Artificial Ecosystems for Creative Discovery Jon McCormack Centre for Electronic Media Art Monash University Clayton 3800, Australia www.csse.monash.edu.au/~jonmc Jon.McCormack@infotech.monash.edu.au 1
Computational Creative Discovery Evolutionary synthesis is creative, able to discover (for example) prokaryotes, eukaryotes, higher multi-cellularity and language through a non-teleological process of replication and selection. We would like to adapt the processes of evolutionary adaptation to problems of creative discovery (either machine initiated or human-machine). The aim is to structure these artificial ecosystems in such a way that they exhibit novel discovery in a creative context rather than a biological one. In the past, many EC algorithms have downplayed the role of environment and interactions between biotic and abiotic components. 2
Biomorphs Richard Dawkins, The Blind Watchmaker, 1986 3
Evolved Image Steven Rooke, 1998 Evolved Image Karl Sims, 1991 4
Interactive Genetic Algorithm human selection bottleneck genetic search is poorly balanced most discoveries depend on initial conditions method doesn t scale generative system user fitness selection phenotypes 5
Artificial Ecosystems A new kind of Evolutionary Computing algorithm, particularly for creative discovery No explicit fitness function: agents evolve to fit their environment Gives more consideration to the role of the environment and the interactions between biotic and abiotic components Macro properties emerge as a simulation outcome, from the interaction of (specified) micro components Knowledge is encoded in the environment (a different knowledge representation scheme) Environment acts as a kind of memory Heterogeneous environments Homeostasis, mutualism, symbiosis, parasitism... 6
Essential Properties and Processes The concept of genotype and phenotype produced through enaction of the genotype; Groups of individuals represent species, multiple species are possible; Spatial distribution and (optionally) movement of individuals; The ability of individuals to modify and change their environment (either directly or indirectly as a result of their development within, and interaction with, the environment); the concept of individual health as an abstract scalar measure of an individual's success in surviving within its environment over its lifetime; the concept of an individual life-cycle, in that an individual undergoes stages of development that may affect its properties, physical interaction and behaviour; the concept of an environment as a physical model with consistent physical rules on interaction and causality between the elements of the environment; An energy-metabolism resource model, which describes the process for converting energy into resources that may be utilised by species in the environment to perform actions (including the production of resources). 7
Example: Colourfield An ecosystem of colour, operating in a one-dimensional world R t = f(h t ) r i = w 2 i ( k 0 + k 1 log ( )) d Ci dt + k 2 dw i dt histogram resources added based on histogram distribution... individuals 1 2 3 4 5 6 7 8 9 10 11 12 13... n-3 n-2 n-1 n cells... resources H S L w l w r w s w g genome colour colour weights left, right, self growth weight age health R width H S V state individual 8
Example: Resource Maximisation 600 500 400 Total Available Resources 300 200 100 500 1000 1500 2000 2500 3000 Iterations 9
Colourfield Ecosystem development over 5000 time steps (warm colour bias function) t = 10 t = 500 t = 1000 t = 5000 10
Horizontal Stripe Painting Patrick Heron, 1957 58, 274.3 x 154.8 cm Tate Modern Collection, London UK 11
Example: Eden An ecosystem of sonic agents, operating in a two-dimensional world Rocks, Biomass, Agents Agents learn about and adapt to their environment, based on a modified version of Wilson s XCS Those agents that best fit the environment come to dominate the population The virtual and real worlds are connected: human interest drives resource production, giving rise to selection pressure based on maintaining human interest Agents use changing, interesting sound to maintain their food supply, hence ensure their survival 12
Eden architecture AGENT sunlight sound-related sound-related XCS learning system biomass agent sensors actuators human interest 13
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Causal Mechanisms: Colourfield and Eden f(h) energy source f(aa + Ab, day) energy source radiant energy chroma and intensity histogram (H) albedo (Aa) albedo (Ab) radiant energy growth entities hue, saturation, lightness animals death biomass death COLOURFIELD eat / death eat EDEN people 15
Buy the book! Impossible Nature: the art of Jon McCormack Jon McCormack, Jon Bird, Annemarie Jonson, Alan Dorin. Australian Centre for the Moving Image, 2004 www.acmi.net.au 16