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This paper propose a linearizing scheme based on wavelet networks to reduce nonlinear distortion introduced by a high power amplifier over 256QAM signals. Parameters of the proposed linearizer are estimated by using a hybrid algorithm, namely least square and gradient descent. Computer simulation results confirm that once the 256QAM signals are amplified at an input back off level of 0 dB, there is a reduction of 29 dB spectrum re-growth. In addition proposed linearizing scheme has a low complexity and fast convergence. [ABSTRACT FROM AUTHOR]

This paper presents computer simulations which investigate the effect that different group sizes have on the emergence of compositional structures in languages. The simulations are based on a model that integrates the language game model with the iterated learning model. The simulations show that compositional structures tend to emerge more extensively for larger groups, which has a positive effect on the time in which the languages develop and on communicative success, which may even have an optimal group size. A mathematical analysis of the time of convergence is presented that provides an approximate explanation of the results. The paper concludes that increasing group sizes among humans could not only have triggered the origins of language, but also facilitated the evolution of more complex languages. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology to estimate the information content of protein structures by using: an alphabet of local microenvironments obtained from a set of protein domains with equivalent function, a modification of the physical complexity concept [1], the measures of mutual information (I(seq;str)) and conditional entropy (H(strseq)) between sequence and structure. The kinase domain catalytic subunit was used as a specific example. Our results are in accord with the hypothesis that proteins are information gathering and using systems [3], and suggest that protein structure depends less on protein sequence than biologists have historically supposed. [ABSTRACT FROM AUTHOR]

Symbiosis, the collaboration of multiple organisms from different species, is common in nature. A related phenomenon, symbiogenesis, the creation of new species through the genetic integration of symbionts, is a powerful alternative to crossover as a variation operator in evolutionary algorithms. It has inspired several previous models that use the repeated composition of pre-adapted entities. In this paper we introduce a new algorithm utilizing this concept of symbiosis which is simpler and has a more natural interpretation when compared with previous algorithms. In addition it achieves success on a broader class of modular problems than some prior methods. [ABSTRACT FROM AUTHOR]

Bio-inspiration borrows three properties characteristic of living organisms: multicellular architecture, cellular division, and cellular differentiation. Implemented in silicon according to these properties, our self-organizing systems are able to grow, to self-replicate, and to self-repair. The growth and branching processes, performed by the so-called Tom Thumb algorithm, lead thus to the configuration and cloning mechanisms of the systems. The repair processes allow its cicatrization and regeneration mechanisms. The cellular implementation and hardware simulation of these mechanisms constitute the core of this paper. [ABSTRACT FROM AUTHOR]

In this paper we propose an approach to agent modeling that follows a signal based metaphor where agents are modeled as dissipative structures and their cognitive structures are modeled as compositions of multiple energetic potentials. This uniform representational support is used to model both reactive and deliberative processes. To illustrate the descriptive adequacy of the model, two experimental cases are presented where reactive and deliberative processes are modeled based on the proposed approach. [ABSTRACT FROM AUTHOR]

The traditional GA theory is pillared on the Building Block Hypothesis (BBH) which states that Genetic Algorithms (GAs) work by discovering, emphasizing and recombining low order schemata in high-quality strings, in a strongly parallel manner. Historically, attempts to capture the topological fitness landscape features which exemplify this intuitively straight-forward process, have been mostly unsuccessful. Population-based recombinative methods had been repeatedly outperformed on the special designed abstract test suites, by different variants of mutation-based algorithms. Departing from the BBH, in this paper we seek to exemplify the utility of crossover from a different point of view, emphasizing the creative potential of the crossover operator. We design a special class of abstract test suites, called Trident functions, which exploits the ability of modern GAs to mix good but significantly different solutions. This approach has been so far neglected as it is widely believed that disruption caused by mating individuals that are too dissimilar may be harmful. We anticipate that hybridizing different designs induces a complex neighborhood structure unattainable by trajectory-based methods which can conceal novel solutions. Empirical results confirm that the proposed class of problems can be solved efficiently only by population-based panmictic recombinative methods, employing diversity maintaining mechanisms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, we consider asynchronous update scheme for a variant of graph rewriting systems called graph-rewriting automata, and show that synchronous update can be simulated by asynchronous update using a constructed rule set from the one for synchronous update. It is well known that such rule construction is possible on cellular automata or other automata networks whose structures are fixed, but graph rewriting automata induce structural changes and additional mechanism of communication and local synchronization is required. Some simple examples are given by simulation. [ABSTRACT FROM AUTHOR]

The human body has a complex skeleton, giving a very high number of degrees of freedom, and is actuated by a large number of elastic elements - muscles and tendons. As a consequence, it has extremely challenging dynamics. Conventional humanoid robots use reduced degrees of freedom and traditional stiff actuators, and so fail to capture or exploit the important dynamic aspects of the human body. It has proved possible to build robots that mimic the human body - anthropomimetic or ‘musculo-skeletal' robots - but the control of such robots will require very different methods from those used in existing humanoid robots. This paper reports the results of a preliminary investigation of the control problems using SIMNOS, a physics-based model of the anthropomimetic robot CRONOS. The transient and steady state effects of load changes on two simple feedforward methods for maintaining arm posture are assessed. The addition of a feedback controller reduces the steady state effects considerably, but still shows oscillatory transient effects. However, by combining this feedback controller with a velocity-limiting feedforward element, it proves possible to make smooth and reasonably accurate changes of posture under conditions of constant load. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In a social scenario, establishing whether a collaboration is required to achieve a certain goal is a complex problem that requires decision making capabilities and coordination among the members of the group. Depending on the environmental contingencies, solitary actions may result more efficient than collective ones and vice versa. In robotics, it may be difficult to estimate the utility of engaging in collaboration versus remaining solitary, especially if the robots have only limited knowledge about the environment. In this paper, we use artificial evolution to synthesise neural controllers that let a homogeneous group of robots decide when to switch from solitary to collective actions based on the information gathered through time. However, being in a social scenario, the decision taken by a robot can influence—and is influenced itself—by the status of the other robots that are taking their own decisions at the same time. We show that the simultaneous presence of robots trying to decide whether to engage in a collective action or not can lead to cooperation in the decision making process itself. [ABSTRACT FROM AUTHOR]

One major criticism of direct or active perception (and other forms of embodied action) from the perspective of cognitive psycology is that, according to common sense, there are some actions that require strictly symbolic information — for example, to stop a car in response to a red traffic light — which fall outside the realm of a perception-action cycle. Although such cognitive responses are not necessarily a goal of artificial life, they must necessarily be included within the embodied paradigm if it is to encompass the cognisant individual, the self-aware individual, or, potentially, the conscious individual. This paper will address the question, ‘can an animat appreciate art?' Although this may seem very different to the example of a prosaic response to a traffic light, it will be argued that a common framework for establishing the meaning of an object is needed. It will also be argued that clarification to previous philosophical models of artistic engagement is required: in particular that the process of understanding is not a dialogue between an autopoietic artwork and animat, but that there is either a unity of object (artwork-animat) which becomes self-maintaining, or a more classical Gibsonian interpretation as a fixed set of affordances offered by an object to the subject, both of which lead to the conclusion that the process of understanding becomes a resonance in the unity or animat. [ABSTRACT FROM AUTHOR]

Some research works have mentioned the similarity of autopoiesis with (M,R) systems proposed by Rosen, from the perspective of closedness of the systems. However, there are some difference between the aspects of closedness required for autopoiesis and (M,R) systems. This paper aims at clarifying these differences to investigate the possibility of algebraic description of living systems, based on category theoretic frameworks. [ABSTRACT FROM AUTHOR]

Language can be viewed as a complex adaptive system which is continuously shaped and reshaped by the actions of its users as they try to solve communicative problems. To maintain coherence in the overall system, different language elements (sounds, words, grammatical constructions) compete with each other for global acceptance. This paper examines what happens when a language system uses systematic structure, in the sense that certain meaning-form conventions are themselves parts of larger units. We argue that in this case multi-level selection occurs: at the level of elements (e.g. tense affixes) and at the level of larger units in which these elements are used (e.g. phrases). Achieving and maintaining linguistic coherence in the population under these conditions is non-trivial. This paper shows that it is nevertheless possible when agents take multiple levels into account both for processing meaning-form associations and for consolidating the language inventory after each interaction. [ABSTRACT FROM AUTHOR]

We have developed a new reinforcement learning technique called Bayesian-discrimination-function-based reinforcement learning (BRL). BRL is unique, in that it not only learns in the predefined state and action spaces, but also simultaneously changes their segmentation. BRL has proven to be more effective than other standard RL algorithms in dealing with multi-robot system (MRS) problems, where the learning environment is naturally dynamic. This paper introduces an extended form of BRL that improves its learning efficiency. Instead of generating a random action when a robot encounters an unknown situation, the extended BRL generates an action calculated by a linear interpolation among the rules with high similarity to the current sensory input. In both physical experiments and computer simulations, the extended BRL showed higher search efficiency than the standard BRL. [ABSTRACT FROM AUTHOR]

We have developed a software system that simulates chemotaxis-based cell aggregation in 2D. The model implemented within the system consists of such cell behaviors as chemical diffusion/detection, motility, proliferation, adhesion and life cycle stages. Each virtual cell detects the state of the environment, and responds to the environment based on a pre-defined "program" and its own internal state. Cells are discrete units that are located on a grid, exist in discrete states (e.g. active or dying) and perform discrete tasks (e.g. divide and attach), but they also contain and are affected by continuous quantities (e.g. chemoattractant concentrations, gradients, age and velocities). This paper provides an overview of our chemotaxis-based aggregation model and details the algorithms required to perform chemotaxis-based cell aggregation simulation. A number of biological studies are being conducted with the system. They include fine-tuning the model parameters to reproduce in vitro PC12 cell aggregation experiments and parametric studies that demonstrate the effect that the model's components have on cell aggregation dynamics. [ABSTRACT FROM AUTHOR]

In this paper, we explore the emergence of acoustic categories in sensor arrays. We describe a series experiments on the automatic categorization of species and individual birds using self-organizing maps. Experimental results showed that meaningful acoustic categories can arise as self-organizing processes in sensor arrays. In addition, we discuss how distributed categorization could be used for the emergence of symbolic communication in these platforms. [ABSTRACT FROM AUTHOR]

In psychology the ‘A not B' error, whereby infants perseverate in reaching to the location where a toy was previously hidden after it has been moved to a new location, has been the subject of fifty years research since it was first identified by Piaget [1]. This paper describes a novel implementation of the ‘A not B' error paradigm which is used to test the notion that minimal systems evolutionary robotics modelling can be used to explore developmental process and to generate new hypotheses for test in natural experimental populations. The model demonstrates that agents controlled by plastic continuous time recurrent neural networks can perform the ‘A not B' task and that homeostatic mediation of plasticity can produce perseverative error patterns similar to those observed in human infants. In addition, the model shows a developmental trend for the production of perseverative errors to reduce during development. [ABSTRACT FROM AUTHOR]

Behavior-based artificial systems, e.g. mobile robots, are frequently designed using (various degrees and levels of) biology as inspiration, but rarely modeled based on actual quantitative empirical data. This paper presents a data-driven behavior-based model of a simple biological organism, the hydra. Four constituent behaviors were implemented in a simulated animal, and the overall behavior organization was accomplished using a colony-style architecture (CSA). The results indicate that the CSA, using a priority-based behavioral hierarchy suggested in the literature, can be used to model behavioral properties like latency, activation threshold, habituation, and duration of the individual behaviors of the hydra. Limitations of this behavior-based approach are also discussed. [ABSTRACT FROM AUTHOR]

This paper addresses the role of space in evolving a novel Non-Spatial GasNet model. It illustrates that this particular neural network model which make use of modulatory effects of diffusing gases has its evolvability improved when its neurons are not constrained to a Euclidean space. The results show that successful behaviour is achieved in fewer evaluations for the novel unconstrained GasNet than for the original model. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a method, Assortative Preferential Attachment, to grow a scale-free network with a given assortativeness value. Utilizing this method, we investigate information-cloning — recovery of scale-free networks in terms of their information transfer — and identify a number of recovery features: a full-recovery threshold, a phase transition for both assortative and disassortative networks, and a bell-shaped complexity curve for non-assortative networks. These features are interpreted with respect to two opposing tendencies dominating network recovery: an increasing amount of choice in adding assortative/disassortative connections, and an increasing divergence between the joint remaining-degree distributions of existing and required networks. [ABSTRACT FROM AUTHOR]

Scientific researchers from computer science, communication and as well from sociology and epidemiology reveal a strong interest in the study of networks. One important feature studied in complex network is the community structure. A new evolutionary technique for community detection in complex networks is proposed in this paper. The new algorithm is based on an information sharing mechanism between the individuals of a population. A real-world network is considered for numerical experiments. [ABSTRACT FROM AUTHOR]

This paper presents a new model for computational embryology that mimics the behaviour of biological cells, whose characteristics can be applied to the solution of computational problems. The presented tests apply the model to simple structure generation and provide promising results with regard to its behaviour and applicability to more complex problems. [ABSTRACT FROM AUTHOR]

In previous work [4] a framework was demonstrated that allows an autonomous robot to automatically synthesize physically-realistic models of its own body. Here it is demonstrated how the same approach can be applied to empower a robot to synthesize physically-realistic models of its surroundings. Robots which build numerical or other non-physical models of their environments are limited in the kinds of predictions they can make about the repercussions of future actions. In this paper it is shown that a robot equipped with a self-made, physically-realistic model can extrapolate: a slow-moving robot consistently predicts the much faster top speed at which it can safely drive across a terrain. [ABSTRACT FROM AUTHOR]

The paper presents a method to guide the self-organised development of behaviours of autonomous robots. In earlier publications we demonstrated how to use the homeokinesis principle and dynamical systems theory to obtain self-organised playful but goal-free behaviour. Now we extend this framework by reinforcement signals. We validate the mechanisms with two experiment with a spherical robot. The first experiment aims at fast motion, where the robot reaches on average about twice the speed of a not reinforcement robot. In the second experiment spinning motion is rewarded and we demonstrate that the robot successfully develops pirouettes and curved motion which only rarely occur among the natural behaviours of the robot. [ABSTRACT FROM AUTHOR]

Traditional eye-in-hand robotic systems are capable of performing versatile manipulation, but generally they can only observe a restricted workspace. As regards eye-to-hand configurations, tasks can be controlled within the field of view of the vision system with accuracy up to the pixel resolution of the vision system. In this paper, the robot workspace is further expanded by allowing cameras to be actively controlled by pan, tilt, and zoom motion. This configuration can be applied to a mobile robot equipped with a binocular vision system. The manipulator for visual servo control purpose can be either mounted on-board or in fixed configuration. To enable large and flexible workspace visual servoing with precision under such a configuration, active hand-eye coordination must be assured. The control strategy is successfully validated through convergence analysis and simulations. [ABSTRACT FROM AUTHOR]

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 energy 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 extreme 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In robotics, exogenous fault detection is the process through which one robot detects faults that occur in other, physically separate robots. In this paper, we study exogenous fault detection in a collective leader-follower task for autonomous robots. We record sensory inputs from the robots while they are operating normally and after simulated faults have been injected. Given that faults are simulated, we can correlate the flow of sensory inputs with the fault state of the robots. We use back-propagation neural networks to synthesize fault detection components. We show that the flow of sensory inputs is sufficient information for performing exogenous fault detection, that is, we show that the leader robot is capable of detecting faults in the follower robot. All results are based on experiments with real robots. [ABSTRACT FROM AUTHOR]

Pioneer approaches to Artificial Intelligence have traditionally neglected, in a chronological sequence, the agent body, the world where the agent is situated, and the other agents. With the advent of Collective Robotics approaches, important progresses were made toward embodying and situating the agents, together with the introduction of collective intelligence. However, the currently used models of social environments are still rather poor, jeopardizing the attempts of developing truly intelligent robot teams. In this paper, we propose a roadmap for a new approach to the design of multi-robot systems, mainly inspired by concepts from Institutional Economics, an alternative to mainstream neoclassical economic theory. Our approach intends to sophisticate the design of robot collectives by adding, to the currently popular emergentist view, the concepts of physically and socially bounded autonomy of cognitive agents, uncoupled interaction among them and deliberately set up coordination devices. [ABSTRACT FROM AUTHOR]

It is well known that certain environmental conditions, such as a spatially structured population, can promote the evolution of cooperative traits. However, such conditions are usually assumed to be externally imposed. In this paper, we present a model that allows the conditions that promote or hinder cooperation to arise adaptively via individual selection. Consequently, instead of selection simply favouring cooperation under imposed environmental conditions, in our model selection also operates on the conditions themselves via a niche construction process. Results are presented that show that the conditions that favour cooperation can evolve, even though those that favour selfish behaviour are also available and are initially selected for. [ABSTRACT FROM AUTHOR]

Superstrings Installations (Torino 2005, Cetraro 2005, Milano 2005) - where Art and Science were directly conjugated - pictures and movies have been later collected into a DVD produced in a strictly limited edition and in few copies. These DVDs have been part of a Generative Art Installation presented in Milano in December 2006. A small robotic artifact has been programmed with very simple rules to trace on the DVD surfaces space "visual superstrings", which changed depending both on the interaction with the public and the configuration of the DVDs on the floor. At the end, a unique artwork was generated. This paper discusses the ideas of A-Life Art, the related aesthetic concepts and how the use and programming of a robot has allowed to create A-Life Artistic object inspired by the Superstring Installation through semi-adaptive robotic behaviour. [ABSTRACT FROM AUTHOR]

In this paper we present an extension of the ShapeBugs distributed formation algorithm which enables 2D mobile agents to agree on a consensus coordinate system starting from no coordinate agreement. The participating agents require only local communication and local distance and motion information. Because this distance and motion information can in many cases be obtained implicitly through software motor approximation and attenuation or time-of-flight in communication, this approach has the potential to globally coordinate general mobile communicating agents without additional sensor requirements. The extended algorithm also remains robust against agent incapacitation and disorientation. [ABSTRACT FROM AUTHOR]

Agency detection is studied in a simple simulated model with embodied agents. Psychological experiments such as double TV-monitor experiments and perceptual crossing show the central role of dynamic mutuality and contingency in social interactions. This paper explores the ongoing dynamical aspects of minimal agency detection in terms of the mutuality and contingency. It is investigated how the embodied agents can establish a live interaction and discriminate this from interactions from recorded motions that are identical to the live interaction but cannot react contingently. Our results suggest that the recognition of the presence of another's agency need not lie on complex cognitive individual mechanisms able to integrate past information, but rather on the situated ongoingness of the interaction process itself, on its dynamic properties, and its robustness to noise. [ABSTRACT FROM AUTHOR]

This paper examines the effects of lifetime learning on the diversity and fitness of a population. Our experiments measure the phenotypic diversity of populations evolving by purely genetic means (population learning) and of others employing both population learning and lifetime learning. The results obtained show, as in previous work, that the addition of lifetime learning results in higher levels of fitness than population learning alone. More significantly, results from the diversity measure show that lifetime learning is capable of sustaining higher levels of diversity than population learning alone. [ABSTRACT FROM AUTHOR]

In this paper we describe a model in which artificial evolution is employed to design neural mechanisms that control the motion of two autonomous robots required to communicate through sound to perform a common task. The results of this work are a "proof-of-concept": they demonstrate that evolution can exploit a very simple sound communication system, to design the mechanisms that allow the robots cooperate by employing acoustic interactions. The analysis of the evolved strategies uncover the basic properties of the communication protocol. [ABSTRACT FROM AUTHOR]

This paper describes a study in the use of digital evolution to produce cooperative communication behavior in a population of digital organisms. The results demonstrate that digital evolution can produce organisms capable of distributed problem solving through interactions between members of the population and their environment. Specifically, the organisms cooperate to distribute among the population the largest value sensed from the environment. These digital organisms have no "built-in" ability to perform this task; each population begins with a single organism that has only the ability to self-replicate. Over thousands of generations, random mutations and natural selection produce an instruction sequence that realizes this behavior, despite continuous turnover in the population. [ABSTRACT FROM AUTHOR]

Using a simple simulation model of evolution and learning, this paper provides an evolutionary argument why Lamarckian inheritance - the direct transfer of lifetime learning from parent to offspring - may be so rare in nature. Lamarckian inheritance allows quicker genetic adaptation to new environmental conditions than non-lamarckian inheritance. While this may be an advantage in the short term, it may be detrimental in the long term, since the population may be less well prepared for future environmental changes than in the absence of Lamarckianism. [ABSTRACT FROM AUTHOR]

Artificial agents controlled by dynamic recurrent node networks with fixed weights are evolved to search for food and associate it with one of two different temperatures depending on experience. The task requires either instrumental or classical conditioned responses to be learned. The paper extends previous work in this area by requiring that a situated agent be capable of re-learning during its lifetime. We analyse the best-evolved agent's behaviour and explain in some depth how it arises from the dynamics of the coupled agent-environment system. [ABSTRACT FROM AUTHOR]

Episodic memory represents personal history of an entity. Human-like agents with a full episodic memory are able to reconstruct their personal stories to a large extent. Since these agents typically live in dynamic environments that change beyond their capabilities, their memory must cope with determining trustfulness of memory records. In this paper, we propose an associative network addressing this issue with regard to records about objects an agent met during its live. The network is presently being implemented into our case-study human-like agent with a full episodic memory. [ABSTRACT FROM AUTHOR]

Cultural learning allows individuals to acquire knowledge from others through non-genetic means. The effect of cultural learning on the evolution of artificial organisms has been the focus of much research. This paper examines the effects of cultural learning on the fitness and diversity of a population and, in addition, the effect of self-adaptive cultural learning parameters on the evolutionary process. The NK fitness landscape model is employed as the problem task and experiments employing populations endowed with both evolutionary and cultural learning are compared to those employing evolutionary learning alone. Our experiments measure the fitness and diversity of both populations and also track the values of two self-adaptive cultural parameters. Results show that the addition of cultural learning has a beneficial effect on the population in terms of fitness and diversity maintenance. Furthermore, analysis of the self-adaptive parameter values shows the relative quality of the cultural process throughout the experiment and highlights the benefits of self-adaptation over fixed parameter values. [ABSTRACT FROM AUTHOR]

Studying the role played by evolution and learning in adaptive behavior is a very important topic in artificial life research. This paper investigates the interplay between learning and evolution when agents have to solve several different tasks, as it is the case for real organisms but typically not for artificial agents. Recently, an important thread of research in machine learning and developmental robotics has begun to investigate how agents can solve different tasks by composing general skills acquired on the basis of internal motivations. This work presents a hierarchical, neural-network, actor-critic architecture designed for implementing this kind of intrinsically motivated reinforcement learning in real robots. We compare the results of several experiments in which the various components of the architecture are either trained during lifetime or evolved through a genetic algorithm. The most important results show that systems using both evolution and learning outperform systems using either one of the two, and that, among the former, systems evolving internal reinforcers for learning building-block skills have a higher evolvability than those directly evolving the related behaviors. [ABSTRACT FROM AUTHOR]

Adaptive responses to resource availability are common in natural systems. In this paper we explore one possible evolutionary cause of adaptive sleep/wake behavior. We subjected populations of digital organisms to an environment with a slowly diminishing resource and recorded their ability to adapt to the changing environment using sleep. We also quantified the selective pressure not to sleep in this competitive environment. We observed that diminishing resource availability can promote adaptive sleep responses in digital organisms even when there is an opportunity cost associated with sleeping. [ABSTRACT FROM AUTHOR]

This paper investigates the processes used by an evolved, embodied simulated agent to adapt to large disruptive changes in its sensor morphology, whilst maintaining performance in a phototaxis task. By avoiding the imposition of separate mechanisms for the fast sensorimotor dynamics and the relatively slow adaptive processes, we are able to comment on the forms of adaptivity which emerge within our Evolutionary Robotics framework. This brings about interesting notions regarding the relationship between different timescales. We examine the dynamics of the network and find different reactive behaviours depending on the agent's current sensor configuration, but are only able to begin to explain the dynamics of the transitions between these states with reference to variables which exist in the agent's environment, as well as within its neural network ‘brain'. [ABSTRACT FROM AUTHOR]

The interaction of brain, body and environment can result in complex behavior with rich dynamics even for relatively simple agents. Such dynamics are, however, often notoriously difficult to analyze. In this paper we explore the case of a simple simulated robotic agent, equipped with a reactive neurocontroller and an energy level, that the agent has been evolved to re-charge. A dynamical systems analysis, shows that a non-neural internal state (energy level), despite its simplicity, dynamically modulates the agent-environment system's behavioral attractors, such that the robot's behavioral repertoire is continually adapted to its current situation and energy level. [ABSTRACT FROM AUTHOR]

There is a general agreement when modelling ecosystems that the simplest solutions are generally the best. Comparing water temperature models that affect the feeding rate of starfish can show similar results when simulated under a simple scenario. When the system is modified to include environmental change, water temperature models that have similar mean temperatures but differences in variance can produce variable results that correlate to the magnitude of their variance. This paper will examine and compare the effect that four water temperature models, each with similar mean temperatures, has on the predation of mussels by starfish, and how this affects population densities over time. Results find that water temperature models with comparable variance produce similar results; models that differ in variance produce dissimilar results, especially when environmental conditions capitalise on that variance. [ABSTRACT FROM AUTHOR]