DYNAMICISM & ROBOTICS

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1 DYNAMICISM & ROBOTICS Phil/Psych 256 Chris Eliasmith

2 Dynamicism and Robotics A different way of being inspired by biology by behavior Recapitulate evolution (sort of) A challenge to both connectionism and symbolicism architecture representation time

3 Top-down Robotics Top-down (Red Whittaker) Assumes hierarchical, centralized, Von Neumann-ish architectures Focus on high-level processes goal-directed planning reasoning off-line processing search. Modular organisation to aid integration of components narrow bandwidth interfaces, information encapsulation, independent processing Interaction with the world by building internal models of the external world (like pure vision).

4 Top-down Architecture Internal Model (Memory) Central Planner (CPU) Sensory System (Input) Motor System (Output) Real World Von Neumann-like Robot Architecture Shakey

5 Bottom-up Robotics Bottom-up (Brooks; MIT Lab): Emphasizes decentralized, behaviour-based architectures Behaviour schemas, reaction to environment, on-line processing Modular organisation (layers of behaviour) But complex behavior is the result of many modules interacting No complex internal models or centralized control Subsumption architectures' rely on the world as its own best model Each module can produce a behavior without explicit commands from 'higher-up' The perception/action loop is constantly closed, and occasionally interrupted by higher-level commands.

6 Subsumption Architecture Get object Higher-level goal (direction) Follow path Wander Higher-level goal (co-ordination) Perceive/Act Perceive/Act Perceive/Act Real World Cog

7 Jammin

8 Bio-survivable Robots Artificial Life - Dynamic systems theory (Tilden): Are highly decentralised, behavior-based architectures. Focus on tightly, environmentally-coupled dynamics, and survivability A non-modular organisation Wide bandwidth, coupled behaviours, mutual influence Designs exploit the dynamics of simple analog systems Complexity of behaviour is determined by the complexity of the environment. No internal models or centralized control Like the 'lowest level' of the subsumption architecture Maybe that's all you need.

9 Bio-survivable Architecture React React React React React Real World Walkman

10 Current State-of-the-Art Asimo (Honda) SDR-4X (Sony)

11 Questions Raised by Robotics 1. What kinds of representations are necessary or sufficient for goal-oriented action? (Brooks) 2. What kinds of architecture are best for interacting with the world? (Tilden, Brooks, Whittaker) 3. Can we reproduce intelligent behaviour in a non-biological machine? (Turing, Searle) 4. Would such a machine be conscious, or need to be conscious or have intentionality? (Searle) 5. Should we develop this kind of machine? Who is responsible for its actions?

12 Dynamicism Dynamic systems theory (DST) mathematical tools for understanding systems that vary in time (e.g. pendulums, RC-circuits, etc.) describing phenomena with differential (rate of change) equations Application of DST to the mind A theoretical position voiced by Van Gelder, Globus, Thelen & Smith, Port and others (1990s) Cognitive systems are essentially dynamic i.e. not computational or representational we are more like the weather (or Watt governor) than a computer Critique of GOFAI: Time was left out of the picture Only the notion of (Turing) 'computable' functions (i.e. with infinite time and resources) was considered

13 Watt Governor Metaphor vs.

14 Dynamicism (cont.) Problem for dynamicism: Technically, every physical system is a dynamic system, including computers Solution: Cognitive systems are 'low-dimensional' dynamical systems I.e., only a 'few' (perhaps thousands) of variables are needed to describe cognitive behaviours (not the millions we may think by looking at neurons) Therefore, representation (and hence computation) are irrelevant to dynamic descriptions Consequences: What problems are important changes significantly if we think of cognitive systems as dynamic physical systems rather than symbolcrunching classical systems

15 Robotics and DST Taking DST seriously suggests We need to build systems that are essentially temporal Most important is (inter)action in the real, dynamic world Extra Motivation: Need to regain a unified/holistic approach to understanding intelligence, i.e. we must build whole acting systems. Look at how evolution has spent its time (only % on expert systems). Many hard problems found in perceptual and motor systems (i.e. abstracting and acting), that if solved, may make the 'expert systems' part easy Relegating perception/action to a 'black box' to be dealt with later might be a bad strategy. Toy worlds aren't the real world

16 Brooks Central Claims Desiderata for 'Creatures. They must: 1. react to a dynamic environment in real time 2. be flexible and adaptable to a real world environment 3. be able to capitalize on current situation while fulfilling multiple goals 4. have a purpose 5. be a complete autonomous system in the real world.

17 An early creature Boadichea

18 DST and Robotics Central concepts: Situatedness: The world is its own best model. Embodiment: The world grounds semantics. Intelligence: Intelligence is determined by interactions with the world (i.e. goal-directed and intentional behaviour with no longterm internal states). Emergence: Intelligence is in the eye of the beholder (i.e. we attribute more complex structure than actually exists). Representations: They aren't used and aren't needed.

19 Anti-representationalism Robots and DST systems in general are very different from traditional modeling. For instance: There is no single model of the world (i.e. no complex representations) There is no central controller (i.e. no central representations) There are no represented goals or goal states (i.e. no explicit (goal-oriented) representations) The state of the world determines the actions of the creature (i.e. no representation-guided behaviour) Brooks thus conclude: ''there need be no explicit representation of either the world or the intentions of the system to generate intelligent behaviour" This is a radical departure from both classical AI and connectionism. Maybe a partial return to behaviourism.

20 Concerns with AR 1. Where is the intelligence? Sure there are no complex representations, but there is also no complex behaviour. Cockroaches and other insects have independent (i.e. decentralized) ganglia as a nervous system, but that's not true for intelligent animals (i.e. mammals). Do these robots really exhibit intelligent behaviour? Isn't our definition of intelligence too weak if insects count as intelligent? (e.g. what about abstraction, reasoning, one shot learning?) It seems we use representations (e.g. language) all the time. So they have to fit in the picture somewhere.

21 Concerns with AR (cont.) 1. Is Cog a humanoid insect? Cog is interesting, but his behaviours are no more complicated than an insect. Building something that looks like a human might help, but it won't solve the problem of scaling up. The basic behaviours of Cog are the easy ones. As it gets more complex, it will cease to be flexible. By trading the bottom for the top as a place to start, it moves the difficult problems from bottom to top, but doesn t eliminate them.

22 Concerns with AR (cont.) 1. Levels or degrees of representation Rather than representation being 'there' or 'not there' isn't it a matter of degree? Although current robots don't have semantically evaluable tokens, in order to get interesting behaviours, the internal states of the robot will have to support those kinds of tokens. Maybe single neurons don't represent, but we do. So, somewhere in between representations get generated. We shouldn't throw them out just because we can't see them at the lowest level.