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Using the biologically inspired notion of ‘virtual pheromone’ we describe how a robot swarm can become a distributed computing mesh embedded within the environment, while simultaneously acting as a physical embodiment of the user interface. By virtue of this simple peer-to-peer messaging scheme, many coordinated activities can be accomplished without centralized control.

El libro: condena y salvación
La transtextualidad en El Quijote
Heteronimia y ludonimia: dos juegos hipertextuales
Dos héroes: clasicismo y romanticismo
Lo literario y lo prosaico en el discurso de Morelli
Cartucho: la pulsión de lo femenino
La voz del subalterno: ¿sujetos representados?

Symmetry breaking is commonly found in self-organized collective decision making. It serves an important functional role, specifically in biological and bio-inspired systems. The analysis of symmetry breaking is thus an important key to understanding self-organized decision making. However, in many systems of practical importance avail-able analytic methods cannot be applied due to the complexity of the scenario and consequentially the model. This applies specifically to self-organization in bio-inspired engineering. We propose a new modelling approach which allows us to formally analyze important properties of such processes. The core idea of our approach is to infer a compact model based on stochastic processes for a one-dimensional symmetry parameter. This enables us to analyze the fundamental properties of even complex collective decision making processes via Fokker–Planck theory. We are able to quantitatively address the effectiveness of symmetry breaking, the stability, the time taken to reach a consensus, and other parameters. This is demonstrated with two examples from swarm robotics

This paper shows how ontogenetic programming , an enhancement to the genetic programming methodology, a l l o ws for the automatic generation of adaptive programs. Programs produced by o n togenetic programming may include calls to self-modiication operators. By permitting runtime program self-modiication, these operators allow e v olved programs to further adapt to their environments. In this paper the onto-genetic programming methodology is described and two examples of its use are presented, one for binary sequence prediction and the other for action selection in a virtual world. In both cases the inclusion of self-modiication operators has a clear positive impact on the ability of genetic programming to produce successful programs.

Research on anticipatory behavior in adaptive learning systems continues to gain more recognition and appreciation in various research disciplines. This book provides an overarching view on anticipatory mechanisms in cognition, learning, and behavior. It connects the knowledge from cognitive psychology, neuroscience, and linguistics with that of artificial intelligence, machine learning, cognitive robotics, and others. This introduction offers an overview over the contributions in this volume highlighting their interconnections and interrelations from an anticipatory behavior perspective. We first clarify the main foci of anticipatory behavior research. Next, we present a taxonomy of how anticipatory mechanisms may be beneficially applied in cognitive systems. With relation to the taxonomy, we then give an overview over the book contributions. The first chapters provide surveys on currently known anticipatory brain mechanisms, anticipatory mechanisms in increasingly complex natural languages, and an intriguing challenge for artificial cognitive systems. Next, conceptualizations of anticipatory processes inspired by cognitive mechanisms are provided. The conceptualizations lead to individual, predictive challenges in vision and processing of event correlations over time. Next, anticipatory mechanisms in individual decision making and behavioral execution are studied. Finally, the book offers systems and conceptualizations of anticipatory processes related to social interaction.

We present a methodology for characterizing, analyzing, and synthesizing swarm behaviors using both a macroscopic continuous model that represents a swarm as a continuum and a macroscopic discrete model that enumerates individual agents. Our methodology is applied to a dynamical model of ant house hunting, a decentralized process in which a colony attempts to emigrate to the best site among

The origin and structural and functional significance of modular design in organisms represent an important issue debated in many different disciplines. To be eventually successful in clarifying the evolutionary mechanisms underpinning the emergence of modular design in complex organisms, one should be able to cover all different levels of biological hierarchy. Specifically, one should be able to investigate modularity at the behavioral level-the level on which natural selection operates-and ...

We present a neuromimetic navigation system modelling the columnar structure of the cortex to mediate spatial learning and action planning. The model has been validated on a spatial behavioural task, namely the Tolman & Honzik’s detour protocol, which allowed us to test the ability of the system to build a topological representation of the environment, and to use it to exhibit flexible goal-directed behaviour (i.e., to predict the outcome of alternative trajectories to avoid blocked pathways). First, it is shown that the model successfully reproduces the navigation performance of rodents in terms of goal-directed path selection. Second, we report on the neural response patterns characterising the learnt columnar space representation.

In this work, we are interested in understanding how emotional interactions with a social partner can bootstrap increasingly complex behaviors such as social referencing. Our idea is that social referencing as well as facial expression recognition can emerge from a simple sensori-motor system involving emotional stimuli. Without knowing that the other is an agent, the robot is able to learn some complex tasks if the human partner has some ”empathy” or at least ”resonate” with the robot head (low level emotional resonance). Hence we advocate the idea that social referencing can be bootstrapped from a simple sensori-motor system not dedicated to social interactions.

Ant foraging is a paradigmatic example of self-organized behavior. We give new experimental evidence for previously unobserved short-term adaptiveness in ant foraging and show that current mathematical foraging models cannot predict this behavior. As a true extension, we develop Itô diffusion models that explain the newly discovered behavior qualitatively and quantitatively. The theoretical analysis is supported by individual-based simulations. Our work shows that randomness is a key factor in allowing self-organizing systems to be adaptive. Implications for technical applications of Swarm Intelligence are discussed.

The active vision and attention-for-action frameworks propose that in organisms attention and perception are closely integrated with action and learning. This work proposes a novel bio-inspired integrated neural-network architecture that on one side uses attention to guide and furnish the parameters to action, and on the other side uses the effects of action to train the task-oriented top-down attention components of the system. The architecture is tested both with a simulated and a real camera-arm robot engaged in a reaching task. The results highlight the computational opportunities and difficulties deriving from a close integration of attention, action and learning.