Robustness in artificial life

Finding robust explanations of behaviours in Alife and related fields is made difficult by the lack of any formalised definition of robustness. A concerted effort to develop a framework which allows for robust explanations of those behaviours to be developed is needed, as well as a discussion of what constitutes a potentially useful definition for behavioural robustness. To this end, we describe two senses of robustness: robustness in systems; and robustness in explanation. We then propose a framework for developing robust explanations using linked sets of models, and describe a programme of research incorporating both robotics and chemical experiments which is designed to investigate robustness in systems

Studying a Self-Sustainable System by Making a Mind Time Machine

Abstract We present our pilot study on a special machine that selfsustains its rich dynamics in an open environment. We made a machine called "MTM" (Mind Time Machine) that runs all day long, receiving massive visual data from the environment, processing by an internal neural dynamics with a learning capability, and showing sustainable complex adaptive dynamics. The System's internal time structure is also self-organized as a result of coupling with the environment. By observing MTM over 2 and half months, we argue for the possibility of machine consciousness in an artificial system. Keywords mind time, massive data, plasticity, sustainability Robustness and system design It is time for bringing artificial life in silicon into the real world. In contrast to the artificially simulated environment, the real world presents many unexpected complex encounters, and living systems are essentially adaptive to these real world complexities. In this pilot study, we designed an artificial system that can be a first test system for overcoming various problems for artificial systems to "survive" in an open ended environment. We required that any artificial life should simultaneously cope with various kinds of sensory flows while simultaneously maintaining its own identity and autonomy over a relatively long period of time. In creating such a machine, our main concern is how to design a system's time structure. A human has subjective time structures which is different from objective time. Our hypothesis is that this should be true for all intenPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Workshop on Self-sustaining Systems (S3) 2010 September 27-28, 2010, The University of Tokyo, Japan Copyright c 2010 ACM 978-1-4503-0491-7/10/09. . . $10.00 tional/functional systems, whether natural or artificial. Objective time structures, i.e. the physical Newtonian time scale, can be measured by a mechanical clock, but our mind's time scale, the so-called Bergsonian time scale, may not be treated the same way. That is, a minimal-length time segment can be regarded as infinitesimally small in the case of Newtonian time, but in Bergsonian time it can be bounded. I submit that there is no continuous time flow which can be assumed, as it is always perturbed by the inflow from an open-ended environment. Wiener's definition of Bergsonian time, as opposed to Newtonian time (chapter 1 in Some authors In our case, a novel biochemical experiment together with simulation and robotics approaches are being used to develop an in-depth understanding of robustness and how we may quantify and examine its effects 1 We define robustness of the droplets with respect to their ability to sustain self-moving behavior. In contrast, if we pick an example from the game of Life, gliders (the simplest moving pattern in the game) appear to display self-moving behavior but do not actually function in this way. This evolution of self-movement, autonomy and individuality appears to be a key prerequisite for developing robust behaviors. Using a robotic platform, we used pure Hebbian learning dynamics to show how auditory and visual modules cooperatively work together to self-organize robust goaloriented behavior By increasing our understanding of how we can connect artificial systems with natural environments, we can further our development of a theoretical framework that provides a background of assumptions to inform our robotic and simulated models. One of my proposals is the Maximal Design Principle Concerning the above robustness issue, we designed a machine called MTM (Mind Time Machine). In order to take into account the system's internal time structure, Benjamin Libet's neuro-physiological early experiments In our pilot work, the system receives and edits the video inputs, while it self-organizes the momentary "now," in agreement with Libet's arguments. Its core program is a neural network that includes chaos (a mechanism that expands the small difference) inside the system, and a metanetwork that consists of neural networks. Using this system as a hardware, and chaotic itinerancy In section 2, we illustrate the architecture of MTM explaining the underlying neural dynamics. In section 3, we report how MTM behaves over 2.5 months and show some characterization of the behaviors-its temporal complexity and dynamics of the internal clock. In section 4, we briefly describe how a sound version of MTM might function, and report on the pilot study of it. In section 5, we discuss how a system's sustainability is restored by the asynchronous memory updating and sensory networks. We then return to the Bergsonian vs. Newtonian time scale issue