Intelligence without Reason

1. Intelligence without Reason Rodney A. Brooks CS790X Anil Shankar

2. Overview of the talk • Status-check on research in AI • Intelligence without explicit reasoning systems • Influence of various disciplines and technology on the development of AI • Situatedness, Embodiment, Intelligence and Emergence CS790X Anil Shankar

3. Robotics • Static environments • Off board computation • Sense-Model-Plan-Act architectures (SMPA) • Assuming that the static world can scale to the real dynamic world Were these robots “intelligent”? CS790X Anil Shankar

4. Re-think Intelligence • Do we always problem-solve and plan? • An agent’s internal representation compared with real-world object representation • Where should the agents be? • Can an agent have goals and beliefs? So how do we re-think then ? CS790X Anil Shankar

5. The new manifesto • Situatedness (S) • Embodiment (E) • Intelligence (I) • Emergence (E) • Compare SEIE with SMPA Check your computer for intelligence CS790X Anil Shankar

6. Us and Them • Silicon based machines • Von Neumann architecture • Biological machines • Low speed, massively parallel, fixed and bounded network topology, redundancies in design What would the classical AI guys say? CS790X Anil Shankar

7. Classical A.I • Turing Test • Allowed disembodiment • Chess • What about Go? • Dartmouth Conference • Search • AI techniques • Search, Pattern recognition, learning, planning and induction (disembodied and non-situated, reliance on performance increases Where did all these ideas come from ? CS790X Anil Shankar

8. Other Disciplines • Cybernetics • Organism and it’s environment should be modeled together (situatedness) • Abstraction • Blocks world, controlled environments, Shakey, internal models, complacence with performance in static environments • Knowledge Representation • Represent knowledge, problem-solve, learn …ungrounded! CS790X Anil Shankar

9. Other disciplines (2) • Vision • Reconstruct static external world as a three dimensional model • Parallelism • Neural networks, no situatedness, hand-crafted problems, real-world performance missing • Biology • Use ethology to make an ungrounded assumption about hierarchical models of thinking/intelligence CS790X Anil Shankar

10. Other disciplines (3) • Psychology • Marr’s view of vision maybe different from biological vision • Representation of knowledge as • Central storage (concepts, individuals, categories, goals, intentions, etc.) • Knowledge stored independent of the circumstances in which it is acquired • Modality-specific organization of meaning CS790X Anil Shankar

11. Other disciplines (4) • Neuroscience • What about the hormones? • Do we know enough about the neurological organization simple creatures? Do we want to consider something that might actually work? CS790X Anil Shankar

12. Brave New World • Situatedness • The world is its own best model • Embodiment • The world grounds regress • Intelligence • Intelligence is determined by the dynamics of interaction with the world • Emergence • Intelligence is in the eye of the observer Will these work ? CS790X Anil Shankar

13. Brooks’ Approach • Situatedness • Embodiment • Highly reactive architectures with manipulable representations • No symbols and decentralized computation What do we need next? CS790X Anil Shankar

14. Domain Principles • Complete integrated intelligent autonomous agents • Embodiment in the real world • Efficient performance in dynamic environments • Operate on time-scales in proportion to that used by humans How do we realize them ? CS790X Anil Shankar

15. Computation Principles • Asynchronous network having active computational components • No implicit semantics in exchanged messages • Asynchronously connected sensors and actuators to two-sided buffers What will these ideas help us realize? CS790X Anil Shankar

16. Some consequences • A state enabled system and not just a reactive one • Bounded search space • Simple data structures • No implicit separation of data and computation Practice and Principles ? CS790X Anil Shankar

17. More on Brooks’ robots • No central model, no central control locus • Network components can perform more than one function • Behavior specific networks, build and test method • No hierarchical arrangement, parallel operation of behaviors (layers) • Use the world itself as a communication medium • Simpler design, on-board computation, miniaturization possible • Limitations • Power, computational capability The real robots please CS790X Anil Shankar

18. A few specific robots • Allen • Reactive, sonar, non-reactive goal selecting layer, same computational mechanism for both reactive and non-reactive components • Herbert • World as it’s own model, opportunistic control system, adapt to dynamic changes • Toto • Extract only relevant representations, decentralized, active-maps • Complex goal-directed and intentional behavior with no long term internal state Everything is not peachy CS790X Anil Shankar

19. A few issues • Complexity • Environment, sensors and actuators, layers • Learning • Representations for a task, calibration, interaction of modules, new modules • Behaviors • Specification, number, interaction What else is there to do next? CS790X Anil Shankar

20. Convergence Synthesis Complexity Learning Coherence Relevance Adequacy Representation Emergence Communication Cooperation Interference Density Individuality Issues Almost done CS790X Anil Shankar

21. Main Points Status-check on research in AI Intelligence without explicit reasoning systems, emergent property and evolutionary basis Influence of various disciplines and technology on the development of AI Situatedness, Embodiment, Intelligence and Emergence Questions ? Comments? Suggestions ? CS790X Anil Shankar