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 Biological 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 Chess Dartmouth Conference AI techniques
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 Abstraction Knowledge Representation
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 Parallelism Biology
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 Embodiment Intelligence Emergence
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 Herbert Toto
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 Learning Behaviors
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. Issues Convergence Synthesis Complexity Learning Coherence Relevance
Adequacy
Representation
Emergence
Communication
Cooperation
Interference
Density
Individuality
Almost done
CS790X Anil Shankar

21. Status-check on research in AI
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