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Bedau et al. (2001): Open Problems in Artificial Life
This paper was my introduction to the emerging field of artificial life. Here are some crucial quotes from that paper:
In contrast with mathematics, artificial life is quite young and essentially interdisciplinary. The phrase “artificial life” was coined by C. Langton (1986), who envisaged an investigation of life as it is in the context of life as it could be. [...] 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. [...] Artificial life is foremost a scientific rather than an engineering endeavor. Given how ignorant we still are about the emergence and evolution of living systems, artificial life should emphasize understanding first and applications second, so the challenges we list below focus on the former.
The challenges that sound most intriguing to me are:
Achieve the transition to life in an artificial chemistry in silico.
Artificial chemistries are computer-based model systems composed of objects (abstractions of molecules), which are generated by collision between existing objects according to a predefined interaction law. [...][...] Bimolecular chemistry is assumed to be sufficient to display the transition to life, but this may involve complex structures. The chemistry may be stochastic rather than deterministic, but should be constructive rather than descriptive; that is, an interaction law should predict (like an algorithm) the product molecules for colliding objects of arbitrary complexity. [...] Artificial chemistries have been investigated by many authors in spaces of various dimensionalities, with deterministic and probabilistic interaction laws. Molecules have been abstracted using cellular automata, secondary structure folding algorithms, finite state automata, Turing machines, von Neumann machines, and the lambda calculus.
Simulate a unicellular organism over its entire lifecycle.
The artificial organism should exhibit virtually its complete spectrum of behavior, including its ability to evolve. [...] The integration of the simulation of many thousands of proteins, and genetic as well as regulatory networks, at the level of deterministic kinetics would already provide important novel quantitative understanding of cell cycle dynamics. However, for moderate completeness, simulating the folding of all biopolymers and their reactions and supramolecular interactions is still a formidable challenge, since current successes in folding are statistical rather than ab initio, and vast progress in integrating molecular dynamics on time scales of minutes to hours is needed. [...] [C]ombinations of (for example) reaction kinetics, molecular dynamics simulations, and lattice gas simulations would be more powerful than any single simulation approach.
Determine what is inevitable in the open-ended evolution of life.
In different historical unfoldings of the evolutionary process and in evolution in other media, two related questions arise: (a) What are the features common to all evolutionary processes, or to broad classes of evolutionary processes? (b) Do different evolutionary processes contain fundamentally different evolutionary potential?
Determine the predictability of evolutionary consequences of manipulating organisms and ecosystems.
The ecosystems of interest include those as different as the entire global biosphere and individual human immune systems, and ecological manipulations range from industrial pollution, climate change, and large-scale mono-crop agriculture to the introduction of genetically engineered organisms. [...] How far can one rationally redesign or rapidly select organisms to fulfill multiple novel criteria without disturbing the viability of the organisms’ organization and defense systems? Is there a tradeoff between utility and viability, or between size of modification and duration of organism utilization? [...] With increasing understanding of the genetic control of development, it will be possible to create novel multicellular organisms through sequential genetic reprogramming. Do we need long-term evolutionary optimization to support or perfect such major changes to organisms?
Develop a theory of information processing, information flow, and information generation for evolving systems.
Firstly, there appear to be two complementary kinds of information transmission in living systems. One is the conservative hereditary transmission of information through evolutionary time. The other is transmission of information specified in a system’s physical environment to components of the system, possibly mediated by the components themselves, with the concomitant possibility of a combination of information processing and transmission. The latter is clearly also linked with the generation of information[.] [...] Secondly, the challenge is to unify evolution with information processing. One starting point is the observation that components of evolving systems (organisms or groups of organisms) seem to solve problems as part of their existence. More generally, theory must address what the capacity of an evolving system’s information processing is, and how it changes with evolution. Are there thresholds between levels of information processing during evolution that match the levels identified in automata theory—for example, from finite state machines to universal computation? How do the algorithms employed by organisms classify in terms of their problem solving efficiency? The third and least-understood role of information is its generation during evolution. As evolution takes place, evolving systems seem to become more complex; successfully quantifying complexity and its increase during evolution is one important part of understanding information generation. Another problem in this area is that of understanding how complexity in an evolving system’s environment can affect the complexity of the organisms that are evolving within the environment.
Demonstrate the emergence of intelligence and mind in an artificial living system.
Two deep issues in this area arise for artificial life. The first is substantive: whether and, if so, how the natures of life and mind are intrinsically connected. The second is methodological: whether it is most profitable to study mind and intelligence only when embodied in living systems. Both issues motivate artificial life’s existing attention to autonomous agents and embodied cognition, and they bear on artificial life’s relation to its elder sister, artificial intelligence. Progress on this challenge will shed new light on many current controversies in both fields, such as the extent to which life and mind should be viewed as “computational.” A constructive approach to all these concerns is to try to demonstrate the emergence of intelligence and mind in an artificial living system.
I am looking for people with similar interests, and perhaps even experience in these fields, in order to discuss the state of this emerging branch of science and possibly cooperate on projects.
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