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COM1070: Introduction to
Artificial Intelligence: week 10
Yorick Wilks
Computer Science Department
University of Sheffield
www.dcs.shef.ac.uk/-yorick
Characteristics of von Neumann architecture
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A von Neumann machine is a sequential processor;
executes instructions in a program one after
another.
Each string of symbols stored at specific memory
location.
Access to stored string via numerical address of
string’s location.
Single seat of control, central processing unit, CPU.
But brain may still be a computer, just not a von
Neumann machine, or possibly a VNM with
enormous redundancy of data.
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Ability of GOFAI and Neural Nets to provide an
account of thought and cognition
The debate is about whether it is possible to provide a
neural computing (NN) account of cognition, or
whether we should assume that a symbol system
(GOFAI, or Good Old Fashioned AI) is required.
Physical symbol system hypothesis: it is possible to
construct a universal symbol system that thinks
Strong symbol system hypothesis: only universal
symbol systems are capable of thinking.
I.e. anything which thinks (e.g. human brain) will be a
universal symbol system.
I.e. all human thinking consists of symbol manipulation.
I.e. only computers can think – therefore we are
computers.
If it’s not a universal symbol system, it can’t think!
Pylyshyn: an advocate of strong symbol system
hypothesis
Our mental activity consists of manipulation of sentence
like symbolic expressions.
Human thought = manipulation of sentences of internal
mental language.
Fodor’s Language of Thought or Mentalese
We can show that brain does not have a von Neumann
architecture.
But this does not disprove Strong Symbol System
Hypothesis.
Strong symbol system hypothesis says nothing about
architecture.
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Could have symbol manipulation in parallel system
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Could have different memory system: i.e. contentaddressable storage
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Instead of CPU could have different parallel control.
Brain can form representations of events in world.
SSSH: representations in brain take form of sentencelike strings of symbols.
I.e. sentence ‘Fido licks Brian’ is symbolic
representation of fact that Fido licks Brian.
Language of thought, or Mentalese
Mentalese representations: not literally English, but like
sentences, in that they have basic vocabulary units,
and form in which they are arranged determines
their meaning.
Advantages of mentalese
Provides an account of beliefs, intentions and doubts
etc. these are also expressed in terms of mentalese
sentences.
Provides an account of productivity and systematicity.
Human language is productive, (no limit to number of
sentences we can produce).
And systematic (if you can say John loves Mary, you
can also say Mary loves John)
Human thought: productive and systematic because it
relies on Mentalese, which is productive and
systematic.
Origin of all this in Chomsky’s generative linguistic
theories.
Neural Computing: can it form an alternative to SSSH?
Is it possible to provide a connectionist account of
thought, or is it as SSSH advocates would claim,
impossible?
Symbols: Subsymbolic Hypothesis versus Strong
Symbol system hypothesis
Symbol System Hypothesis (Newell and Simon, 1976)
‘..a physical symbol system has the necessary and
sufficient means for general intelligent action..’
A symbol designates something.
A symbol is atomic (cannot be broken down further).
E.g. ‘elephant’ designates an elephant. Or P could
designate elephant. Or 01100 could designate
elephant, but with no interpretation of the 1s and 0s.
Compositional symbol = compound symbol which has
meaningful parts, whose overall meaning is
determined by meaning of those parts.
E.g. sentences of natural language.
The kangaroo jumped over the elephant.
Distinction between symbol types and symbol tokens
E.g. in AMBIGUITY, 9 letter tokens, 8 letter types.
Same symbol type can be realised in many different
ways.
Symbol is both (a) a computational token, and (b) it
designates something, i.e. it is a representation.
Connectionism: subsymbolic hypothesis
(see Smolensky, 1988).
Symbols can be broken down, computation takes place
at subsymbolic level.
Connectionist representations: distributed representation.
Distributed representation = pattern of activity over
several nodes.
E.g. in a distributed representation of ‘elephant’,
‘elephant’ is represented by distributed representations over several nodes.
Thus, there is not an atomic symbol for elephant.
So connectionism rejects Strong Symbol System
Hypothesis
To complicate matters, one can have localist neural
networks, where concepts are represented by single
nodes. (e.g. node which represents ‘elephant’).
Localist connectionism assumes its symbols given
and may be compatible with the SSSH.
This discussion applies to distributed (non-localist)
representations.
Distributed representations = subsymbolic computation.
E.g. representing letter A
 Localist scheme, single unit for A, one for B etc.
 Or (in a subsymbolic system) letters represented as
patterns of activity across 78 units. E.g. A = units 1,2,
and 3, B = units 4,5, and 6 etc.
 Individual units stand for features of letters. Thus
letter A will be joint activity of various features it
contains. So letter E will share several features with
letter F. Thus similarities and differences among
items are reflected in similarities and differences
among representations.
Symbolic-Subsymbolic distinction.
In a symbolic system the computational level coincides
with the representational level. In a subsymbolic
system, the computational level lies beneath the
representational level.
Symbol is both (a) a computational token, and (b) it
designates something, i.e. it is a representation.
But in subsymbolic Connectionism: representations
across several units, but computational tokens are
units.
But does the same issue reappear? Subsymbolic
computation may not assume the symbols but what
about their features--e.g. horizontal strokes for E and
F??
Symbolic criticisms of connectionism
Arguments in favour of Strong Symbol System
Hypothesis
Fodor and Pylyshyn (1988)
Argument that connectionism is inadequate as a
representational system.
Focus on issues of compositionality and
systematicity
Fodor and Pylyshyn (1988) argue
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Compositionality and structure sensitivity are
necessary to support cognitive processes.
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Only classical representations exhibit compositionality and structure sensitivity (systematicity)
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Only classical representations can support
cognitive processes.
Compositionally concatenative representations: Where
complex representations are composed from
primitive tokens combined concatenatively
Molecular representations can be formed out of
constituents, and can be manipulated in accord with
syntactic rules.
E.g. ‘the kangaroo jumped over the elephant’
If you can understand this, you can also understand a
sentence which has the same words in a different
order. (Systematicity).
E.g. ‘The elephant was jumped over by the kangaroo’
or
‘The elephant jumped over the kangaroo’
Two sentences are composed of same elements.
Constituent elements manipulated according to
syntactic rules.
But, according to Fodor and Pylyshyn, connectionist
representations cannot be manipulated like this.
F&P: cannot compose simple connectionist
representations into more complex representations
Much of this argument comes down to the role of tree
structures or hierarchies, which are needed to
express syntactic relationships --- SSSH people say
trees cannot be learned by connectionist systems..
But Jordan Pollack showed in 1986 that they can (up to
a point anyway)--this argument is very like a return of
the XOR argument at a higher level.
Connectionist Counter-Arguments
Distributed representations: nonsymbolic and
continuous (developed over hidden units).
But van Gelder (1990), can have functional
compositionality
‘.. We have functional compositionality when there are
general, effective and reliable processes for (a)
producing an expression given its constituents, and
(b) decomposing the expression back into those
constituents…’
Many nets can do (a) and (b)
For example, net can be trained to structurally
disambiguate sentences.
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John saw money with the telescope.
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(John (saw money) (with the telescope)).
Complex representations of input sentences developed
over hidden units.
Then these representations decomposed into required
output (ii).
Thus, fully distributed representations carry information
about syntactic structure of inputs, without being
syntactically structured.
I.e. they demonstrate a functional compositionality
by moving from structures to their components and
back again.
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But can distributed representations permit structure
sensitive operations? (systematicity)
(e.g. changing from active to passive).
Fodor and McLaughlin (1990):: to support structure
sensitive operations, representations must contain
explicit tokens of original constituent parts of complex
expression.
But can show that connectionist representations can
have property of systematicity (permitting structure
sensitive operations).
Chalmers (1990), trained a connectionist net to
transform representations of active sentences, to
passive sentences.
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Developed representations for both active and
passive sentences. Used RAAM nets (Pollack,
1990) to do this.
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Took the fully distributed representations from the
RAAM nets, for both active and passive sentences.
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Trained a net to translate from active sentences to
passive sentences.
Training complete: could input active sentences, extract
representation, and translate that into the
representation for a passive sentence, which could
be decoded into the passive sentence.
Also generalised to sentences that were not part of the
training set.
I.e. Connectionist representations did permit structure
sensitive operations, without being decoded into
symbols.
Summary of representation argument
Claimed by Fodor and Pylyshyn that
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Compositionality and structure sensitivity are
necessary to support cognitive processes.
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Only classical representations exhibit compositionality and structure sensitivity (systematicity)
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Only classical representations can support
cognitive processes.
BUT
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Can demonstrate that connectionist nets show
functional compositionality. Although representation
do not contain tokens, as symbolic representations
do, these tokens can be obtained from the
representations.
E.g. Can train nets to disambiguate sentences like the
following. Intervening hidden unit representation
does not contain explicit tokens, but output does.
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John saw money with the telescope.
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(John (saw money) (with the telescope))
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Can also show that connectionist representations
can support structure sensitive operations
(systematicity).
Chalmers (1990) Translating from active to passive
sentences using connectionist distributed
representations.
So counter-argument against claims that neural nets
cannot provide an account of cognition. So Neural
Computing can provide an alternative to the Strong
Symbol System Hypothesis.
But has yet to be shown if can provide a connectionist
account of all aspects of thought.
Smolensky, (1988): ‘.. It is far from clear whether
connectionist models …have adequate… power to
perform high level cognitive tasks..’
Connectionism: Does well at accounting for low-level
aspects of cognition: e.g. movement, pattern
recognition.
But Beliefs? Intentions?
Can Neural Computing provide an account of ‘Direct
Conscious Control’? (Norman, 1986)
E.g. consciously planning what to do, introspecting
about our thoughts, holding beliefs, making logical
inferences, early stages of learning a skill.
Possible that brain best modelled in terms of hybrid
system:
Connectionist account of lower level processes
Symbolic account of higher level processes.
Adaptive Behaviour, and Symbol Grounding Revisited
Three different approaches to Artificial Intelligence, two
of which we have already encountered many times:
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Symbolic AI or Traditional AI or GOFAI (Good Old
Fashioned AI)
 Neural Computing or Connectionism or Parallel
Distributed Processing
 Adaptive Behaviour or Behaviour-based Robotics
Adaptive Behaviour
Rodney Brooks, at MIT Artificial Intelligence Lab. (see
reference to Brooks on web page for the course)
Brooks, R.A. (1991) Intelligence without Reason. MIT
AI Lab Memo 1293, April 1991
An example: Allen, a reactive robot. (named after Allen
Newell?)
Sonar sensors, and odometer to keep track of distance
travelled.
Controlled by cable from off-board special purpose
computer.
Lowest level reactive layer; used sonar readings to
keep away from moving and static obstacles. - if an
obstacle is close, instead of bumping into it, stop.
Second level; random wandering. Every 10 seconds,
generate a movement in a random direction.
Third level: Look for a distant place, and move towards
it. Odometry can be used to monitor progress.
Three layers made it possible for robot to approach
goal, whilst avoiding obstacles.
Goal subsumption: switching control between the
modules is driven by the environment, not by a
central locus of control.
Robot heads for goal until sensors pick up information
that there is an obstacle in the way. The obstacle
avoidance module cuts in. Once the obstacle has
been avoided the goal-finding module takes control
again.
Robot can move around in the environment although it
does not build, or use, any map of that environment,
and is only driven by simple environmental cues.
Second example: Herbert (Herbert Simon?)
Wanders about an office environment, picking up coke
cans and returning them to start point.
Sensors: infrared ports, and laser 3D data.
Actuators: motors driving wheels, and manipulator arm
with sensors.
Subsumption architecture: several behaviour-generating
modules.
Modules include obstacle avoidance, wall following, and
recognition of coke cans.
Control of modules: Only suppression and inhibition
between alternative modules - no other internal
communication.
Each module connected to sensors and to arbitration
network which decides which competing action to
take.
Description of Herbert in action:
When following a wall, Herbert spots a coke can. The
robot locates itself in front of the can. The arm
motion is then begun - when can is detected with
sensors local to the arm, it is picked up.
Advantages; naturally opportunistic. If coke can put
right in front of Herbert, can collect it and return to
start, since no expectations about where coke cans
will be found. Can find coke cans in a variety of
locations, even if never found there before.
But….
Behaviour-based Robotics
Idea of building autonomous mobile robots.
New approach, where robots operate in the world, and
use ‘…highly reactive architectures, with no
reasoning systems, no manipulable representations,
no symbols, and totally decentralized computation’
(Brooks, 1991)
‘…I wish to build completely autonomous mobile agents
that co-exist in the world with humans, and are seen
by those humans as intelligent beings in their own
right. I will call such agents Creatures...’ (Brooks,
1991)
Brooks, R. (1991) Intelligence without Representation
Artificial Intelligence, 47, 139-159.
See “Elephants don’t play chess”, (1990 paper by
Brooks)
Brooks, Rodney A. (1990) “Elephants don’t play chess”.
In Pattie Maes (Ed) Designing autonomous Agents,
Cambridge, Mass: MIT Press.
Because elephants don’t play chess, no reason to
assume they are not intelligent.
Emphasis on kind of behaviour exemplified by
elephants, rather than on more abstract human
behaviours (e.g. games, speech recognition, problem
solving).
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A Creature must cope appropriately and in a timely
fashion with changes in its dynamic environment.
 A Creature should be robust with respect to its
environment: minor changes in the properties of the
world should not lead to total collapse of the
Creature’s behaviour; rather one should only expect
a gradual change in the capabilities of the Creature
as the environment changes more and more.
 A Creature should be able to maintain multiple goals
and, depending on the circumstances it finds itself in,
change which particular goals it is actively pursuing;
thus it can both adapt to surroundings and capitalize
on fortuitous circumstances.
 A Creature should do something in the world: it
should have some purpose in being.
Set of principles (Brooks, 1991)
 The goal is to study complete integrated intelligent
autonomous agents.
 The agents should be embodied as mobile robots
situated in unmodified worlds found round laboratory.
(embodiment).
 Robots should operate under different environmental
conditions - e.g. in different lighting conditions, when
sensors and actuators drift in calibration
(situatedness).
 Robots should operate on timescales commensurate
with timescales used by humans (situatedness).
Key Topics of Behaviour-based Approach
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Situatedness
Embodiment
(animal or insect) Intelligence
Emergence
Situatedness
A situated automation is a finite-state machine whose
inputs are provided by sensors connected to the
environment, and whose outputs are connected to
effectors.
The world is its own best model
Traditional AI, working in symbolic abstracted domain.
Problem solvers which are not participating in the world
as agents.
Dealing with model world - no real connection to
external world.
Alternative approach, to use a mobile robot which uses
the world as its own model, referring to information
from sensors rather than internal world model.
Representations are developed which capture
relationships of entities to robot.
Situated agent must respond in timely fashion to inputs;
but much information from the world.
Embodiment
The world grounds symbolic regress
Embodiment: Physical grounding of robot in real world.
According to Brooks (1991), embodiment is critical for 2
reasons.
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Only an embodied agent is validated as one that
can deal with real world.
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Only through a physical grounding can any internal
symbolic system be given meaning.
Brooksian view of Intelligence
Intelligence is determined by the dynamics of interaction
with the world
Some activities we think of as intelligent have only been
taking place for a small fraction of our evolutionary
lineage.
‘Simple’ behaviours to do with perception and mobility
took much longer to evolve.
Would make sense to begin by looking at simpler
animals.
- looking at dynamics of interaction of robot with its
environment.
Emergence
Intelligence is in the eye of the observer
Intelligence emerges from interaction of components of
the system.
Behaviour-based approach - intelligence emerges from
interaction of simple modules.
e.g. Obstacle avoidance, goal finding, wall following
modules.
Main ideas
 No central model maintained of world
 No central locus of control
 No separation into perceptual system, central system
and actuation system
 Behavioural competence improved by adding one
more behaviour specific network to existing network.
Crude analogy to evolutionary development
 No hierarchical development
 Layers or behaviours run in parallel
Criticisms?
This approach won’t necessarily lead to system capable
of more complex behaviours. A new controller is
needed for each task.
The experimenter is deciding on what modules to add,
and what environment and task the robot should be
exposed to. - not the same as evolution.
But in terms of evolution, new behaviours and new
mental structures are learnt in response to the
environment, not added by an experimenter.
Similarly, in the development of an individual, new
representational structures are developed in
response to the environment, not added by an
experimenter.
It would be more impressive if the robot learnt new
behaviour modules in response to the environment.
This possibility is discussed by Brooks (1991), but
has not yet been successfully tackled.
Emphasis in this approach on reacting to the
environment. And it is the case that apparently quite
sophisticated behaviours can result from simple
reaction to the environment. But representations are
needed for more complex tasks.
e.g. ‘Find an empty can and bring it back to the starting
point’
requires the formation of an internal representation
corresponding to a map. Need to provide an account
of the development of representations.
Symbol Grounding revisited
Traditional view: the language of thought (Fodor, 1975),
that The mind is a symbol system and cognition is
symbol manipulation.
Advocates of symbolic model of mind (e.g. Fodor, and
Pylysyn) argue that symbol strings capture what
mental phenomena such as thoughts and beliefs are.
Symbol system: symbols (arbitrary physical tokens)
manipulated on the basis of explicit rules.
Rule-governed symbol manipulation is based on syntax
of symbol tokens (not their meaning).
Symbols can be rulefully combined; primitive atomic
symbol tokens can be combined to form composite
symbol-token strings.
Resulting symbol-token strings can be given a meaning
- i.e. they are semantically interpretable.
BUT approach of assuming that mind is symbol system
can be criticised - in terms of symbol grounding.
A criticism of symbol systems is that symbol system
capable of passing the Turing Test will not be a
mind, because the symbols have no semantics
(meaning) (remember the Chinese Room)
From Searle (1997) The Mystery of Consciousness
 Programs are entirely syntactical
 Minds have a semantics
 Syntax is not the same as, not be itself sufficient for,
semantics
Therefore programs are not minds. QED
‘…It does not matter how well the system can imitate
the behaviour of someone who really does
understand, nor how complex the symbol
manipulations are; you cannot milk semantics out of
syntactical processes alone…’ (Searle, 1997).
Symbol grounding, as discussed by Stevan Harnard
Harnard, S (1990) The Symbol Grounding Problem.
Physical D 42, 335-346.
Copy of paper can be obtained from:
http://www.cogsci.soton.ac.uk/harnad/genpub.html
N.B. see the relevant websites for this course at
http://www.dcs.shef.ac.uk/~yorick/ai_course/aicourse.html
Computation consists of manipulation of meaningless
symbols.
For them to have meaning they must be grounded in
non-symbolic base.
Like the idea of trying to learn Chinese from a Chinese
dictionary.
Standard reply of symbolist (e.g. Fodor, 1980) is that
the meaning of the symbols comes from connecting
the symbol system to the world “in the right way”.
But how could this be done?
Harnard provides one possible solution:Symbols need
to have some intrinsic semantics or real meaning.
For Harnard, symbols are grounded in iconic
representations of the world.
e.g. consider the symbol “horse”
iconic representation of a horse, is a representation of
the shapes that horses cast on our retinas (i.e.
sensory surface of the eye).
From these iconic representations (many from individual
views of horses), we form a categorical
representation - that captures the features we need
to identify a horse.
Thus the name “horse” is grounded in iconic and
categorical representations, learned from experience.
Similarly, “stripes” is grounded in iconic and categorical
representations, learned from experience.
These symbols can be combined:
“zebra” = “horse” & “stripes”.
New symbol of zebra, is built up from the grounded
representations of “horse” and “stripes”, which gives
it meaning.
Harnard is proposing a hybrid system, in which thought
is assumed to be symbol manipulation, but the
symbols are grounded in iconic and categorical
representations of the world.
The problem with all this is WHICH symbols are so
grounded (peace, courage, Hamlet?)
Other solutions to symbol grounding problem have been
proposed.
Essential idea is that symbols need to be given some
meaning - need for grounding in meaningful
representations, to escape from circularity of defining
symbols in terms of symbols .
2 other (partial) solutions
 Adaptive Behaviour and embodiment
 Connectionist (neural computing)
 Both are robot-prosthetic arguments, the first without
representations and the second with implicit ones.
Symbol Grounding and Adaptive Behaviour
Would make sense to have symbols physically
grounded in real world.
Embodiment:
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Only an embodied agent is validated as one that
can deal with real world

Only through a physical grounding can any internal
symbolic system be given meaning.
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But adaptive behaviour people don’t want to have
symbols, grounded or not.
Suggests a new approach to grounding symbolic
representations - but as yet no clear account of how
symbols might emerge due to such interactions with
the real world.
Emphasis in work on behaviour-based robotics has
been on behaviour without representation.
New approach, where robots operate in the world, and
use ‘…highly reactive architectures, with no
reasoning systems, no manipulable representations,
no symbols, and totally decentralized computation.’
(Brooks, 1991)
Symbol Grounding and Neural Nets
Alternative idea: symbols are grounded in connectionist
representations.
Connectionist symbols = distributed representations =
patterns of activation across several units.
Connectionist symbols have internal structure. They are
not meaningless in the same way that atomic
symbols are.
This is a persuasive argument made by Chalmers
(1992)
Chalmers (1992) Subsymbolic computation and the
Chinese Room. In J. Dinsmore (Ed) The symbolic
and connectionist paradigms: closing the gap,
Lawrence Erlbaum: Hillsdale, New Jersey. pp 25-49.
Topics to think about
Mycin
 SHRDLU
 PARRY
 Expert systems
 Chinese Room
 Turing Test
 Weak & Strong AI

Neural networks
 Adaptive behaviour
 Symbolic AI
 Symbol grounding
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