Phototaxic foraging of the archaepaddler, a hypothetical deep-sea species

An autonomous agent (animat, hypothetical animal), called the (archae) paddler, is simulated in sufficient detail to regard its simulated aquatic locomotion (paddling) as physically possible. The paddler is supposed to be a model of an animal that might exist, although it is perfectly possible to view it as a model of a robot that might be built. The agent is assumed to navigate in a simulated deep-sea environment, where it hunts autoluminescent prey. It uses a biologically inspired phototaxic foraging-strategy, while paddling in a layer just above the bottom. The advantage of this living space is that the navigation problem is essentially two-dimensional. Moreover, the deep-sea environment is physically simple (and hence easier to simulate): no significant currents, constant temperature, completely dark. A foraging performance metric is developed that circumvents the necessity to solve the travelling salesman problem. A parametric simulation study then quantifies the influence of habitat factors, such as the density of prey, and the body-geometry (e.g. placement, direction and directional selectivity of the eyes) on foraging success. Adequate performance proves to require a specific body-% geometry adapted to the habitat characteristics. In general performance degrades smoothly for modest changes of the geometric and habitat parameters, indicating that we work in a stable region of 'design space'. The parameters have to strike a compromise between on the one hand the ability to 'fixate' an attractive target, and on the other hand to 'see' as many targets at the same time as possible. One important conclusion is that simple reflex-based navigation can be surprisingly efficient. In the second place, performance in a global task (foraging) depends strongly on local parameters like visual direction-tuning, position of the eyes and paddles, etc. Behaviour and habitat 'mould' the body, and the body-geometry strongly influences performance. The resulting platform enables further testing of foraging strategies, or vision and locomotion theories stemming either from biology or from robotics

Knowledge-based vision and simple visual machines

The vast majority of work in machine vision emphasizes the representation of perceived objects and events: it is these internal representations that incorporate the 'knowledge' in knowledge-based vision or form the 'models' in model-based vision. In this paper, we discuss simple machine vision systems developed by artificial evolution rather than traditional engineering design techniques, and note that the task of identifying internal representations within such systems is made difficult by the lack of an operational definition of representation at the causal mechanistic level. Consequently, we question the nature and indeed the existence of representations posited to be used within natural vision systems (i.e. animals). We conclude that representations argued for on a priori grounds by external observers of a particular vision system may well be illusory, and are at best place-holders for yet-to-be-identified causal mechanistic interactions. That is, applying the knowledge-based vision approach in the understanding of evolved systems (machines or animals) may well lead to theories and models that are internally consistent, computationally plausible, and entirely wrong

De simbólicos vs. subsimbólicos, a los robots etoinspirados

En la Inteligencia Artificial, desde sus orígenes, han existido dos corrientes básicas, la simbólica y la subsimbólica. Estas dos aproximaciones han tenido gran influencia también en la robótica. En este artículo queremos presentar un enfoque menos conocido, el de la etología, y en concreto su aplicación a la generación de comportamiento autónomo en robots móviles. Para ello presentamos los fundamentos de la "Jerarquía Dinámica de Esquemas", una arquitectura para el control de robots móviles, basada en la composición de unidades simples denominadas "esquemas" siguiendo las teorías etológicas de Arbib. Igualmente se presentan experimentos preliminares que validan esta aproximación y se discute su viabilidad y se presentan los trabajos previstos para continuar investigando en esta líne

An Evolutionary Active-Vision System

We describe an evolutionary vision system capable of autonomously scanning through an image while zooming in and out and changing filtering strategy in order to perform shape discrimination. The system consists of a small artificial retina controlled by an evolutionary recurrent neural network without hidden units. We show that such a simple active-vision system can success-fully recognize different shapes independently of their position and size by dynamically exploring relevant parts of the image. We also show that a standard feed-forward neural network trained with the back-propagation algorithm cannot perform the task, not even with hidden units added to the architecture. Given its compactness, computational requirements, and versatility, this evolutionary active vision system is a suitable solution for small-size and embedded vision systems with stringent energetic and computational requirements, such as micro-robotic systems. In addition, this approach provides a framework for studying emergent active-vision behaviors in autonomous systems

Social Preference in Juvenile Zebrafish

Social behaviours are essential for the survival and reproduction of many species, including our own. A fundamental feature of all social behaviour is social preference, which is an individual’s propensity to interact with members of their species (termed conspecifics). In an average population, various social preference behaviours are readily observed, ranging from uninterested (not engaging with conspecifics) to very social (engaging with conspecifics). Individuals expressing these behaviours are typically labelled as having an asocial or prosocial, respectively. Little is known about how the underlying social circuitry gives rise to such distinct social behaviours in the population. It is well established that adverse social experiences can impact social behaviour, including isolation during early development. Undesired social isolation (loneliness) alters behavioural patterns, neuroanatomy (e.g., brain volume) and neurochemistry in ways that resemble developmental neuropsychiatric disorders, including autism and schizophrenia. However, few studies have investigated the impact of early life isolation on social circuitry, and how this results in dysfunctional social behaviour commonly associated with these and other disorders. In this thesis, juvenile zebrafish was used to model social preference behaviour, as it is an excellent translational model for human developmental and behavioural disorders. Population-level analysis revealed that several features of social preference behaviour could be summarised via Visual Preference Index (VPI) scores representing sociality. Using multiple behavioural parameters, comprehensive investigations of asocial and prosocial fish identified via VPIs revealed distinct responses towards conspecifics between the two phenotypes. These initial results served as a baseline for facilitating the identification of atypical social behaviour following periods of social isolation. The impact of isolation on social preference was assessed by applying either the full isolation over the initial three weeks of development or partial isolation, 48 hours or 24 hours, before testing. Following periods of social isolation, juvenile zebrafish displayed anxiety-like behaviours. Furthermore, full and partial isolation of 48 hours, but not 24 hours, altered responses to conspecifics. To assess the impact of social isolation on the social circuitry, the brain activities of fish were analysed and compared between different rearing conditions using high-resolution two-photon imaging. Whole-brain functional maps of isolated social phenotypes were distinct from those in the average population. Isolation-induced activity changes were found mainly in brain regions linked to social behaviour, social cue processing, and anxiety/stress (e.g., the caudal hypothalamus and preoptic area). Since some of these affected regions are modulated by serotonin, the reversibility of the adverse effects of social isolation on preference behaviour was investigated by using pharmacological manipulation of the monoaminergic system. The administration of an anxiolytic the drug buspirone demonstrated that altered social preference behaviour in isolated fish could be rescued by acutely reducing serotonin levels. By investigating social preference at the behavioural and functional level in wild-type juvenile zebrafish, this work contributes to our understanding of how the social brain circuity produces diverse social preferences. Furthermore, it provides important information on how early-life environmental adversity gives rise to atypical social behaviour and the neurotransmitters modulating the circuit, offering new opportunities for effective intervention

Old tricks, new dogs : ethology and interactive creatures

Thesis (Ph. D.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1997.Includes bibliographical references (p. 135-140).by Bruce Mitchell Blumberg.Ph.D

Explorations into the behaviour-oriented nature of intelligence : Fuzzy behavioural maps.

This thesis explores the behaviour-oriented nature of intelligence and presents the definition and use of Fuzzy Behavioural Maps (FBMs) as a flexible development framework for providing complex autonomous agent behaviour. This thesis provides a proof-of-concept for simple FBMs, including some experimental results in Mobile Robotics and Fuzzy Logic Control. This practical work shows the design of a collision avoidance behaviour (of a mobile robot) using a simple FBM and, the implementation of this using a Fuzzy Logic Controller (FLC). The FBM incorporates three causally related sensorimotor activities (moving around, perceiving obstacles and, varying speed). This Collision Avoidance FBM is designed (in more detail) using fuzzy relations (between levels of perception, motion and variation of speed) in the form of fuzzy control rules. The FLC stores and manipulates these fuzzy control (FBM) rules using fuzzy inference mechanisms and other related implementation parameters (fuzzy sets and fuzzy logic operators). The resulting FBM-FLC architecture controls the behaviour patterns of the agent. Its fuzzy inference mechanisms determine the level of activation of each FBM node while driving appropriate control actions over the creature's motors. The thesis validates (demonstrates the general fitness of) this control architecture through various pilot tests (computer simulations). This practical work also serves to emphasise some benefits in the use of FLC techniques to implement FBMs (e.g. flexibility of the fuzzy aggregation methods and fuzzy granularity).More generally, the thesis presents and validates a FBM Framework to develop more complex autonomous agent behaviour. This framework represents a top-down approach to derive the BB models using generic FBMs, levels of abstraction and refinement stages. Its major scope is to capture and model behavioural dynamics at different levels of abstraction (through different levels of refinement). Most obviously, the framework maps some required behaviours into connection structures of behaviour-producing modules that are causally related. But the main idea is following as many refinement stages as required to complete the development process. These refinement stages help to identify lower design parameters (i.e. control actions) rather than linguistic variables, fuzzy sets or, fuzzy inference mechanisms. They facilitate the definition of the behaviours selected from first levels of abstraction. Further, the thesis proposes taking the FBM Framework into the implementation levels that are required to build BB control architecture and provides and application case study. This describes how to develop a complex, non-hierarchical, multi-agent behaviour system using the refinement capabilities of the FBM Framework. Finally, the thesis introduces some more general ideas about the use of this framework to cope with some, current complexity issues around the behaviour-oriented nature of intelligence