity analysis is performed on an evolutionary robotics model of the non-avian theropod dinosaur Allosaurus, used previously to estimate maximum running speed in this extinct animal. A range of muscle parameters were varied over the range observed in extant vertebrates, whereas mass-related parameters were altered across the range of published estimates for Allosaurus. Muscle parameters have a linear relationship with maximum running speed, whereas surprisingly total body mass and torso center of mass have little effect. Muscle force values produced the greatest range in predicted running speeds (4.5–10.7 m/s) and stride lengths (4–5.8 m) in the sensitivity analysis, equating to 65.9% and 30.7% variation about the original 'best-estimate' prediction, a relatively high potential margin of error. These results highlight the importance of sensitivity analyses in biomechanical modeling of extinct taxa, particularly where values for soft tissues parameters are not tightly constrained. The current range in plausible values for soft tissue properties makes a robust quantitative assessment of behavioral ecology and species interactions in dinosaurian communities extremely difficult. [ABSTRACT FROM AUTHOR]

Subjective evaluation of a seal robot was conducted in exhibitions that were held in seven different countries: Japan, the UK, Sweden, Italy, South Korea, Brunei and the US. Surveys were administered to the visitors after interaction with the robot. To explore the differences among people's evaluations of the robot in the seven countries, statistical analysis was conducted in the data set obtained from over 1400 respondents. The subjective evaluation provided high scores on the whole, and the factors 'comfortable feeling like interacting with real animals' and 'favorable impression to encourage interaction' were extracted. The results showed that people in the UK, Sweden and Italy evaluated the robot highly in the first factor, and people in Japan and South Korea evaluated it highly in the second factor. We considered that the subjective evaluation of the seal robot was influenced by cultural differences of relationships with animals. [ABSTRACT FROM AUTHOR]

A study of place cognition and 'place units' in robots produced via artificial evolution is described. Previous studies have investigated the possible role of place cells as building blocks for 'cognitive maps' representing place, distance and direction. Studies also show, however, that when animals are restrained, the spatial selectivity of place cells is partially or completely lost. This suggests that the role of place cells in spatial cognition depends not only on the place cells themselves, but also on representations of the animal's physical interactions with its environment. This hypothesis is tested in a population of evolved robots. The results suggest that successful place cognition requires not only the ability to process spatial information, but also the ability to select the environmental stimuli to which the agent is exposed. If this is so, theories of active perception can make a useful contribution to explaining the role of place cells in spatial cognition. [ABSTRACT FROM AUTHOR]

In biology/psychology, the capability of natural organisms to learn from the observation/interaction with conspecifics is referred to as social learning. Roboticists have recently developed an interest in social learning, since it might represent an effective strategy to enhance the adaptivity of a team of autonomous robots. In this study, we show that a methodological approach based on artifcial neural networks shaped by evolutionary computation techniques can be successfully employed to synthesise the individual and social learning mechanisms for robots required to learn a desired action (i.e. phototaxis or antiphototaxis). [ABSTRACT FROM AUTHOR]

In this paper, we present the results of an experiment in which a collection of simulated robots that have been evolved for the ability to solve a collective navigation problem develop a communication system that allows them to co-operate better. The analysis of the results obtained indicates how evolving robots develop a non-trivial communication system and exploit different communication modalities. The results also indicate how the possibility of co-adapting the robots' individual and social/communicative behaviour plays a key role in the development of progressively more complex and effective individuals. [ABSTRACT FROM AUTHOR]

A major facet of multi-legged robot control is locomotion. Each leg must move in such a manner that it efficiently produces thrust and provides maximum support. The motion of all the legs must be coordinated so that they are working together to provide constant stability while propelling the robot forward. In this paper, we discuss the use of a cyclic genetic algorithm (CGA) to evolve control programs that produce gaits for actual hexapod robots. Tests done in simulation and verified on the actual robot show that the CGA successfully produces gaits for both fully capable and disabled robots. [ABSTRACT FROM AUTHOR]

The artificial life approach to evolutionary robotics is used as a fundamental framework for the development of a modular neural control of autonomous mobile robots. The applied evolutionary technique is especially designed to grow different neural structures with complex dynamical properties. This is due to a modular neurodynamics approach to cognitive systems, stating that cognitive processes are the result of interacting dynamical neuro-modules. The evolutionary algorithm is described, and a few examples for the versatility of the procedures are given. Besides solutions for standard tasks like exploration, obstacle avoidance and tropism, also the sequential evolution of morphology and control of a biped is demonstrated. A further example describes the co-evolution of different neuro-controllers co-operating to keep a gravitationally driven art-robot in constant rotation. [ABSTRACT FROM AUTHOR]

In this paper, we aim to design decision-making mechanisms for a simulated Khepera robot equipped with simple sensors, which integrates over time its perceptual experience in order to initiate a simple signalling response. Contrary to other previous similar studies, in this work the decision-making is uniquely controlled by the time-dependent structures of the agent controller, which in turn are tightly linked to the mechanisms for sensorimotor coordination. The results of this work show that a single dynamic neural network, shaped by evolution, makes an autonomous agent capable of 'feeling' time through the flow of sensations determined by its actions. Further analysis of the evolved solutions reveals the nature of the selective pressures that facilitate the evolution of fully discriminating and signalling agents. Moreover, we show that, by simply working on the nature of the fitness function, it is possible w bring forth discrimination mechanisms that generalize to conditions never encountered during evolution. [ABSTRACT FROM AUTHOR]

Many researchers in embodied cognitive science and artificial intelligence, and evolutionary robotics in particular, emphasize the interaction or brain, body and environment as crucial to the emergence of intelligent. adaptive behaviour. Accordingly. the interaction between agent and environment. as well as the co-adaptation of artificial brains and bodies, has been the focus of much research in evolutionary robotics. Hence, there are plenty of studies of robotic agents/species adapting to a given environment. Many animals, on the other hand, in particular humans, to some extent can choose to adapt the environment to their own needs instead of adapting (onIy) themselves. That alternative has been studied relatively little in robot experiments. This paper, therefore, presents some simple initial simulation experiments, in a delayed response task setting, that illustrate how the evolution of environment adaptation can sent to provide cognitive scaffolding that reduces the requirements for individual agents. Furthermore, theoretical implications, open questions and future research directions for evolutionary robotics are discussed. [ABSTRACT FROM AUTHOR]

Robotics can contribute significantly to our understanding of the behaviour of organisms because of the emphasis on the role of the body and its physical interactions with the external environment in determining the organism's behaviour. However, behaviour is the result of the interactions of an organism's nervous system with both the external environment and the internal environment, i.e. with what lies within the organism's body. While robotics has concentrated so far on the first type of interactions (external robotics), to understand the behaviour of organisms more adequately we also need to reproduce in robots the inside of the body of organisms and to study the interactions of the robot's control system with what is inside the body (internal robotics). In this paper, seven differences between the two types of interactions are discussed and some examples of internal robotics are briefly described: robots that evolve a biological clock that allows them to look for food during the day and to rest at night: robots that evolve a pain signal associated with some damage in their body that allows the robot to stop moving when ill in order to recover more quickly from the bodily damage; and robots that can be hungry and/or thirsty and respond adaptively or maladaptively to conflicts between these two motivations, [ABSTRACT FROM AUTHOR]

To study the interfacial complexity between an agent and its environment such as the adaptive selection aspects of sensory inputs, we propose a new coupling mechanism, called autonomous coupling, where an agent can spontaneously switch on or off its interaction with the environment. An oscillatory neural system with autonomous coupling sums the sensory inputs and initiates action selection via a sensorimotor coupling. An example task we designed to show dynamical categorization is the classification of light frequencies. An evolved agent selects specified light frequencies by approaching them and avoiding light of other frequencies. Dynamical categorization and active coupling are the key concepts for the understanding of situated and embodied cognitive functions. [ABSTRACT FROM AUTHOR]

Kamil and Jones have shown that Clark's nutcrackers (a species of crow) can exploit abstract geometrical relationships between landmarks to reach a target location. This has been interpreted as evidence tor the existence of 'cognitive maps'. In the research reported here, we replicate the behaviour observed by Kamil and Jones, using methods from evolutionary robotics. A genetic algorithm is used to train a software model of the Khepera miniature mobile robot, controlled by an artificial neural network. The architecture of the network precludes the existence of cognitive maps. The robots display a range of different behaviours. We demonstrate that these depend on a process of 'active perception and analyst' the underlying computational mechanisms. On the basis of our results, we suggest that animals in the wild may indeed use active perception as a cool to exploit geometrical relationships between landmarks and that such a mechanism might complement other mechanisms, including cognitive maps. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the limitations of current evolutionary robotics models and we propose a new framework that might solve some of these problems and lead to art open-ended evolutionary process in hardware. More specifically, the paper describes a novel approach where the usual concepts of population, generations and fitness tire made implicit in the system. Individuals co-evolve embedded in their environment. Exploiting the sell-assembling capabilities of the (simulated) robots, the genotype of a successful individual can spread in the population. In this way, interesting behaviours emerge spontaneously, resulting in chasing and evading other individuals, collective obstacle avoidance and co-ordinated motion of self-assembled structures. [ABSTRACT FROM AUTHOR]

In autonomous agent systems, internal memory can be an important element to overcome the limitations of purely reactive agent behaviour. This paper presents an analysis of memory requirements for T-maze tasks well known as the road sign problem. In these tasks, a robot agent should make a decision about turning left or right at the T-junction in the approach corridor, depending on a history of perceptions. The robot agent in simulation can sense the light intensity influenced by light lamps placed on the bank of the wall. We apply the evolutionary multiobjective optimization approach to finite state controllers with two objectives: behaviour performance and memory size. Then the internal memory is quantified by counting internal states needed for the T- maze tasks in noisy environments. In particular, we focused on the influence of noise on internal memory and behaviour performance, and it is shown that state machines with variable thresholds can improve the performance with a hysteresis effect to filter out noise. This paper also provides an analysis of noise effect on perceptions and its relevance on performance degradation in state machines. [ABSTRACT FROM AUTHOR]

Discusses prospects for a robotic future through the replacement of human beings by intelligent machines. Moral basis for human extinction; Concept of evolutionary progress; Comment on the extinctionist vision of the future.

Operant shaping is a technique used by psychologists to train new behaviours in animals and is also of interest to roboticists and artefactual modellers. This review argues that an understanding of the nature of shaping in animals will facilitate the use of this training method by roboticists, and other designers of artificial adaptive systems, who wish to adopt ideas and concepts from animal psychology and ethology. To do this, the paper reviews a number ofanimal models of operant shaping. It identifies issues that are relevant to the successful application of this technique in both animals and autonomous agents. These issues include the question of how to identify and assess the behaviours subject to shaping, the nature of the learning contingencies which are active during operant shaping, and the issue of whether shaping generates new and original functionalities or, alternatively, simply acts to reorganize the existing behavioural repertoire of the animal or robot. A number of robotic applications of shaping techniques are considered. The similarities between ontogenetic shaping and the phylogenetic shaping techniques employed in evolutionary learning are discussed. The paper concludes by arguing that an understanding of ontogenetic shaping, achieved through the study of animals, will facilitate the design of robots that must function in real world situations. [ABSTRACT FROM AUTHOR]

This article describes how the SGOCE paradigm has been used within the context of a 'minimal simulation' strategy to evolve neural networks controlling locomotion and obstacle avoidance in a six-legged robot. A standard genetic algorithm has been used to evolve developmental programs according to which recurrent networks of leaky-integrator neurons were grown in a user-provided developmental substrate and were connected to the robot's sensors and actuators. Specific grammars have been used to limit the complexity of the developmental programs and of the corresponding neural controllers. Such controllers were first evolved through simulation and then successfully downloaded on the real robot. [ABSTRACT FROM AUTHOR]

The new wave of robotics aims to provide robots with the capacity to learn, develop and evolve in interaction with their environments using biologically inspired techniques. This work is placed in perspective by considering its biological and psychological basis with reference to some of the grand theorists of living systems. In particular, we examine what it means to have a body by outlining theories of the mechanisms of bodily integration in multicellular organisms and their means of solidarity with the environment. We consider the implications of not having a living body for current ideas on robot learning, evolution, and cognition and issue words of caution about wishful attributions that can smuggle more into observations of robot behaviour than is scientifically supportable. To round off the arguments we take an obligatory swipe at ungrounded artificial intelligence but quickly move on to assess physical grounding and embodiment in terms of the rooted cognition of the living. [ABSTRACT FROM AUTHOR]

This paper introduces a new type of artificial neural network (GasNets) and shows that it is possible to use evolutionary computing techniques to find robot controllers based on them. The controllers are built from networks inspired by the modulatory eff ects of freely diff using gases, especially nitric oxide, in real neuronal networks. Evolutionary robotics techniques were used to develop control networks and visual morphologies to enable a robot to achieve a target discrimination task under very noisy lighting conditions. A series of evolutionary runs with and without the gas modulation active demonstrated that networks incorporating modulation by diff using gases evolved to produce successful controllers considerably faster than networks without this mechanism. GasNets also consistently achieved evolutionary success in far fewer evaluations than were needed when using more conventional connectionist style networks. [ABSTRACT FROM AUTHOR]

A perspective relating adaptivity and emergence is outlined. Organisms and robotic devices utilizing sensing, computing, and effecting elements with various plasticities are considered. Qualitatively distinct types of adaptivity (adaptive computations, adaptive measurements, adaptive controls) are presented. Capacities and limitations of these various types are assessed. An artificial electrochemical device which adaptively evolved the capacity to discriminate sounds is presented as an example of a semantically adaptive device. Emergence is related to structural adaptation. Three types of emergence, nonemergence, syntactic emergence, and semantic emergence, correspond to nonadaptive, syntactically adaptive, and semantically adaptive devices. Networks of adaptive elements have functional properties similar to their elements. Semantic adaptation here permits the evolution of new, independent interelement signalling channels. In neurons semantic adaptation entails the creation of new independent, orthogonal signal types. Neural mechanisms by which such signal types could be generated in the temporal microstructure of pulse trains are briefly considered. [ABSTRACT FROM PUBLISHER]

This paper presents an overview of the articles presented in the Volume 16 issue of "Connection Science." Bianco and Nolfi discussed how current evolutionary robotics methods can be extended to lead to a truly open-ended evolutionary process leading to a large variety of qualitatively different solutions and to the development of novelties. Hulse et al. present a new approach based on the evolution of neuro-modules that are combined in order to form the control system of the corresponding robots. Miglino and Walker used the evolutionary robotic approach in studying how natural organisms are able to exploit abstract geometrical relationships between landmarks to reach a target location. Iizuka and Ikegami provide a new interesting perspective on perception and categorization. Parisi's contribution stresses the potential advantages of modeling behavior as a property that emerges not only from the interaction of the organism with the external environment but also with the organism's internal physical structure.