Transcript Bioinspired programming

Bioinspired Computing
Lecture 1:
Overview & Biased History
Lecturing Staff
• Tony Cohn
– Topics 1,2, 5
• David Hogg
– Topic 3
• Eric Silverman
– Topic 4
• Slides based on last year’s
– Netta Cohen, Seth Bullock and guest lecturers
– Slides will be handed out
– All slides currently available, but these will be updated from last
year’s version as the weeks progress (so don’t assume things will
be exactly the same as last year!)
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What is this module all about?
Bio-inspired computing
Biological computation
Artificial Intelligence
Examples:
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The First Computer
Charles Babbage (1791-1871):
Inventor of difference engine –
recognised as direct ancestor
of the modern computer.
First (non-biological)
digital machine.
- is it bioinspired?
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Bioinspired programming
Some of the best bioinspired applications to
computing are the ones we would never associate
with biology.
Perhaps the most successful and pervasive
bioinspired application is…
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Alan Kay
by Scott Gasch
While studying at the University of Utah he learned about the
innovative Sketchpad program developed by Ivan
Sutherland and began programming in Simula. Borrowing
ideas from this and other systems, as well as from his
background in biology and mathematics, he formulated his
"biological and algebraic analogy.“
Kay postulated that the ideal computer would function like a
living organism; each "cell" would behave in accord with
others to accomplish an end goal but would also be able to
function autonomously. Cells could also regroup
themselves in order to attack another problem or handle
another function.
http://ei.cs.vt.edu/~history/GASCH.KAY.HTML
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How it works
• Autonomous cells
• Messages (data, sender & receiver addresses)
• Operations (contained in message)
• Cell differentiation (context-dependent functionality)
What good is this?
‘Building’ programs the way civil engineers design buildings.
programmers can create objects to mimic generic conceptual
building blocks: No need for a new language with each
application.
Inspiration & technical basis for the MacIntosh OS and
subsequent windowing based systems (NextStep, Microsoft
Windows 3.1/95/98/NT, X-Windows, Motif, etc.).
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AI programming
Bioinspired programming stands in stark contrast to familiar
AI programming languages. In 1959, John McCarthy
suggested a programming language with common sense.
Common sense: the ability to deduce for one’s self a
sufficiently wide class of consequences based on available
information.
Lisp (List Processing): logical operations represented as
manipulations of lists. Even functions and procedures are
defined as lists.
McCarthy’s goal in designing Lisp was - and still is - “to make
a machine that would be as intelligent as a human.”
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Principles of AI
Cast your mind back to AI22: Introduction to AI…
The Symbolic Search Hypothesis:
“A physical symbol system exercises
its intelligence in problem solving by
search – that is, by generating and
progressively modifying symbol
structures until it produces a solution
structure.”
Good solutions benefit from appropriate representations.
Good solutions rely on appropriate heuristics (rules of thumb).
These principles date from AI’s earliest beginnings…
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The Birth of AI
In the 1940s Alan Turing was already speculating on…
1. …the possibility of general computer intelligence –
abstract games: good initial tasks ‘requiring little
contact with the outside world’…
2. …the potential for a computer chess player –
search algorithms to used to find good moves…
3. …a way of deciding whether a computer was
intelligent – the Turing Test is a totally disembodied
interrogation (but a somewhat situated one)…
Artificial Intelligence is an attempt to simulate reasoning as:
abstract, formal, disembodied, symbol manipulation.
Note that Turing was also interested in BiC – “The chemical
basis of morphogenesis” (52)
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“Intelligence w/out Reason”
Rodney Brooks (“Intelligence Without Reason”): Critic of
the AI approach & strong proponent of embodiment and
situatedness in bioinspired computing (BIC).
AI, he claims, followed the abstract route due to
technological gaps in the 40s & 50s.
Today (1991), he says, it’s time to move on.
Brooks recognised that life-like systems are often
intelligent to some degree, yet reasoning is primarily
considered to be a human attribute. Rather than modelling
complicated human behaviour, why not start simple?
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“There are a number of key aspects characterizing
this style of work
• [Situatedness] The robots are situated in the world - they do not
deal with abstract descriptions but with the here and now of the
world directly influencing the behavior of the system.
• [Embodiment] The robots have bodies and experience the
world directly their actions are part of a dynamic with the
world and have immediate feedback on their own sensations.
• [Intelligence] They are observed to be intelligent - but the
source of intelligence is not limited to just the computational
engine. It also comes from the situation in the world, the signal
transformations within the sensors, and the physical coupling
of the robot with the world.
• [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.”
(Brooks 91)
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Why Not The Whole Iguana?
(Dennett 78)
• Traditional computing is task-oriented (vertical).
• To survive, animals have to be good across the board!
Walking
Sex
Memory
Chess
Iguana
Cricket
Horizontal
Vertical
Human
Ant
Rather than build parts of human intelligence, why not
build an entire much simpler intelligence?
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AI vs BIC
Overlap between AI and BIC is
considerable, but there are also
distinctions. BIC might be the
follower of AI. However, it could also
be regarded as a rebel.
• building (esp. human-like) intelligent machines with
common sense & decision making that naively
belies deductive logic.
• using them to improve our understanding of human
intelligence.
BIC • building life-like systems (utilising cartoon models of
biological systems, tasks & concepts).
• using them to improve our understanding of biology
AI
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Timetabling
This module has two lecture slots
Mondays 12:00 – 13:00 Roger Stevens LT24
Thursdays 15:00 – 16:00 Roger Stevens LT06
Demonstration slot allocated
Mondays 13:00 – 14:00 Roger Stevens LT03
Demonstration sessions will only be given some weeks
- notified in advance in lecture and/or newsgroup.
WATCH THE MODULE NEWSGROUP FOR UPDATES
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SHRDLU
By 1972, Terry Winograd’s invented a
simulated robot arm surrounded by blocks
of various shapes and colours on a table.
SHRDLU could…
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understand natural language instructions
ask questions to disambiguate these instructions
act & reason in a simulated “blocksworld” environment
answer questions about the world & its own reasoning
In Winograd’s words, it was “a computer program which
‘understands’ language in a limited domain.”
SHRDLU’s algorithms carried out model building,
search, planning and logical deduction.
See for example: http://hci.stanford.edu/~winograd/shrdlu/
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A Conversation with SHRDLU
> Pick up a big red block.
OKAY.
> Grasp the pyramid.
I DON’T KNOW WHICH PYRAMID YOU MEAN.
> What does the box contain?
THE BLUE PYRAMID AND THE BLUE BLOCK.
> I own blocks which are not red, but I don’t own
anything which supports a pyramid.
I UNDERSTAND.
> Do I own the box?
NO.
(adapted from Dreyfus’ 1979 What Computers Can’t Do)
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Is that really how our brain works?
John Von Neumann, father of modern high-speed
computers also thought about neuro-computation and
tried, for the first time, to construct a meaningful
comparison between brain and computer power.
Von Neumann argued that the brain must employ digital
computation. Figuring in the number of neurons,
connections, and estimates of computational speed and
statistical noise in the brain, he then concluded that the
brain could not be explained by logic alone.
In fact, he apparently postulated (and began writing) an
alternative theory but died soon after.
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The manuscript (published post mortem) ends as follows:
“The Language of the Brain is Not the Language of Mathematics …
whatever language the central nervous system is using, it is
characterized by less logical and arithmetical depth than what we
are normally used to … Consequently, there exist here different
logical structures from the ones we are ordinarily used to...
… whatever the system is, it cannot fail to differ considerably from
what we consciously and explicitly consider as mathematics.”
(John Von Neumann, The Computer and the Brain, 1958.)
In a recent commentary, Harold Morowitz writes:
“Von Neumann challenged the validity of the underlying
conceptualizations we use to study the brain and compare it with
computers. Yet, what is surprising, given the great esteem for John
Von Neumann, is that no one has taken up on his argument and
fully developed its consequences in the mind versus artificial
intelligence arguments that had been waging the last few years.” 19
Chess vs. Football
Chess
• Discrete
• Full Information
• Single Opponents
• Turn Taking
• Limited Options per Turn
• Intellectual, disembodied
• Optimal Strategy Exists?
• Demands General Intelligence?
• Formal, Analytical, Symbolic
Football
• Continuous
• Partial Information
• Heterogeneous Teams
• Continuous Confrontation
• Unlimited Options
• Physical, embodied
• No Optimal Strategy?
• Demands Specialist Skills?
• Dynamic, Physical, Reactive
•Can the problems faced by footballers be solved
through symbol processing and heuristic search?
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•Commentary v. playing?
Recent developments
Autonomous Mobile Robots
1990s: roboticists turn to building simple, robust, insectlike robots geared towards performing tasks that belie
their mediocre brains.
Brooks’ autonomous mobots embody the new philosophy:
– embedded, embodied, and unencumbered by intellect –
“fast, cheap, and out of control”
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Take home message...
What is BIC and what does it want to achieve?
Bio-inspired
computing
Biological
computation
or
Artificial
Intelligence
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Popular Reading
• “John McCarthy: The uncommon logician of common
sense”, in Shasha & Lazere (1995).
• “Alan Kay: A clear romantic vision”, ibid.
• “Computers and Brains”, in Morowitz (1997).
Additional Papers
• “Intelligence without reason” – Brooks (1991).
At home
• Reading: Brooks (for first Demonstration Session)
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Module Structure
• Key Topics include
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Multi-agent systems and swarm Intelligence
Artificial neural networks
Evolutionary design and genetic algorithms
Co-evolutionary design
Artificial life
Robotics and control
Interfacing biology with silicon
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Some basics
• This module is for you. Think, read, ask questions,
tinker, and have fun!
• Warning: Some of the material covered is, or recently
was, cutting edge.
• There will be programming, some biology, some
philosophy.
• There is a lot of material to cover in lectures, but…
lecture slides will not tell the whole story. You must
attend lectures and demo sessions
• The module courseware – BEAST – will be introduced
over the coming weeks. Help with C++, installation etc.
will be given in demo sessions.
• Originality and innovation will be rewarded. Show
enthusiasm, play, contribute original code.
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Resources
• Course Website: http://www.comp.leeds.ac.uk/ai23
– Lecture Slides
– Module Outline
– Reading Lists
– Assignments
– Useful Links
• Newsgroups:
local.modules.ai23 and
...ai23.talk
• Reading available in library, or via links on course page.
• Each other
– Talk about the material.
– Talk about the assignments.
– Help each other; feel free to work together, but
– Submit only you own personal original work.
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Assessment
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2-Hour Exam (60%) - answer 3 questions of 4
2 assignments (40%) of which
1st assignment – due 9am, 17 March (15%).
2nd assignment includes BEAST analysis &
programming project. Due 9am, 28 April (25%).
• Additional exercises may be given in demo sessions.
• Assignments & exam must be passed to pass module.
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