Emergenta system C-kurs, 5 pong, HT-05 Jonny Pettersson - PDF document

Emergenta system C-kurs, 5 poäng, HT-05 Jonny Pettersson jonny@cs.umu.se 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 1 Course Description � The focus of the course includes the acquisition of: � knowledge about the concepts emergence, emergent behavior and emergent systems; � knowledge about how agent based techniques can be used as tools for modeling and simulation; and � knowledge of what applications of emergent systems can be used for and how to evaluate them. 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 2 Course Description (cont.) � Moment 1, teoridel, 3 poäng � Målet med kursen är att ge en förståelse för emergenta system. Emergenta system är system där systemets beteende uppstår som en emergent egenskap ur interaktionen mellan systemets delar. Emergenta egenskaper kan observeras i alla icke-linjära system som är tillräckligt komplexa, både naturliga och artificiella. Under kursen kommer bland annat fraktaler, kaos, komplexa system och adaptation att behandlas. Kursen utgör en grund för kursen Design av samverkande system. � Moment 2, laborationsdel, 2 poäng � Obligatoriska uppgifter 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 3 1

Last year � First time � An interesting course with good result � Average workload � Literature � Good and not so difficult to read � Assignments and project � Interesting 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 4 This Course � Text book + papers � Computer programs � Focus on � Fractals � Chaos � Complex Systems � Adaptation � Contents � Lectures � Guest lectures � Assignments � Project 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 5 Assignments and Project � Assignments � L-systems � NetLogo and termites • First (?) contact with NetLogo • Termites – a simple system with emergent behavior � Boids � Genetic Algorithms � 2 and 2 in the assignments � Project � 1 to 4 in the project 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 6 2

Teaching � Pedagogical thoughts � Slides � Source � Contents � Language � What should you learn? 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 7 The rest of today � Concepts � Emergence, emergent systems, … � Life � Real life � Artificial life � Topics 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 8 Emergence � Definitions � The whole is more then a sum of parts � The global behavior could not be predicted from lower levels � Something that emerges in the interactions between simple(?) parts and the environment, and that is not described in the parts � Emergent properties � Emergent systems � Example: Ants 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 9 3

Emergent Behavior � Bottom-up � Distributed � Local determination of behavior � On all levels � Example: The human body 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 10 Properties of Emergent Systems � Many interacting parts � Decentralized � Non-linear � Dynamic � Competition and cooperation � Emergent properties 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 11 Many interacting parts � Societies made of many people, people made of many organs, organs made of many cells � A system of parts because of interactions � Number of parts may differ � Massive parallelism � Often many simple parts doing the same thing � Complexity comes from interaction � Example: Weather 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 12 4

Decentralised � Self-organization � The order emerges from the system itself � Advantages of decentralization � Easier to adapt to changes � A system does not need to have a smart leader � Examples: � WWW � Peer-to-peer architectural models 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 13 Non-linear � Do not obey the superposition principle � Output is not proportional to input � Interactions between parts � Example: Phase transitions � Solid – liquid - gas 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 14 Dynamic � Often the interactions continues on and on � Does not always come to a ”fixed” state � Can be better to handle changing environments � Dynamic systems can have different amounts of complexity � Example: Society 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 15 5

Competition and Cooperation � Some agents may cooperate � Some agents may fight for the same resource � Some agents may destroy what others tries to do � Example: Producer-consumer systems 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 16 Discussion � Questions to consider: � What is the emergent property? � What is the goal of the system? � Does each agent know the goal? � How was the system created? � Emergent systems in the society � Emergent systems in the nature 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 17 Complex Systems � (Almost) like emergent systems � Many parts � Interdependent parts � Difficult to understand � The behavior of the whole system understood from behavior of the parts � The behavior of the parts depends of the behavior of the whole system � Example: Family 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 18 6

Adaptation � Can lead to improved fitness and performance, or just to be able to survive � Adaptation can happen in three ways � Improved handling of an event by the agent � Learning – in the lifetime of the agent � Evolution – across generations 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 19 Complex Adaptive Systems � An complex adaptive system is a system consisting of many interacting parts. The behavior of the system emerges out of the parallel interactions between the parts and the environment without any global plan. The parts adapt and evolve over time. � Example: Ecosystems 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 20 Life – What is Life? � Vitalism � Life is ”something” extra over and above the detailed organization of a material organism � Langton, 1988 � ”…living organisms are nothing more than complex biochemical machines. … A living organism … must be viewed as a large population of relatively simple machines.” � ”Life is a property of form, not matter” � Flake, 1998 � ”Nature appears to be a hierarchy of computational systems that are forever on the edge between computability and incomputability.” 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 21 7

Life - Biology � The Concise Oxford Dictionary, 1990 � Biology – The study of living organisms � Merriam-Webster´s Collegiate Dictionary � Biology - A branch of knowledge that deals with living organisms and vital processes � From Greek � bios – life � logus - discourse � Does it have to be the study of carbon- based life? 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 22 Life – Artificial Life � Langton, 1988 � ”Artificial Life is the study of man-made systems that exhibit behaviors characteristic of natural living systems.” � ”… Artificial Life can contribute to theoretical biology by locating life-as-we-know-it within the larger picture of life-as-it-could-be .” � ”The artificial in Artificial Life refers to the component parts, not the emergent processes.” 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 23 From Chaos to Life � Living organisms are nonlinear systems! � Living organisms are complex systems! � How has nature achieved this? 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 24 8

From Chaos to Life - Naturally � Evolution through natural selection � Darwin, The Origin of Species , November 24, 1859 � Genotype – phenotype � Criteria for evolution • Heredity • Variability • Fecundity � The role of environment � Co-evolution � A necessary condition? � Self-similarity 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 25 � Self-organization From Chaos to Life - Artificially � How to do this artificially? � Can not predict the global behavior of simple interacting subparts � Can not decide which subparts to use to get a predetermined global behavior � You must ”run” the system to see what kind of global behavior it generate � You need methods to search through the solution space of a nonlinear system 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 26 From Chaos to Life - Artificially � Methods in Artificial Life � Lindenmayer systems � Cellular Automata � Boids, herds and flocks � Ant Algorithms � Genetic Algorithms � And more… 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 27 9

Fractals � Lindenmayer systems � Consist of sets of rules for rewriting strings of symbols � “Random processes in nature are often self- similar on varying temporal and spatial scales” (Flake, 1998) � Example: Flake 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 28 Chaos – Dynamic Systems � Examples of dynamic systems � Computability - Incomputability � Fractals � Cellular Automata 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 29 Chaos - Characteristics � Deterministic � Not random � Sensitive � Extremely sensitive to initial conditions � Ergodic � A chaotic system will return to the ”same” place 1/11 - 05 Emergent Systems, Jonny Pettersson, UmU 30 10