t for Two: Linear Synergy Advances the Evolution of Directional Pointing Behaviour

Rohde M, Di Paolo E. t for Two: Linear Synergy Advances the Evolution of Directional Pointing Behaviour. In: Capcarrere M, Freitas A, Bentley P, Johnson C, Timmis J, eds. Advances in Artificial Life: 8th European Conference, ECAL 2005, Canterbury, UK, September 5-9, 2005, Proceedings. Vol 3630. Heidelberg: Springer; 2005: 262-271

Optimizing Associative Information Transfer within Content-addressable Memory

Original article can be found at: http://www.oldcitypublishing.com/IJUC/IJUC.htmlPeer reviewe

"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

El mundo de las ciencias de la complejidad

La situación es verdaderamente apasionante. Mientras que en el mundo llamado real –y entonces se hace referencia a dominios como la política, la economía, los conflictos militares y sociales, por ejemplo–, la percepción natural –digamos: de los medios y la opinión pública– es que el país y el mundo se encuentran en condiciones difíciles; en algunos casos, dramática; y en muchas ocasiones trágica, en el campo del progreso del conocimiento asistimos a una magnífica vitalidad. Esta vitalidad se expresa en la ciencia de punta y, notablemente, en las ciencias de la complejidad. Mientras que la ciencia normal –para volver a la expresión de Kuhn– se encuentra literalmente a la defensiva en numerosos campos, temas y problemas –digamos, a la defensiva con respecto al decurso de los acontecimientos y a las dinámicas del mundo contemporáneo–, en el contexto del estudio de los sistemas complejos adaptativos asistimos a una vitalidad que es prácticamente desconocida para la corriente principal de académicos –independientemente de los niveles en los que trabajan–, de científicos, de administradores de educación y de ciencia y tecnología (por ejemplo rectores, vicerrectores, decanos, directores de departamentos, tomadores de decisión, políticos y gobernantes). La corriente principal del conocimiento (mainstream) desconoce una circunstancia, un proceso, una dinámica que sí es conocida por parte de quienes trabajan e investigan activamente en el campo de las ciencias de la complejidad

A Process-Oriented Architecture for Complex System Modelling

A fine-grained massively-parallel process-oriented model of platelets (potentially artificial) within a blood vessel is presented. This is a CSP inspired design, expressed and implemented using the occam-pi language. It is part of the TUNA pilot study on nanite assemblers at the universities of York, Surrey and Kent. The aim for this model is to engineer emergent behaviour from the platelets, such that they respond to a wound in the blood vessel wall in a way similar to that found in the human body -- i.e. the formation of clots to stem blood flow from the wound and facilitate healing. An architecture for a three dimensional model (relying strongly on the dynamic and mobile capabilities of occam-pi) is given, along with mechanisms for visualisation and interaction. The biological accuracy of the current model is very approximate. However, its process-oriented nature enables simple refinement (through the addition of processes modelling different stimulants/inhibitors of the clotting reaction, different platelet types and other participating organelles) to greater and greater realism. Even with the current system, simple experiments are possible and have scientific interest (e.g. the effect of platelet density on the success of the clotting mechanism in stemming blood flow: too high or too low and the process fails). General principles for the design of large and complex system models are drawn. The described case study runs to millions of processes engaged in ever-changing communication topologies. It is free from deadlock, livelock, race hazards and starvation em by design, employing a small set of synchronisation patterns for which we have proven safety theorems

Diverse Exploration via InfoMax Options

In this paper, we study the problem of autonomously discovering temporally abstracted actions, or options, for exploration in reinforcement learning. For learning diverse options suitable for exploration, we introduce the infomax termination objective defined as the mutual information between options and their corresponding state transitions. We derive a scalable optimization scheme for maximizing this objective via the termination condition of options, yielding the InfoMax Option Critic (IMOC) algorithm. Through illustrative experiments, we empirically show that IMOC learns diverse options and utilizes them for exploration. Moreover, we show that IMOC scales well to continuous control tasks.Comment: Preprint. Under revie

Development and Evaluation of Sensor Concepts for Ageless Aerospace Vehicles: Report 6 - Development and Demonstration of a Self-Organizing Diagnostic System for Structural Health Monitoring

This report describes a significant advance in the capability of the CSIRO/NASA structural health monitoring Concept Demonstrator (CD). The main thrust of the work has been the development of a mobile robotic agent, and the hardware and software modifications and developments required to enable the demonstrator to operate as a single, self-organizing, multi-agent system. This single-robot system is seen as the forerunner of a system in which larger numbers of small robots perform inspection and repair tasks cooperatively, by self-organization. While the goal of demonstrating self-organized damage diagnosis was not fully achieved in the time available, much of the work required for the final element that enables the robot to point the video camera and transmit an image has been completed. A demonstration video of the CD and robotic systems operating will be made and forwarded to NASA

Advances in Artificial Life 8th European Conference, ECAL 2005, Canterbury, UK, September 5-9, 2005. Proceedings

The Artificial Life term appeared more than 20 years ago in a small corner of New Mexico, USA. Since then the area has developed dramatically, many researchers joining enthusiastically and research groups sprouting everywhere. This frenetic activity led to the emergence of several strands that are now established fields in themselves. We are now reaching a stage that one may describe as maturer: with more rigour, more benchmarks, more results, more stringent acceptance criteria, more applications, in brief, more sound science. This, which is the natural path of all new areas, comes at a price, however. A certain enthusiasm, a certain adventurousness from the early years is fading and may have been lost on the way. The field has become more reasonable. To counterbalance this and to encourage lively discussions, a conceptual track, where papers were judged on criteria like importance and/or novelty of the concepts proposed rather than the experimental/theoretical results, has been introduced this year. A conference on a theme as broad as Artificial Life is bound to be very diverse, but a few tendencies emerged. First, fields like ‘Robotics and Autonomous Agents’ or ‘Evolutionary Computation’ are still extremely active and keep on bringing a wealth of results to the A-Life community. Even there, however, new tendencies appear, like collective robotics, and more specifically self-assembling robotics, which represent now a large subsection. Second, new areas appear

ELDEN: Exploration via Local Dependencies

Tasks with large state space and sparse rewards present a longstanding challenge to reinforcement learning. In these tasks, an agent needs to explore the state space efficiently until it finds a reward. To deal with this problem, the community has proposed to augment the reward function with intrinsic reward, a bonus signal that encourages the agent to visit interesting states. In this work, we propose a new way of defining interesting states for environments with factored state spaces and complex chained dependencies, where an agent's actions may change the value of one entity that, in order, may affect the value of another entity. Our insight is that, in these environments, interesting states for exploration are states where the agent is uncertain whether (as opposed to how) entities such as the agent or objects have some influence on each other. We present ELDEN, Exploration via Local DepENdencies, a novel intrinsic reward that encourages the discovery of new interactions between entities. ELDEN utilizes a novel scheme -- the partial derivative of the learned dynamics to model the local dependencies between entities accurately and computationally efficiently. The uncertainty of the predicted dependencies is then used as an intrinsic reward to encourage exploration toward new interactions. We evaluate the performance of ELDEN on four different domains with complex dependencies, ranging from 2D grid worlds to 3D robotic tasks. In all domains, ELDEN correctly identifies local dependencies and learns successful policies, significantly outperforming previous state-of-the-art exploration methods.Comment: Accepted to NeurIPS 202