Artificial Life
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The Effect of Compression and Expansion on Stochastic Reaction Networks
IMT School for Advanced Studies, Lucca Lucca, Italy The Effect of Compression and Expansion on Stochastic Reaction Networks PhD Program in Institutions, Markets and Technologies Curriculum in Computer Science and Systems Engineering (CSSE) XXXI Cycle By Tabea Waizmann 2021 The dissertation of Tabea Waizmann is approved. Program Coordinator: Prof. Rocco De Nicola, IMT Lucca Supervisor: Prof. Mirco Tribastone, IMT Lucca The dissertation of Tabea Waizmann has been reviewed by: Dr. Catia Trubiani, Gran Sasso Science Institute Prof. Michele Loreti, University of Camerino IMT School for Advanced Studies, Lucca 2021 To everyone who believed in me. Contents List of Figures ix List of Tables xi Acknowledgements xiii Vita and Publications xv Abstract xviii 1 Introduction 1 2 Background 6 2.1 Multisets . .6 2.2 Reaction Networks . .7 2.3 Ordinary Lumpability . 11 2.4 Layered Queuing Networks . 12 2.5 PEPA . 16 3 Coarse graining mass-action stochastic reaction networks by species equivalence 19 3.1 Species Equivalence . 20 3.1.1 Species equivalence as a generalization of Markov chain ordinary lumpability . 27 3.1.2 Characterization of SE for mass-action networks . 27 3.1.3 Computation of the maximal SE and reduced net- work . 28 vii 3.2 Applications . 31 3.2.1 Computational systems biology . 31 3.2.2 Epidemic processes in networks . 33 3.3 Discussion . 36 4 DiffLQN: Differential Equation Analysis of Layered Queuing Networks 37 4.1 DiffLQN .............................. 38 4.1.1 Architecture . 38 4.1.2 Capabilities . 38 4.1.3 Syntax . 39 4.2 Case Study: Client-Server dynamics . 41 4.3 Discussion . -
Close Engagements with Artificial Companions: Key Social, Psychological, Ethical and Design Issues
OII / e-Horizons Forum Discussion Paper No. 14, January 2008 Close Engagements with Artificial Companions: Key Social, Psychological, Ethical and Design Issues by Malcolm Peltu Oxford Internet Institute Yorick Wilks Oxford Internet Institute OII / e-Horizons Forum Discussion Paper No. 14 Oxford Internet Institute University of Oxford 1 St Giles, Oxford OX1 3JS United Kingdom Forum Discussion Paper January 2008 © University of Oxford for the Oxford Internet Institute 2008 Close Engagements with Artificial Companions Foreword This paper summarizes discussions at the multidisciplinary forum1 held at the University of Oxford on 26 October 2007 entitled Artificial Companions in Society: Perspectives on the Present and Future, as well as an open meeting the previous day addressed by Sherry Turkle2. The event was organized by Yorick Wilks, Senior Research Fellow for the Oxford Internet Institute (OII)3, on behalf of the e-Horizons Institute4 and in association with the EU Integrated Project COMPANIONS. COMPANIONS is studying conversational software-based artificial agents that will get to know their owners over a substantial period. These could be developed to advise, comfort and carry out a wide range of functions to support diverse personal and social needs, such as to be ‘artificial companions’ for the elderly, helping their owners to learn, or assisting to sustain their owners’ fitness and health. The invited forum participants, including computer and social scientists, also discussed a range of related developments that use advanced artificial intelligence and human– computer interaction approaches. They examined key issues in building artificial companions, emphasizing their social, personal, emotional and ethical implications. This paper summarizes the main issues raised. -
Open Problems in Artificial Life
Open Problems in Artificial Life Mark A. Bedau,† John S. McCaskill‡ Norman H. Packard§ Steen Rasmussen Chris Adami†† David G. Green‡‡ Takashi Ikegami§§ Abstract This article lists fourteen open problems in Kunihiko Kaneko artificial life, each of which is a grand challenge requiring a Thomas S. Ray††† major advance on a fundamental issue for its solution. Each problem is briefly explained, and, where deemed helpful, some promising paths to its solution are indicated. Keywords artificial life, evolution, self-organi- zation, emergence, self-replication, autopoeisis, digital life, artificial chemistry, selection, evolutionary learning, ecosystems, biosystems, astrobiology, evolvable hardware, dynamical hierarchies, origin of life, simulation, cultural evolution, 1 Introduction information theory At the dawn of the last century, Hilbert proposed a set of open mathematical problems. They proved to be an extraordinarily effective guideline for mathematical research in the following century. Based on a substantial body of existing mathematical theory, the challenges were both precisely formulated and positioned so that a significant body of missing theory needed to be developed to achieve their solution, thereby enriching mathematics as a whole. In contrast with mathematics, artificial life is quite young and essentially interdis- ciplinary. The phrase “artificial life” was coined by C. Langton [11], who envisaged an investigation of life as it is in the context of life as it could be. Although artificial life is fundamentally directed towards both the origins of biology and its future, the scope and complexity of its subject require interdisciplinary cooperation and collabo- ration. This broadly based area of study embraces the possibility of discovering lifelike behavior in unfamiliar settings and creating new and unfamiliar forms of life, and its major aim is to develop a coherent theory of life in all its manifestations, rather than an historically contingent documentation bifurcated by discipline. -
Open-Endedness for the Sake of Open-Endedness
Open-Endedness for the Sake Arend Hintze Michigan State University of Open-Endedness Department of Integrative Biology Department of Computer Science and Engineering BEACON Center for the Study of Evolution in Action [email protected] Abstract Natural evolution keeps inventing new complex and intricate forms and behaviors. Digital evolution and genetic algorithms fail to create the same kind of complexity, not just because we still lack the computational resources to rival nature, but Keywords because (it has been argued) we have not understood in principle how to create open-ended evolving systems. Much effort has been Open-ended evolution, computational model, complexity, diversity made to define such open-endedness so as to create forms of increasing complexity indefinitely. Here, however, a simple evolving computational system that satisfies all such requirements is presented. Doing so reveals a shortcoming in the definitions for open-ended evolution. The goal to create models that rival biological complexity remains. This work suggests that our current definitions allow for even simple models to pass as open-ended, and that our definitions of complexity and diversity are more important for the quest of open-ended evolution than the fact that something runs indefinitely. 1 Introduction Open-ended evolution has been identified as a key challenge in artificial life research [4]. Specifically, it has been acknowledged that there is a difference between an open-ended system and a system that just has a long run time. Large or slow systems will eventually converge on a solution, but open- ended systems will not and instead keep evolving. Interestingly, a system that oscillates or that is in a dynamic equilibrium [21] would continuously evolve, but does not count as an open-ended evolving system. -
Type Checking Physical Frames of Reference for Robotic Systems
PhysFrame: Type Checking Physical Frames of Reference for Robotic Systems Sayali Kate Michael Chinn Hongjun Choi Purdue University University of Virginia Purdue University USA USA USA [email protected] [email protected] [email protected] Xiangyu Zhang Sebastian Elbaum Purdue University University of Virginia USA USA [email protected] [email protected] ABSTRACT Engineering (ESEC/FSE ’21), August 23–27, 2021, Athens, Greece. ACM, New A robotic system continuously measures its own motions and the ex- York, NY, USA, 16 pages. https://doi.org/10.1145/3468264.3468608 ternal world during operation. Such measurements are with respect to some frame of reference, i.e., a coordinate system. A nontrivial 1 INTRODUCTION robotic system has a large number of different frames and data Robotic systems have rapidly growing applications in our daily life, have to be translated back-and-forth from a frame to another. The enabled by the advances in many areas such as AI. Engineering onus is on the developers to get such translation right. However, such systems becomes increasingly important. Due to the unique this is very challenging and error-prone, evidenced by the large characteristics of such systems, e.g., the need of modeling the phys- number of questions and issues related to frame uses on developers’ ical world and satisfying the real time and resource constraints, forum. Since any state variable can be associated with some frame, robotic system engineering poses new challenges to developers. reference frames can be naturally modeled as variable types. We One of the prominent challenges is to properly use physical frames hence develop a novel type system that can automatically infer of reference. -
Dynamic Extension of Typed Functional Languages
Dynamic Extension of Typed Functional Languages Don Stewart PhD Dissertation School of Computer Science and Engineering University of New South Wales 2010 Supervisor: Assoc. Prof. Manuel M. T. Chakravarty Co-supervisor: Dr. Gabriele Keller Abstract We present a solution to the problem of dynamic extension in statically typed functional languages with type erasure. The presented solution re- tains the benefits of static checking, including type safety, aggressive op- timizations, and native code compilation of components, while allowing extensibility of programs at runtime. Our approach is based on a framework for dynamic extension in a stat- ically typed setting, combining dynamic linking, runtime type checking, first class modules and code hot swapping. We show that this framework is sufficient to allow a broad class of dynamic extension capabilities in any statically typed functional language with type erasure semantics. Uniquely, we employ the full compile-time type system to perform run- time type checking of dynamic components, and emphasize the use of na- tive code extension to ensure that the performance benefits of static typing are retained in a dynamic environment. We also develop the concept of fully dynamic software architectures, where the static core is minimal and all code is hot swappable. Benefits of the approach include hot swappable code and sophisticated application extension via embedded domain specific languages. We instantiate the concepts of the framework via a full implementation in the Haskell programming language: providing rich mechanisms for dy- namic linking, loading, hot swapping, and runtime type checking in Haskell for the first time. We demonstrate the feasibility of this architecture through a number of novel applications: an extensible text editor; a plugin-based network chat bot; a simulator for polymer chemistry; and xmonad, an ex- tensible window manager. -
Artificial Intelligence: How Does It Work, Why Does It Matter, and What Can We Do About It?
Artificial intelligence: How does it work, why does it matter, and what can we do about it? STUDY Panel for the Future of Science and Technology EPRS | European Parliamentary Research Service Author: Philip Boucher Scientific Foresight Unit (STOA) PE 641.547 – June 2020 EN Artificial intelligence: How does it work, why does it matter, and what can we do about it? Artificial intelligence (AI) is probably the defining technology of the last decade, and perhaps also the next. The aim of this study is to support meaningful reflection and productive debate about AI by providing accessible information about the full range of current and speculative techniques and their associated impacts, and setting out a wide range of regulatory, technological and societal measures that could be mobilised in response. AUTHOR Philip Boucher, Scientific Foresight Unit (STOA), This study has been drawn up by the Scientific Foresight Unit (STOA), within the Directorate-General for Parliamentary Research Services (EPRS) of the Secretariat of the European Parliament. To contact the publisher, please e-mail [email protected] LINGUISTIC VERSION Original: EN Manuscript completed in June 2020. DISCLAIMER AND COPYRIGHT This document is prepared for, and addressed to, the Members and staff of the European Parliament as background material to assist them in their parliamentary work. The content of the document is the sole responsibility of its author(s) and any opinions expressed herein should not be taken to represent an official position of the Parliament. Reproduction and translation for non-commercial purposes are authorised, provided the source is acknowledged and the European Parliament is given prior notice and sent a copy. -
How Artificial Intelligence Works
BRIEFING How artificial intelligence works From the earliest days of artificial intelligence (AI), its definition focused on how its results appear to show intelligence, rather than the methods that are used to achieve it. Since then, AI has become an umbrella term which can refer to a wide range of methods, both current and speculative. It applies equally well to tools that help doctors to identify cancer as it does to self-replicating robots that could enslave humanity centuries from now. Since AI is simultaneously represented as high-risk, low-risk and everything in between, it is unsurprising that it attracts controversy. This briefing provides accessible introductions to some of the key techniques that come under the AI banner, grouped into three sections to give a sense the chronology of its development. The first describes early techniques, described as 'symbolic AI' while the second focuses on the 'data driven' approaches that currently dominate, and the third looks towards possible future developments. By explaining what is 'deep' about deep learning and showing that AI is more maths than magic, the briefing aims to equip the reader with the understanding they need to engage in clear-headed reflection about AI's opportunities and challenges, a topic that is discussed in the companion briefing, Why artificial intelligence matters. First wave: Symbolic artificial intelligence Expert systems In these systems, a human expert creates precise rules that a computer can follow, step by step, to decide how to respond to a given situation. The rules are often expressed in an 'if-then-else' format. Symbolic AI can be said to 'keep the human in the loop' because the decision-making process is closely aligned to how human experts make decisions. -
Biological Computation: a Road
918 Biological Computation: A Road to Complex Engineered Systems Nelson Alfonso Gómez-Cruz Modeling and Simulation Laboratory Universidad del Rosario Bogotá, Colombia [email protected] Carlos Eduardo Maldonado Research Professor Universidad del Rosario Bogotá, Colombia [email protected] Provided that there is no theoretical frame for complex engineered systems (CES) as yet, this paper claims that bio-inspired engineering can help provide such a frame. Within CES bio- inspired systems play a key role. The disclosure from bio-inspired systems and biological computation has not been sufficiently worked out, however. Biological computation is to be taken as the processing of information by living systems that is carried out in polynomial time, i.e., efficiently; such processing however is grasped by current science and research as an intractable problem (for instance, the protein folding problem). A remark is needed here: P versus NP problems should be well defined and delimited but biological computation problems are not. The shift from conventional engineering to bio-inspired engineering needs bring the subject (or problem) of computability to a new level. Within the frame of computation, so far, the prevailing paradigm is still the Turing-Church thesis. In other words, conventional 919 engineering is still ruled by the Church-Turing thesis (CTt). However, CES is ruled by CTt, too. Contrarily to the above, we shall argue here that biological computation demands a more careful thinking that leads us towards hypercomputation. Bio-inspired engineering and CES thereafter, must turn its regard toward biological computation. Thus, biological computation can and should be taken as the ground for engineering complex non-linear systems. -
What Was Life? Answers from Three Limit Biologies Author(S): Stefan Helmreich Source: Critical Inquiry, Vol
What Was Life? Answers from Three Limit Biologies Author(s): Stefan Helmreich Source: Critical Inquiry, Vol. 37, No. 4 (Summer 2011), pp. 671-696 Published by: The University of Chicago Press Stable URL: http://www.jstor.org/stable/10.1086/660987 . Accessed: 25/08/2011 13:15 Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. The University of Chicago Press is collaborating with JSTOR to digitize, preserve and extend access to Critical Inquiry. http://www.jstor.org What Was Life? Answers from Three Limit Biologies Stefan Helmreich “What was life? No one knew.” —THOMAS MANN, The Magic Mountain What is life? A gathering consensus in anthropology, science studies, and philosophy of biology suggests that the theoretical object of biology, “life,” is today in transformation, if not dissolution. Proliferating repro- ductive technologies, along with genomic reshufflings of biomatter in such practices as cloning, have unwound the facts of life.1 Biotechnology, bio- This paper grew from a presentation at “Vitalism Revisited: History, Philosophy, Biology,” at the Center for Interdisciplinary Studies in Science and Cultural Theory, Duke University, 22 Mar. 2008. I thank Barbara Herrnstein Smith for inviting me. The paper went through revision for “Extreme: Histories and Economies of Humanness Inside Outerspaces,” at the American Anthropological Association meeting, 2–6 Dec. -
Artificial Intelligence in Computer Graphics: a Constructionist Approach
Artificial Intelligence in Computer Graphics: A Constructionist Approach Kristinn R.Thórisson,Christopher ology aims to help coordinate the effort. approach to building AI systems. The Pennock,Thor List,John DiPirro There is also a lack of incremental accumula- Constructionist AI Methodology (CAIM) is an Communicative Machines tion of knowledge in AI and related computer emerging set of principles for designing and graphics. By supporting reuse of prior work implementing interactive intelligences, Introduction we enable the building of increasingly speeding up implementation of relatively The ultimate embodiment of artificial intelli- powerful systems, as core system elements complex, multi-functional systems with full, gence (AI) has traditionally been seen as do not need to be built from scratch. real-time perception-action loop capabilities. physical robots. However, hardware is expen- Background and Motivation It helps novices as well as experts with the sive to construct and difficult to modify. The The creation of believable, interactive charac- creation of embodied, virtual agents that can connection between graphics and AI is ters is a challenge familiar to many game perceive and act in real time and interact with becoming increasingly strong, and, on the one developers. Systems that have to respond to virtual as well as real environments. The hand, it is now clear that AI research can asynchronous inputs in real time, where the embodiment of such humanoids can come in benefit tremendously from embodiment in inputs’ timing is largely unpredictable, can various levels of completeness, from just a virtual worlds [1, 4, 5]. On the other hand, pose a serious challenge to the system design. -
Art and Artificial Life – a Primer
Art and Artificial Life – a Primer Simon Penny University of California, Irvine [email protected] ABSTRACT December 2009) which is a testament to the enduring and It was not until the late 1980s that the term ‘Artificial Life’ arose inspirational intellectual significance of ideas associated with as a descriptor of a range of (mostly) computer based research Artificial Life. practices which sought alternatives to conventional Artificial Intelligence methods as a source of (quasi-) intelligent behavior in technological systems and artifacts. These practices included Artificial Life could not have emerged as a persuasive paradigm reactive and bottom-up robotics, computational systems which without the easy availability of computation. This is not simply to simulated evolutionary and genetic processes, and are range of proclaim, as did Christopher Langton, that Artificial Life was an other activities informed by biology and complexity theory. A exploration of life on a non-carbon substrate, but that Artificial general desire was to capture, harness or simulate the generative Life is ‘native’ to computing in the sense that large scale iterative and ‘emergent’ qualities of ‘nature’ - of evolution, co-evolution process is crucial to the procedures which generate (most) and adaptation. ‘Emergence’ was a keyword in the discourse. Two artificial life phenomena. The notion that Artificial Life is life decades later, the discourses of Artificial Life continues to have created an ethico-philosophical firestorm concerning intelligence, intellectual force,