Download Intelligence without representation

INTELLIGENCE WITHOUT
REPRESENTATION
Overview
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Definitions and introduction
Adoptions from evolution
Abstraction and intelligence
Building creatures
Compensation for representation
The creatures
Comparison to other approaches
Limitations and conclusions
The presented paper
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
Intelligence without representation
Rodney A. Brooks, 1987
 describes
research done at the MIT Artificial
Intelligence Laboratory
 research is supported by IBM Faculty 9 Development
and others
 career summary
 current
professor at the MIT
 former director of CSAIL, founder of iRobot Corp.
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DEFINITIONS AND INTRODCTION
Intelligence without representation
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

Hard to give an exact definition
The sum of higher cognitive abilities
 abstract
thinking
 building representations
 problem solving
 reasoning
(Sternberg & Berg)

Popular consent for human Intelligence
Intelligence without representation
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Artificial intelligence tries to replicate human intelligence
naive goal of research, very large area to work
Today AI is often divided into smaller subareas
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represent knowledge
natural language understanding
computer vision
planning etc.
relevant AI domains
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navigation
medical diagnosis
scheduling etc.
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Etymology
 human

substitution for someone not present
In AI
 symbols
build a representation
 each symbol holds information
 the symbol is interpreted beyond its object character
(likewise a variable)
 an internal model of the world is build
(Strube)

One early AI example: Blocks world
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AI System: Blocks world
logical planning to achieve the goal
most important: an explicit representation of the
state of the world
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AI System: Blocks world
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one possible representation of the initial state
On(A, table) ^ On(B, table) ^ On(C, table)
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On(A, table) ^ On(B, table) ^ On(C, table)

Brooks criticism
 This
describes not the underlying concept on the whole
e.g. the distance between blocks, size of blocks etc.
 Humans representations are much too complex to
replicate them in detail
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The main idea (roughly)
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
by reacting on the real world, representations are
not necessary
produce intelligent systems which include perception
and action as well
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2 steps to achieve this
2.
Build intelligent systems incrementally, ensure that
every step is a complete and valid system
Each System must work in the real world

This forces one to start with very simple systems
1.
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ADOPTIONS FROM EVOLUTION
Adoptions from evolution
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Evolution already created intelligence
Once the “essence of being and reacting” is
established, higher abilities are simple to achieve
Adoptions from evolution
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Starting with a simple system
Robot needs mobility, vision and survival related
tasks to have a basis for intelligence
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ABSTRACTION AND INTELLIGENCE
Abstraction and intelligence
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
Brooks argues that AI suffers from abstraction
Unsolved subproblems of AI are considered as
non-AI problems
Perception or motor skills are often excluded
They are part of AI as well and limit the remaining
abilities to a high degree
A system cannot reason beyond its representation
Abstraction and intelligence
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A system cannot reason beyond its representation
Example MYCIN
 good
diagnosis system for bacterial infections
 has no model of the human body
 if the aorta is ruptured and the patient looses blood
every second
 MYCIN will search for bacterial causes
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Each robot needs its own “Merkwelt”
Merkwelt
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a concept of J. J. Uexküll, who was a popular
German biologist
„Einen anderen Standpunkt gegenüber dem
Weltpanorama als den unseres Subjektes gib es nicht,
weil das Subjekt als Beschauer zugleich der Erbauer
seiner Welt ist. Ein objektives Weltbild, das allen
Subjekten gerecht werden soll, muss notwendig ein
Phantom bleiben.“
(Uexküll)
Merkwelt
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Our model of the world is determined by the things
we are able to sense
We miss many things according to our anatomy
We should not force our Merkwelt on the robot
Instead, the robot must build its own Merkwelt
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BUILDING CREATURES
Building a creature
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Brooks wishes to create entities that must...
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deal with their environment
deal with changes in their environment
be able to maintain several goals
do something in the world, have a purpose in being
How to build such creatures? Which methodology?
Building a creature
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Engineering methodology for building creatures
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decompose the system into parts
build the parts
interface them into a complete system
Decompose by function?
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every function gets its module (eg. vision)
Decompose by action?
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every output action gets its own layer
Decomposition by function
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a vision module perceives an image
a central system processes the data
a motor system controls the output
a chain of modules is needed from
input to output
every module needs special
interfaces
almost not possible to...
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locate errors in a long chain
model complex behaviour
Decomposition by action
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each layer is a “activity producing system”
layers connect input to output
sensing to action
layers can interact with other layers
massively parallel working agent
each layer implements one activity
Decomposition by action
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Advantages of decomposition by action
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one can start with a simple but complete system
for additional behaviour, new layers can be added
intelligence is therefore incrementally raised
an approach comparable to evolutionary processes
Decomposition by action
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Advantages of decomposition by action
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one can start with a simple but complete system
for additional behaviour, new layers can be added
intelligence is therefore incrementally raised
an approach comparable to evolutionary processes
Layers work in parallel and can be composed incrementally,
Brooks calls this concept “subsumption architecture”
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COMPENSATION FOR
REPRESENTATION
Compensation for representation
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Is there a central representation?
• Sensory input is not stored
• Processes doing the perception are separated
• Data is processed differently and independently
There is no central representation
• This occurred by accident
Compensation for representation
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
Layer consists of several finite-state machines
They have state, transition and action
These are the modules doing the computation
Their behavior/activity is depending on their state
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Example: states while parsing the word “nice”
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Compensation for representation
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The finite-state machines react to their input
They cannot be controlled by a single control center
Only new data-input result in a different state
 Although the states are data depended, they are not
used as a representation to work with
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THE CREATURES
The creatures
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Three kinds of robots:
1. Allen
2. Tom and Jerry
3. Herbert
(from left to right)
The creatures
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Allen:
• Allen is using an offboard LISP
machine for computation support
• Connections between the finite state
machines are virtual wires
The creatures
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Tom and Jerry:
• Maximum of 1Bit messages
• The finite state machines are
connected by real wires
The creatures
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Herbert:
• Consists of 24 processors
• Has an arm to grip cans
• Maximum of 24Bit messages
• The finite state machines are
connected by real wires
• Has infrared proximity sensors
to avoid obstacles
• Grasping is controlled by
depth-sensor
Architecture
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The layer-system (subsumption architecture):
• 3 layers were implemented
• The first is most basic
 avoid
• The second is to cause action and avoid standing still
 wander
• The third uses lower level finite-state machines to explore
 explore
Architecture
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Architecture
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The first layer Avoid
• Makes the agent avoid hitting objects
• Both static and moving objects are avoided
• It is divided into several finite-state machines
Architecture
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Finite-state machines of Avoid
Sonar
• Runs sonar-system and emits every second a map of
polar coordinates to 'Collide' and 'Feelforce'
Architecture
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Collide
• Is looking for anything dead ahead, if so sends a halt to
‘Forward’
Feelforce (runs simultaneously to 'Collide'):
• Each sonar return is considered as repulsive object
causing this finite state machine to calculate a repulsive
force
• All repulsions are put together to produce the output
given to 'Runaway'
Architecture
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Runaway
• Simply thresholds the output from 'Feelforce' and passes
it on to 'Turn'
Turn
• Directs the robot away from repulsive objects
Forward
• Drives the robot forwards
• If the robot is moving forward and a halt is received it
will stop moving forwards
Architecture
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The second layer Wander
• When ‘turn’ and ‘forward’ have not the state ‘busy’
• Wander shall prevent the robot from standing still
Architecture
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Finite-state machines of Wander
Wander
• Generates a random heading every ten seconds
• The heading is passed to the 'Avoid' machine
Architecture
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Avoid
• Treats heading as attractive objects and sums it up with
the map from 'Feelforce‘
• By injecting its results to the output of lower-level layers
'Runaway' it suppresses the lower-level behavior.
• Thus the decision by 'Wander' is considered as well as
the initial repulsive input
• The input from 'Avoid' will be ignored if 'Turn' and
'Forward' are currently busy running the robot
Architecture
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The third layer Explore
• Makes the robot try to explore
• Interacting with the 'Wander'-layer
Architecture
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Finite-state machines of Wander
Whenlook
• Notices when the robot is not busy('Status')
• Inhibits the machine 'Wander‘
• Starts the mechanism to look around for a distant
location to explore ('Look'/'Stereo')
Architecture
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Stereo
• Finds a location to explore
• Sends output to 'Pathplan' and 'Integrate'
Pathplan
• Sends a direction to the machine 'Avoid'. This is to make
lower-level avoidance still possible
Integrate
• As avoiding might corrupt the indicated direction given
by 'Pathplan' this machine monitors the actual direction of
the robot('Status')
• It updates 'Pathplan' with the actual direction
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COMPARISON TO OTHER
APPROACHES
Comparison to Connectionism
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Connectionism consists of many simple processing units
They are highly linked
Knowledge is stored in
• the network structure
• weighted links
• special properties of units
This can be considered as representation
The processing units work in parallel
Comparison to Connectionism
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The used finite-state machines are neither simple nor
uniform
They are only a little linked
The developers are not interested in stored knowledge
Comparison to Neural Networks
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Neural Networks process activity
They try to replicate human biology
This contradicts strongly with Brooks idea of incremental
intelligence
Systems should not be copied on the whole
Comparison to Production Rules
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Consider the layers as production rules
Each layer/production is selected according to the input
But: there is no big rule database
The concept is not based on selecting the adequate rule
Comparison to Blackboard
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Blackboard is an architecture with many independent
modules
• The modules write all information into one data structure
 Data is public and anonym
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Brooks has no such information-repository
The finite-state machines are in order so the information
producer is determined
Comparison to Uexküll
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Uexküll and Heidegger examined the dynamics of
existence
Merkwelt etc.
Brooks finds many similarities in their approaches
They occurred by accident, because Brooks only followed
engineering considerations
He designed the architecture on his own ideas
Comparison to Other Approaches
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There are many analogies between the architectures
At least, Brooks finds some differences to all of them
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LIMITATIONS
Limitations – Number of layers
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The number of layers will increase complexity
• Three layers were tested with robots
• Six layers were tested in simulation
• Adding layers should only be done when the system runs
without problems in real world
• Because of the developed subsumption architecture,
adding new layers is quite simple
Limitations – Complexity
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The number of layers will increase complexity
• Brooks started of with a three layer system
• A fourteen layer architecture is currently under
development
• Goal of this high-complex architecture is to wander
around and take soda-cans to a bin
• The complexity will always be lower than connectionist
models
Limitations - Learning
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The number of layers will increase complexity
• Learning is currently performed by an isolated module in
order to copy the behavior of insects
• The copied insects show a constant amount of knowledge
• The module is not connected to other modules
• Research is still in progress on how to connect them
Conclusion
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Conclusion and hypotheses of Brooks
Representations are counterproductive for simple
level intelligence. Instead, one should use the world as
its own model (conclusion)
Representations are the wrong unit of abstraction
for intelligent systems (hypothesis)
Conclusion
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Replicating human intelligence leads to disregarding
the basis of intelligence
A traditional AI System is bound to the validity of its
representation
Brooks pursues his ways of achieving intelligence
incrementally
Outlook
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Brooks lead to new approach in AI research
Behaviour based artificial intelligence or situated AI
His approach is often used in RoboCup-robots
His foundation iRobot delivers many robots today
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(PackBot at for rescuing)
Outlook
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Brooks lead to new approach in AI research
Behaviour based artificial intelligence or situated AI
His approach is often used in RoboCup-robots
His foundation iRobot delivers many robots today
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(Roomba for cleaning purposes)
Criticism
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• Because of the new and elementary approach, the
evolution of such robots might start very slow
(comparable to biological evolution)
• No representation is used, what makes learning
hardly possible
• The interface-problems of “decomposition by
function” cannot discard the whole principle
• At least the state machines have a state and
therefore some kind of representation