Artificial Intelligence

Embodied Intelligence

(R. Brooks, MIT)

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Outline

• Key perspectives for thinking about how an

intelligent system interacts with world

• Compare mainstream AI to early artificial creature

approaches

– Derive number of morals from comparison

• Look at some simple animals to see how they

operate in their worlds

– Making comparisons to artificial creatures

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GOFAI(Good Old Fashioned AI)

• Traditional AI approach was/is

– Identify essence of something

– Study that, expecting to generalize back to full concept later

• Playing with blocks

– “Microworld”: the blocks world

– Ignores untidiness of real world

– Only the essence of building simple block towers is considered

• Everything represented in a logical calculus to describe a 2-D scene

• Blocks all same size, and perfectly aligned on top of each other

• Has perfect perception of world (no ambiguities)

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GOFAI(Good Old Fashioned AI)

• Standard type of problem in microworld

– Transform stacks of blocks in left scene to stack of blocks in right scene

– Input to planner might be goal situation

• (and (on A B) (on B C))

• Not the geometrical description that humans see/use

– Representations chosen are usually highly dependent on the problem to be solved

• Often constrains the types of problems to work on

B A

C

A

C

B

Initial situation Goal situation

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GOFAI(Good Old Fashioned AI)

• Large parts of AI devoted to recasting problems of intelligence in terms similar to simple blocks world descriptions

– Then finding ways to solve them

– This has not scaled well

• Microworlds were used to simplify the study of intelligence to manageable levels

– Implicitly assumes that intelligence is about problem solving (recall Minsky)

– The “essence” of intelligence in solving a puzzle omits lots of details not important to explicit statement of the puzzle

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An Alternate View

• Intelligence is all about making judgments when

there are large numbers of messy details all about

– Especially when no clear single best answer

• Analogy to Sherlock Holmes

– His strength was perceiving details others did not notice

– Perception was not abstract and distant

• At scene of crime, walked along getaway lanes, poked his head

into the pantry…

– Directly experienced what was there and where

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From an Evolutionary Perspective

• How was time spent within the 4.6 billion years of earth-based biological evolution?

– Single cell entities arose roughly 3.5 billion years ago

– A billion years passed before photosynthetic plants

– Fish and vertebrates arrived around 550 million years ago

– Insects at 450 million years ago

– Reptiles appeared 370 million years ago

– Dinosaurs at 330 million years ago

– Mammals at 250 million years ago

– First primates appeared 120 million years ago

– Predecessors of great apes at 18 million years ago

– Humankind around 2.5 million years ago

• Agriculture (19,000), writing (5,000), scientific knowledge (last few hundred years)

Slow

start

Things

now

move

fast

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Another Approach to Intelligence

• Consider the previous evolutionary

perspective

• Stick with messy detailed world

– Consider how a creature might get around and

survive in the world

– Evolution spent most of its time in this before

getting to us

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New AI: Embodied Intelligence• Key points

– Intelligent systems operate in a world that is

• Complex, uncertain, and not fully perceivable

– It carries out tasks involving perception and motion

• Must do some things to survive in world

• Not do solve problems built into it by researchers

– Artificial creature is embodied

• Effects of actions depend on state of external world

• External world influences perception of creature

– It must have internal drive to direct operations in world

• Hunger for electricity

• Reductionist approach to AI

– Shift from “solving problems” to “existing within a world and maintaining of goals”

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Embodied AI vs. GOFAI

• Traditional AI research keeps task difficulty

– Then tries to make environment complex

• Embodied AI research starts with most complex world ever to be encountered

– Then takes up challenge of task to perform in that environment

Traditional AI

starting regionEmbodied AI

starting region

Environment Complexity

Tas

k C

om

ple

xit

y

Target

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Braitenberg’s Vehicles

• Valentino Braitenberg

– Neuroscientist

– Wrote Vehicles: Experiments in Synthetic Psychology(1984)

• Series of fourteen “thought experiments” about building little vehicles to operate in the world

– Relates physical systems to concepts of psychology, cognition, and free will

• Each chapter discusses a different vehicle

– Increasing in sophistication

– Display increasingly life-like phenomena

• Eventually exhibit behavior like egotism and optimism

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The Vehicles• Braitenberg’s Vehicle 1

– One sensor connected to one actuator

• More stuff sensed makes it go faster

– Suppose

• Sensor measures temperature

• Actuator is little rocket engine, with force proportional to temperature

– Result

• In friction environment

– Go faster when warm, and slower when cold

– Can veer off straight path due to non-smooth world

• In frictionless environment (outer space)

– Vehicle never slows down (acceleration proportional to temperature)

– Proceeds in straight line

Sensor

Actuator

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Moral 1: Situatedness

The behavior of a vehicle, or creature,

depends on the environment in which it is

embedded or situated

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The Vehicles

• Braitenberg’s Vehicle 2

– Design

• Two actuators in laterally symmetric position

• Two sensors facing forward, symmetrically placed

– Comes in a number of types

• Depending on how sensors are connected to actuators

• When actuators apply same force, go straight ahead

• When right actuator apply more force than the left, vehicle veers left

00

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The Vehicles

• Vehicle 2.a

– Left sensor is connected to left

actuator

– Right sensor connected to right

actuator

– Suppose

• Sensors measure intensity of light

coming from source

• Actuators produce more force

(speed) when there is higher light

intensity level

00

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The Vehicles

• Result of Vehicle 2.a

– If right sensor closer to light

source than left sensor

• Right sensor gets more light

– Then right actuator drives

harder making it turn left and

away from light source

• Initial turning cause even

more turning

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The Vehicles

• Vehicle 2.b

– Crossed sensor/actuator connections

• Left sensor is connected to right actuator

• Right sensor connected to left actuator

– When light falling on right sensor more than falling on left sensor

• Left actuator produces higher force

• Causes vehicle to turn to right

– Towards the light

– As get closer to light, both actuators increase and vehicle accelerate toward light

• Eventually running right into it

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Moral 2: Embodiment

The actions of a creature are part of a dynamic with the world and have immediate feedback on the creature’s own sensations through direct physical coupling and its consequences

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Vehicle Behaviors

• Braitenberg describes vehicle behaviors in anthropomorphic terms

• From point of view of an observer

– Vehicles 2a and 2b both seem to dislike light sources

• Vehicle 2a is a coward (moves away from light source)

• Vehicle 2b is aggressive toward light sources (smashing into them at high velocity)

• There is nothing about like, dislike, or aggressionexplicitly built into the vehicles

– But observers do describe the behavior of the vehicles in those terms!

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Moral 3

Terms descriptive of behavior are in the eye

of the observer

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Other Vehicles

• Vehicles 3.a and 3.b

– Sensors inhibit the actuator

• The more of the sensed quantity, less force produced by actuator

– These vehicles slow down in vicinity of light source

– Steering is opposite to class 2 counterparts

• Vehicle 3.a (uncrossed wires)

– Tends to stay centered on light source until stop in front of light

• Vehicle 3.b (crossed wires)

– Tends to veer away from light source

– When eventually faces away from light source, then more influenced by rest of environment

• Once inhibition added to connection options, opens up possibility to build more behaviors

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Other Vehicles

• Vehicles 4.a

– Adds non-linear relationships to wires connecting sensors and actuators

– Leads to very complicated behavior in all sorts of environments

– But too complex to describe the behavior of the vehicles directly

– Instead use descriptive terms like instinct to describe particular action patterns

• The resulting behavior is generated by the totalityof the system

– Not by any one piece

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Moral 4: Emergence

The intelligence of the system emerges from the system’s interactions with the world and from sometimes indirect interactions between its components – it is sometimes hard to point to one event or place within the system and say that is why some external action was manifested

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Emergence

• Emergence is not a linear phenomena

– Behavior produced by the system is more than the sum of its parts

• We do not necessarily need to build an explicit behavior into the system itself

• More with vehicles

– In Vehicle 4.b, discontinuous connecting elements introduce thresholds into system

• Vehicles appear to reach decisions at times

– In Vehicle class 5, networks of non-linear elements are introduced

• Vehicles now have memory (more on this later…)

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Vehicles as Creatures

• Braitenberg devised artificial creatures (called

vehicles) that live in a world

• Braitenberg did not simplify the world to be clean

and neat

– Instead he describes how properties of world may effect

the behavior of his creations

• Starting from very simple creatures, Braitenberg

sketches out how one could proceed in bottom-up

manner to reach intelligence

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Autonomy

• An autonomous vehicle/creature

– Able to maintain long-term dynamic with

environment without intervention

– Once switched on, it does what is in its nature

to do

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Physical Artificial Creatures

• Tortoises Elmer and Elsie (1950)

– W. Grey Walter

– Design

• Two electric motor actuators

• Single bump sensor

• Light sensor

– Recharging hutch

– Explored environment with

complex behaviors

• “Remarkably unpredictable”

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From 1950 SciAm Article

Seeking light Reaction to obstacle

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Physical Artificial Creatures

MIT

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Other Robots

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Physical Non-Artificial Creatures

• Are artificial creatures anything (computationally) like real creatures?

• Animals

– Frogs

• “Bug detector”: respond to size and motion

– Pigeons

• Navigation: stars, sun, magnetic fields, olfactory, ultrasound

• Ranked sensors (layered), not sensor fusion

– Jumping spiders

• Responds to moving stimulus (prey, courtship)

– Bees

• Dance language for signaling location of food to others

• Seem to be implementable for artificial creatures

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Learning

• Simple connections can lead to complex (emergent) behavior

• Hard to predict which components/connections lead to specific behavior

• Given a desired behavior (or result), how do we figure out the proper set of connections?

– Machine learning!

– More later (after mid-term)

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Summary

• Compared mainstream AI to early artificial

creature approaches

– GOFAI

• Braitenberg’s vehicles

• Looked at some simple animals to see how they

operate in their worlds

– Making comparisons to artificial creatures