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Intelligence without Reason

Rodney A. Brooks

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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

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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”?

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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 ?

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The new manifesto

• Situatedness (S)

• Embodiment (E)

• Intelligence (I)

• Emergence (E)

• Compare SEIE with SMPA

Check your computer for intelligence

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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?

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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 ?

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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!

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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

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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

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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?

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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 ?

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Brooks’ Approach

• Situatedness

• Embodiment

• Highly reactive architectures with manipulable representations

• No symbols and decentralized computation

What do we need next?

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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 ?

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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?

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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 ?

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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

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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

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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?

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Issues

• Convergence• Synthesis• Complexity• Learning• Coherence• Relevance• Adequacy• Representation

• Emergence• Communication• Cooperation• Interference• Density• Individuality

Almost done

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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 ?