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Autonomy and Artificiality

Autonomy and Artificiality. Margaret A. Boden Hojin Youn. 1. The Problem - and Why It Matters. H. Simon : “The Science of the Artificial” AI, Cybernetics A-Life : uses informational concepts and computer-modelling to study the functional principles of life in general (C. Langton, 1989)

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Autonomy and Artificiality

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  1. Autonomy and Artificiality Margaret A. Boden Hojin Youn

  2. 1. The Problem - and Why It Matters • H. Simon : “The Science of the Artificial” • AI, Cybernetics • A-Life : • uses informational concepts and computer-modelling to study the functional principles of life in general (C. Langton, 1989) • A-Life vs. AI • abstract study of life : abstract study of mind • the concept of autonomy, applies to A-Life.

  3. 1. The Problem - and Why It Matters • Human autonomy / freedom • Rollo May(1961) • dehumanizing dangers in modern science • refer to the mechanic implications of natural sciences(behaviourists psychology) • Skinner(1971) • Freedom, is an illusion. • Environmental pressures determine our behavior. • What ofartificial sciences?

  4. 1. The Problem - and Why It Matters • Contents • How A-Life addresses the phenomenon of autonomy • The concept of autonomy • Artificial sciences doesn’t deny, downgrade, our freedom

  5. 2. AI, A-Life, and Ants • Simon(1969) • much the same view with Skinner • rational thought and skilled behaviour are triggered by specific environmental cues • but allows internal, mentalistic cue • GPS • paid no attention to environmental factors • human thought purely in terms of internal mental/computational process

  6. 2. AI, A-Life, and Ants • Robotics driven by internalist view • guided top-down by internal planning and representation • not real-world, real-time creatures : their env. were simple, highly predictable ‘toy-worlds’ • noubelle AI • behaviour controlled by an interaction between • low-level mechanisms in the system • constansly changing details of the environment

  7. 2. AI, A-Life, and Ants • Robotics, situated • no need for the symbolic representations / detailed anticipatory planning • Traditional robotics: • brittleness caused by unexpected input • no way the problem environment can help • “Best source of information about the real world is the real world itself” • usually in hardware, but • Behavior apparently guided by goals and hierachical planning can occur(Maes 1991)

  8. 2. AI, A-Life, and Ants • Studying ‘emergent’ behaviors - GA and A-Life • GA • self-modifying programs, continually come up with new rules(structures) • use rule-changing algorithms modelled on genetic processes • Mutation : makes an change in a single rule • Crossover • Algorithms for identifying & selecting the successful rules • e.g.) Karl Sims(1991) • use GA to generate new images

  9. 2. AI, A-Life, and Ants • A-Life • use computer modelling to study processes that start with relatively simple, locally interacting units, and generate complex individual/group behaviors • Self-organization / Reproduction / Adaptation / Purposiveness / Evolution • Self-organization • flocking : Boids(a collection of very simple units) modelling • Possible for group-behavior to depend on very simple, local rules • Situated robotics / GA / A-Life share: • bottom-up, self-adaptive, parallel processing

  10. 2. AI, A-Life, and Ants • Evolutionary Robotics • simulation of insect-like robots • adapts to its specific task-environment • Links with biology • noubell AI : autonomous agents • A-life : autonomous systems

  11. 3. Autonomous Agency • Artificial insects: • specifically constructed to adapt to the particular environment • Autonomy 1. The extent to which response to the environment is direct or indirect • involves behavior mediated by inner mechanisms shaped by experience

  12. 3. Autonomous Agency 2. The extent to which the controlling mechanisms were self-generated rather than externally imposed • behavior which ‘emerges’ as a result of self-organizing process, not prefigured in the design of the creature • emergence-hierachies, evolve as a result of new forms of perception • intelligible vs. unintelligible emergence • (flocking : Sims’s program) • e.g.) different thoughts in consciousness

  13. 3. Autonomous Agency 3. The extent to which inner directing mechanisms can be reflected upon, and/or selectively modified in the light of general interests or the particularities of the current problem in its environmental contexts • conscious deliberation : the crux of human autonomy • conscious thought requires a sequential ‘machine’ more like a von Neumann computer • Creativity: an aspect of human autonomy • Autonomy and Unpredictablity • AI systems, not necessarily deterministic • Determinism  Predictability

  14. 4. Conclusion • The science of artificial can model autonomy of various kinds • highlights autonomy - as a characteristic of living things • A-Life can teach us how increasing complexity arises from self-organization on successive levels, and how a creature can negotiate its environment by constant interaction with it. • But, the kind of autonomy, free choice, is better illuminated by the classical AI. • AI does not reduce our respect for human minds. • Helps us to understand how it is possible

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