Design principles for adaptive self-organizing systems

1. Design principles for adaptive self-organizing systems Finding Fluid Form Symposium University of Brighton December 9-10, 2005 Peter Cariani www.cariani.com Department of Physiology Tufts Medical School Boston

2. My trajectory Organismic biology (undergrad @ MIT mid 1970s)Biological cybernetics & epistemology (1980s) Biological alternatives to symbolic AIHoward Pattee, Systems Science, SUNY-BinghamtonTemporal coding of pitch & timbre (1990s)Auditory neurophysiology, neurocomputation How is information represented in brains? Commonalities of codingacross modality & phylaNeural timing nets for temporal processingAuditory scene analysis Possibilities inherent in time codes Temporal alternatives to connectionismsignal multiplexing; adaptive signal creation broadcast

3. Evolution of ideas Elaboration of structures & functions over time in biological, social, and technological realms, What makes new functions possible (functional emergence)?Can we put these principles to work for us?Is structural complexification by itself sufficient? (No)Notions of function & functional emergence are needed.What kinds of functions? Sensing, effecting, coordinatingIs pure computation on symbols sufficient? (No)How are brains/minds capable of open-ended creativity?Neural codes, temporal codes, timing nets Neural coding of pitch in the auditory systemRethinking the architecture of the brain: Temporal alternatives to connectionism Adaptive signal creation & multiplexing, Broadcast coordinative strategies

4. Combinatoric vs. creative emergence

5. An example Exhaustive description Limited description All permutations of single digits 0 1 2 3 4 5 6 7 8 9 consisting of 6 tokens All permutations of 6 arbitrarily defined objects One well-defined set having 610 permutations BOUNDED Ill-defined number of sets, each w. 610 permutations UNBOUNDED

6. Describing the world: Two perspectives Omniscent “God’s eye view” Postulational, ontological analytical mode Perspective of the limited observer epistemological empirical mode Appearance of new structures over time Violations of expectations “Surprise”

7. Well-defined vs. ill-defined realms Exhaustive description God’s eye view Limited description Limited observer System-environment as well-defined realm Environment as ill-defined realm Description is dependent on set of observables (environment has as many properties as one can measure) Description of all-possible organism-environment relations CLOSED WORLD ASSUMPTION OPEN WORLD ASSUMPTION No fundamental novelty is possible All novelty is combinatoric Combinatoric and Creative emergence

8. New features CREATING A NEW OBSERVABLE ADDS A NEW PRIMITIVE THAT INCREASES THE EFFECTIVE DIMENSIONALITY OF THE SYSTEM

9. Philosophy Ontology Aristotelian hylomorphism Material substrate that exists independently of us, yet whose form is largely ill-defined, incompletely known Organization is embedded in material system (e.g. mind is the organization of the nervous system)Conscious awareness requires a particular kind of regenerative informational organization embedded in a material system (cybernetic functionalism)Aristotle's Causes: Multiple complementary modes of explanation that answer different kinds of questions

11. Philosophy Epistemology Pragmatism (truth of a model related to its purpose) Perspective of the limited observer Relativism: different observational frames & purposes Analytical, empirical and pragmatic truthsAnalytic: truths of convention (non-material truths, finist mathematics)Empirical: truths of measurement, observation (science) Pragmatic: truths of efficacy & aesthetics (engineering, art)Constructivism & epistemic autonomy: by semi-freely choosing our own observables & concepts, we construct ourselves (for better or worse)

12. Design principles for adaptive, self-organizing systems We are interested in designing & fabricating systems that autonomously organize themselvesto elaborate structures & improve functions in response to challenges of their environments in ways that are meaningful and useful to us and/or them

13. Design principles for adaptive, self-organizing systems Richness of material possibility (e.g. polymeric combinatorics)+ Ability to steer & stabilize structure (feedback to structure: sensors, coordination mechanisms, effectors)+ Means to interact w. material world(sensing, action = "situatedness", semantics)+ Means to evaluate actions re: purposes (goal-laden representations, "intentionality")----------------------------------------------------------------------------------------=> Material system capable of adaptive, elaboration & improvement of informational functions

14. Design principles for adaptive, self-organizing systems Richness of material possibility (need polymers, replicated aperiodic structure, Schrodinger's aperiodic crystal, analog dynamics, ill-defined interactions)Ability to steer & stabilize structure(need controls on self-production of internal structure, enzymes)Means to interact w. material world(Need sensors, effectors, neural nets)Means to evaluate actions re: purposes(Need natural selection or internal goal states, limbic system)

15. Vibratory dynamics of matter Cymatics: Bringing Matter to Life with Sound Hans Jenny Richness of material possibility Complexity is easy Steerable complexity is hard

16. Design principles for adaptive, self-organizing systems VARIATION + SELECTION + INHERITANCE => ADAPTATION Material possibility+ Steer, stabilize, specify, inherit+ Sensorimotor interaction + Evaluation => ASOS Two phases in creative learning processes Expansive phase: generation of possibility Realm of free & open creation e.g. scientific imagination and hypothesis creation Contractive phase: selection of best possibilities Realm of clarity & rigorous evaluation e.g. hypothesis testing (clarity, removal of ambiguity)

17. Analog dynamics and discrete symbols We will also argue that one almost inevitably needs mixed analog-digital systems for complex systems: i.e. systems w. analog dynamics constrained by digital states ("symbols") for reliable replication of function for inheritability of adaptive improvements Analog and digital are complementary modes of description analog descriptions - continuous differential equations digital descriptions - discrete states & ST rules/probabilities Digital states or discrete symbols are ultrastable basins of attraction

18. Different theoretical approaches tounderstanding brains and their functions Dynamical systems approaches Neural information processing Symbol- processing differential growth homeostasis analog representations processing states & switches branching discrete

19. action interaction w. environment perception metabolism: self-production steering: percept-action coordinations Requisite: sensorimotor loopsInner and outer loops

20. Von Uexküll’s umwelts

21. McCulloch’sinternal andexternal loops

23. Self-conscious description of the modeling process:Hertzian modeling relation: measurement & computation

24. The choice of observables Finding the variables The would-be model maker is now in the extremely common situation of facing some incompletely defined "system," that he proposes to study through a study of "its variables." Then comes the problem: of the infinity of variables available in this universe, which subset shall he take? What methods can he use for selecting them? W. Ross Ashby, "Analysis of the system to be modeled" in: The Process of Model-Building in the Behavioral Sciences, Ohio State Press, pp. 94-114; reprinted in Conant, ed. Mechanisms of Intelligence

25. The choice of observables - analogous problems Choice of primitive features for classifiers Evolution of sensory organs in organisms Choice of sensors for robots

26. Semioticsof adaptivedevices Feedback to state Feedback to structure alters functionalities

27. Semiotic relations (Charles Morris) MEANING PURPOSE

28. Evaluate re: goals Frontal & limbic systems Internally generated pattern sequences sensory systems motor systems

29. Adaptivity in percept-action loops (Cariani)

30. Pure computation (state-determined system, no independent informational transactions w. environment)

31. Fixed robotic device Fixed sensors, coordinators, and effectors; Purely reactive and driven by its inputs; Incapable of learning

32. Computationallyadaptivedevice Trainable machines Neural networks Adaptive classifiers Genetic algorithms Robots w. adaptive programs Capable of learning new percept-action mappings (classifications)

33. Some observations about adaptability Whatever functionalities are fixed, the designer must specify works for well-defined problems & solutions advantage: predictable, reliable behavior drawback: problems of specification Whatever is made adaptive must undergo a learning phase needed for ill-defined problems & solutions some unpredictability of solutions found creative behavior! the more autonomy, the more potentially creative Consequently, there are tradeoffs between adaptability & efficiency autonomy/creativity & control/predictability

34. Evolution/adaptive construction of new sensors sensory evolution immune systems perceptual learning capable of learning new perceptual categories new feature primitives (new observables)

35. Epistemic autonomy • When a system can choose its own categories – through which it perceives and acts on the world – that system achieves some limited degree of epistemic autonomy. • A rudimentary electrochemical device was built by cyberneticist Gordon Pask in 1958 that grew its own sensors to create its own “relevance criteria.”

36. "With this ability to make or select proper filters on its inputs, such a device explains the central problem of epistemology. The riddles of stimulus equivalence or of local circuit action in the brain remain only as parochial problems." . Warren McCulloch, preface ,Gordon Pask (1961) . An Approach to Cybernetics.

37. Principles of action/use 1. Front-ends for trainable classifiers Useful in ill-defined situations where one does not a priori know what features are adequate to effect a classification 2. Adaptive, self-organizing sensors Grow structures over analog-VLSI electrode arrays in order to sense new aspects of the world. Use biochemical and/or biological systems coupled to an electrode array 3. Materially-based generator of new behaviors (adaptive pattern-generators) Similar steerable, ill-defined systems could be used to generate new patterns (sound, images) in an open-ended way that is not at all obvious to the observer/controller 4. Epistemic autonomy Device chooses how it will be connected to the outside world; what aspects of the material world (categories) are relevant to it. (Symbol grounding, frame problem)

38. Feedback to state vs. feedback to structure A thermostat is limited in the information that it can gain from its environment by the fixed nature of its sensors. It has feedback to state, but not feedback to structure. The amount of information that such a system can extract from its environment is finite at any time, and bounded by its fixed structure. A system capable of sensory evolution or perceptual learning has the ability to change its relation to its environs. Such a system has an open-ended set of observational primitives. It has both feedback to state and feedback to structure. The amount of information that such a system can extract from its environment is finite at any time, but unbounded. Such a system is open-ended.

39. Analog dynamics without inheritable constraint (Hans Jenny)

40. von Neumann's kinematic (robotic) self-reproducing automaton (1948)

41. Inheritable constructionanalog dynamics constrained & selected by discrete symbols Purely analog adaptive system must be trained each generation Genetic algorithm + Pattern grammar for guiding construction constrained search Symbolically-encoded memory permits results of an optimization process to be passed to subsequent generations

42. The homeostat

44. Relation to Ashby's homeostat Analog sensor/controller Uniselector 25x25x25x25 = 390k construction possibilities -> variety of the control system, unconstrained search Evaluation of ability to control inputs

45. Relation to Ashby's homeostat Analog sensor/controller Uniselector 25x25x25x25 = 390k construction possibilities -> variety of the control system, unconstrained search Evaluation of ability to control inputs

46. Uniselector evaluate (in bounds?) Analog controller (ill-defined structure) Adaptive analog controller Structure of particular controllers is unknown to designer Requisite variety for control is the number of alternative controllers available 25x25x25 = 390,625 Ashby's homeostat Environment

47. The homeostat & the brain A few cybernetics-inspired accounts of brain function Sommerhoff (1974) Logic of the Living Brain Klopf, The Selfish Neuron Arbib, The Metaphorical Brain Most successful neuroscientific application of cybernetics: W.Reichardt's analysis of fly optomotor loop The homeostat never caught on as a brain metaphor Some possible reasons: Homeostats never were cast in terms of neural nets No obvious digital uniselector function in the brain Predominance of problems of pattern recognition and formulation of coherent action over simple problems of internal regulation

48. The brain as an adaptive self-organizing system Ideas that flow from cybernetics and theoretical biology: Brains as signal self-production systems related to reverberant loops (a la Lorente, Lashley, Hebb, McCulloch, Pitts & many others) 2) Brains as pattern-resonance systems related to Lashley, Hebb, many others 3) Brains as multiplexed signaling and storage systems holographic paradigms, Longuet-Higgins, Pribram,John 4) Brains as mass-dynamics, broadcast systems 5) Brains as communications nets that create new signals 6) Brains as temporally-coded pulse pattern systems I believe all this is possible using temporal pattern codes.

49. Regeneration of parts

50. Von Neumann’s kinematic self-reproducing automaton