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The Ulam programming language for artificial life
Ackley D., Ackley E. Artificial Life22 (4):431-450,2016.Type:Article
Date Reviewed: Nov 17 2017

The most amazing information processing imaginable can be seen in biology: a fertilized egg grows into an adult, a seed to a tree. Biological information processing can be thermodynamic (1010b/sec to 1015b/sec per gram 1010 cells) in magnitude [1]. Cells, as computers, know the states of their neighbors; global data structures exist only for outside observers. Even a global bit to synchronize cell activity would be unnatural. The problem is to understand the computing behind morphogenesis in a network of 10large cells.

Historically, there are the neural networks of McCulloch and Pitts [2] and cellular automata of von Neumann [3]. But unnatural assumptions (for example, synchronization, global knowledge, regularity, and so on) invalidate these models. Turing, thinking of epidermal tissue (a network of 1010 cells/kg), proposed reaction-diffusion mechanisms as a means of generating the global pattern of leopards’ spots [4]. Sixty years later, Tompkins [5] confirmed Turing’s ideas in wet-lab experiments, and I demonstrated traditional cell programs for spots [6]. The need for a modern framework is described by Stark and Hughes [7].

That framework is Abelson’s amorphous processes [8]. Amorphous models have asynchronous cell activity in a random network of cells--features that until recently seemed mathematically intractable. Communication involves reading neighboring cell values so there are no buffer problems.

In this paper, David Ackley describes Ulam, a procedural language for programming cells in a premodern network. Neither the language nor the model is precisely defined, so the paper is a status report.

Each cell has 40 neighbors whose values determine its input. Adjacent cells may not be active simultaneously. Cell programming in Ulam (an extension of C++) is concerned with race conditions, pointers, typed variables, and so on, all of which detract from essential concerns. Examples of programs and computations are given, but they read like work done 50 years ago. The implied model is unnatural and does not fit into any modern line of research.

Reviewer:  W. Richard Stark Review #: CR145661 (1802-0084)
1) Leff, H. S.; Rex, A. F. Maxwell’s demon: entropy, information, computing. Princeton University Press, Princeton, NJ, 1990.
2) McCulloch, W. S.; Pitts, W. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5, 4(1943), 115–133.
3) von Neumann, J. The general and logical theory of automata. Pergamon Press, Oxford, UK, 1948.
4) Turing, A. The chemical basis of morphogenesis. Philosophical Transactions of the Royal Society of London B 237, 641(1952), 37–72.
5) Tompkins, N.; Li, N.; Girabawea, C.; Heymann, M.; Ermentrout, G. B.; Epstein, I. R.; Fraden, S. Testing Turing’s theory of morphogenesis in chemical cells. PNAS 111, 12(2014), 4397–4402.
6) Stark, W. R. Amorphous computing: examples, mathematics and theory. Natural Computing 12, 3(2013), 377–392.
7) Stark, W. R.; Hughes, W. H. Asynchronous, irregular automata nets: the path not taken. Biosystems 55 (2000), 107–117.
8) Abelson, H.; Allen, D.; Coore, D.; Hanson, C.; Homsy, G.; Knight, T. F., Jr.; Nagpal, R.; Rauch, E.; Sussman, G. J. Amorphous computing. Communications of the ACM 43, 5(2000), 74–82.
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