The International Society for Artificial Life is organizing the Third ISAL Summer School on Artificial Life, hosted by ALIFE 2016 on Wednesday July 6th. This school is open to all ALIFE participants whose registration includes attendance on Wednesday.
| 10:30 – 10:35 | Welcome and introduction
René Doursat (Manchester Metropolitan University, UK) |
| 10:35 – 11:50 | How complexity drives ALife’s philosophical foundations and its scientific challenges and opportunities
Mark Bedau (Reed College, USA) |
| 11:50 – 13:05 | Engineering and controlling self-organizing systems Carlos Gershenson (Universidad Nacional Autónoma de México) |
| 16:00 – 17:15 | Steering complex human systems Alexandra Penn (University of Surrey, UK) |
| 17:15 – 18:30 | Information dynamics in complex systems Mikhail Prokopenko (The University of Sydney, Australia) |
Artificial life comes in “soft,” “hard,” and “wet” forms; “soft” artificial life involves creating and studying complex software systems such as self-reproducing and evolving computer programs; “hard” artificial life involves creating complex hardware systems such as autonomous robots; and “wet” artificial life involves creating complex bio-chemical systems such as artificial cells. The behavior of these soft, hard, and wet systems is produced by very complex causal webs, in which many nodes simultaneously respond in a non-linear way to multiple local inputs that are affected by multiple causal feedback loops. These complex causal systems have characteristic epistemic consequences, including the impossibility of predicting their exact future behavior even given complete knowledge of their initial and boundary conditions, and the necessity of synthesizing and simulating them to learn their robust behavior patterns. These epistemic consequences explain many of the central scientific challenges that artificial life must face, but they also create new scientific opportunities. One new opportunity is automated “robot scientists” that apply machine learning methods to big-data produced in high-throughput experimental procedures.
Bio: Prof. Mark A. Bedau (Professor of Philosophy and Humanities at Reed College; Adjunct Professor of Systems Science at Portland State University) has extensively published and lectured on philosophical and scientific issues concerning emergence, evolution, life, mind, and the social and ethical implications of new and emerging technologies. He combines training in analytical philosophy with over two decades of experience in artificial life, and he has co-authored or co-edited 7 books, including Emergence: Contemporary Readings in Philosophy and Science (MIT Press), Protocells: Bridging Nonliving and Living Matter (MIT Press), and The Ethics of Protocells: Moral and Social Implications of Creating Life in the Laboratory (MIT Press), Living technology: 5 questions (Automatic Press/VIP), and The nature of life: classical and contemporary perspectives from philosophy and science (Cambridge University Press). For the past fifteen years he has been Editor-in-Chief of the journal Artificial Life (published by MIT Press), and he was the founding President of the International Society for Artificial Life.
Artificial life uses a synthetic approach for understanding biology, but also takes inspiration form biology to build adaptive systems. Adaptivity is necessary to complement the limited predictability inherent in complex systems. One successful approach for engineering and controlling adaptive systems uses the concept of self-organization: instead of specifying the function of a system, we can design components so that the function of the system will be a product of the interactions of the components. This is useful when the desired function is unknown or changing constantly. Examples from urban mobility will be used to illustrate the potential of this “living technology”.
Bio: Carlos Gershenson is a tenured research professor at the Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas of the Universidad Nacional Autónoma de México, where he leads the Self-organizing Systems Lab. His research interests include complex systems, self-organization, urbanism, philosophy, and artificial life.
Many pressing societal issues involve managing complex adaptive systems, many of which have human or technological, as well as “natural”, components. Examples include industrial networks, cities, global food and energy systems, economies and societies. The complexity, adaptability and reflexivity of these systems make managing them difficult and efforts to control them fully are often doomed to failure. I will discuss “steering” methodologies, in which one attempts to uncover and exploit the structure and dynamics of a system effectively whilst remaining in interaction with it, ready to respond to change. I will cover the background and history of this philosophy in artificial life, complexity science and other fields, discussing the drivers of its development and key concepts. I will then detail the various approaches and tools that have been applied with this goal, from participatory modelling and network analysis to adaptive management and whole-systems design. This talk will cover the state of the art in the field, its strengths and challenges, case studies in specific applications such as industrial ecology and the possible future for steering complex systems.
Bio: Alexandra Penn is a Senior Research Fellow at the University of Surrey working on combining participatory methodologies and mathematical models to create tools for stakeholders to understand and “steer” their complex human ecosystems. She is a principal member of the new “Centre for Evaluating Complexity across the Nexus” a collaboration between academics, policy professionals and the UK government to generate novel, cutting-edge methods for evaluating policy for complex systems. With a background in Artificial Life, Evolutionary Theory, Physics and permaculture design, she has long-standing interests in bringing ideas from diverse domains to innovative applications. She was made a fellow of the Royal Society of Arts for her work in novel application of whole-systems design to bacterial communities and is Chair for Societal Impact of the International Society for Artificial Life.
Many evolutionary and self-organization pressures can be characterized information-theoretically not only because it’s an approximation useful in designing biologically- inspired systems, but also because numerous optimal structures evolve/self-organize in nature when information dynamics approach critical points. The talk will focus on information dynamics of computation within spatiotemporal systems in terms of three fundamental operations: information storage, transfer, and modification, quantifying these operations on a local scale in space and time. The methods will be exemplified in different contexts, including modular robotics, swarms, computational neuroscience, and random Boolean networks. In addition, we shall/may discuss a relation between Fisher information and phase transitions / order parameters, drawing from both thermodynamics and statistical estimation theory.
Bio: Prof. Mikhail Prokopenko leads the University of Sydney’s Centre for Complex Systems and its new postgraduate program in Complex Systems. Mikhail has a strong international reputation in complex self-organizing systems, with over 150 publications, patents, and edited books, including “Guided Self-Organization: Inception” (Springer, 2014). He received a PhD in Computer Science (2002, Australia), MA in Economics (1994, USA), and MSc in Applied Mathematics (1988, USSR). Over the last decade, Prof. Prokopenko has co-organized and co-chaired the series of International Workshops on Guided Self-organization (GSO); was a keynote speaker at The 2013 IEEE Symposium on Artificial Life; The 3rd International Workshop on Computation in Cyber-Physical Systems (Mexico, 2012), and other events. Currently, Mikhail is the Chief Editor for Computational Intelligence section of Frontiers Robotics and AI journal, having served in the past as an editor of special issues on Complex Networks (Artificial Life), and Guided Self-organization (HFSP; Theory in Biosciences; Advances in Complex Systems, Entropy), and a section editor for Encyclopedia of Machine Learning (Evolutionary Computation). He is also a senior member of IEEE and an Executive Committee member of the international RoboCup Federation. The general objective of Mikhail’s research is to analyze and model various critical dynamics, aiming to increase robustness and resilience of complex real-world systems, by identifying required interventions during technological, socio-ecological, and socio-economic crises. The approach is strongly motivated by the search for a fundamental theory of non-equilibrium information thermodynamics in systems capable of complex computation.
