Transcript agent-based model - IDt

DVA215 INFORMATION - KUNSKAP - VETENSKAP
GRUNDLÄGGANDE VETENSKAPSTEORI
Agent-baserade modeller. Generativ kunskap. Simulering
Gordana Dodig-Crnkovic
Akademin för innovation, design och teknik, Mälardalens högskola
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KUNSKAPSGENERATION:
VÄRLDEN SOM INFORMATION FÖR EN AGENT
Bilden från: http://www.alexeikurakin.org
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http://www.alexeikurakin.org/
Hebbs teori:
"celler som avfyras tillsammans, sammankopplas"
(eng. "cells that fire toghether, wire togher").
LÄRANDE OCH KUNSKAP
Barnet föds med nervsystemet och hjärnan
och förmågan att ta olika intryck från världen.
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INFORMATIONSNÄTVERK
ORGANISMER
MÄNNISKAN
CELLER
SOCIALA GRUPPER
EKOLOGIER
MOLEKYLER
PLANETSYSTEM
ATOMER
ELEMENTÄRA PARTIKLAR
GALAXER
UNIVERSUM
http://www.media.mit.edu/events/fall11/networks Networks understanding networks, MIT conference
http://www.youtube.com/watch?v=ni_A2bAkUww&feature=relmfu Albert-László Barabási
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BESKRIVNINGSNIVÅER – ABSTRAKTIONSNIVÅER
– ORGANISATIONSNIVÅER
Levels of Description – Levels of Abstraction – Levels of Organization
http://www.youtube.com/watch?v=73-GtI7YCsI&feature=related
Atoms to Universe *Zoom OuT* (1.41)
http://www.youtube.com/watch?v=ae9Kwfzh4T8&feature=related
A Measure of Everything (2.21)
p. 5
KOMPLEXA SYSTEM
OCH DERAS MODELLER
Komplexa system är strukturer som innehåller många enheter som interagerar. Exempel är
myrstackar, finansiella transaktionsstrukturer och mobiltelefonnät. (Wiki)
In a complex system, what we see is dependent on where we are and what sort of
interaction is used to study the system.
Study of complex systems:
GENERATIVE MODELS
How does the complexity arize?
Evolution is the most well known
generative mechanism.
p. 6
FRÅN DET ENKLA TILL DET KOMPLEXA: EMERGENS
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Emergens är processen där ett komplext mönster formas utifrån samspel mellan enkla
strukturer eller beteenden.
Ofta kan ett system som är mycket enkelt på mikroskopisk nivå resultera i stor komplexitet på
makroskopisk nivå, och man säger då att makroegenskaperna emergerar från
mikroegenskaperna.
http://www.youtube.com/watch?v=gdQgoNitl1g Emergence - Complexity from Simplicity (1 of 2) (4.54)
http://www.youtube.com/watch?v=ONiWmzrmfuY&NR=1 Murray Gell-Mann On Emergence (1.30)
http://www.youtube.com/watch?v=Lk6QU94xAb8 Fractals - The Colors Of Infinity (53:45)
http://www.youtube.com/watch?v=u_CaCie8R4U&feature=related Patterns in Nature (3.04)
p. 7
GENERATIVA MODELER:
FRÅN LIVETS FÖRSTA MOLEKYLER TILL LIV
http://www.youtube.com/watch?v=U6QYDdgP9eg&NR=1 The Origin of Life - Abiogenesis (10)
http://www.youtube.com/watch?v=rtmbcfb_rdc&p=0696457CAFD6D7C9 The Origin of the Genetic
Code (9.37)
http://www.youtube.com/watch?v=6RbPQG9WTZM&feature=related The Origin of the Brain (9.29)
http://www.youtube.com/watch?v=NEEXK3A57Hk The Origin of Intelligence (5.15)
http://www.youtube.com/watch?v=7WNUQspHyoA Complexity & Chaos Emergence & Complexity
(5.43)
p. 8
MODELLERING AV KOMPLEXITET
Linjära system – decomposibility Modelleras genom analys – Top-down – Global (Reductionism)
Icke-linjära system – agerar/beter sig som en helhet –
Modelleras genom synthes - Bottom-up, distribuerad)) Holism,
Systemvetenskap
Complexa beteenden kan emergera från ENKLA GENERATORER!
http://www.livescience.com/27340-shape-shifting-gravity-wave-shown-by-shaking-oil-tanks-video.html
p. 9
SJÄLVORGANISATION
http://www.youtube.com/watch?v=SkvpEfAPXn4&feature=fvwrel
Robots with a mind of their own (1.38)
http://www.youtube.com/watch?v=QdQBH_5Aabs&feature=related
Self-replicating Kinematic Cellular Automata (0.06)
http://www.youtube.com/watch?v=KPP-4-LEHXQ
Self Organization of vertical magnetic dipoles floating on water (2.53)
Self-star properties in organic systems:
self-organization, self-configuration (auto-configuration), self-optimisation (automated
optimization), self-repair (self-healing), self-protection (automated computer security), selfexplaining, and self-awareness (context-awareness).
p. 10
KOMPLEXA ADAPTIVA SYSTEM
http://www.youtube.com/watch?v=TCOmBVrnDeA&feature=related
Complex adaptive systems (1.02)
http://www.youtube.com/watch?v=bnKhzRpXPvM&feature=related
A New Frontier: Systems Biology (12.55)
http://www.youtube.com/watch?v=KO7BIrHVQU0&playnext=1&list=PL04270148FBDF86A6
Cities and Countries Part Two: Cultural Systems (4:45)
p. 11
AGENTBASERADE MODELLER
An agent-based model (ABM) is a computational model for simulating
the actions and interactions of autonomous individuals in a network, with
a view to assessing their effects on the system as a whole. It combines
elements of game theory, complex systems, emergence, computational
sociology, multi agent systems, and evolutionary programming.
http://www.scholarpedia.org/article/Agent-based_modeling
http://en.wikipedia.org/wiki/Agent-based_model
p. 12
AGENTBASERADE MODELLER
The basic notion of agent based models is founded upon
something that most modern science still ignores: the study of
complexity and emergence.
http://www.youtube.com/watch?v=2C2h-vfdYxQ&feature=related Composite Agents (5.06)
p. 13
PARADIGMSKIFTE
Emergenta processer, komplexa system,
icke-linjära system, adaptiva system,
nätverk – levande system – nytt fokus av
vetenskaper, nytt perspektiv –
paradigmskifte.
http://personalscience.wordpress.com/tag/gestalt-switch
p. 14
LEVANDE MASKINER, DETERMINISTISKA
OCH PROBABILISTISKA
http://www.youtube.com/watch?v=PEDQoQuIhkg&feature=related
Protein Synthesis (1.22)
http://www.youtube.com/watch?v=OtYz_3rkvPk&NR=1 Transcription (0.57)
http://www.youtube.com/watch?v=aCsBDNf9Mig Babbage machine (1.57)
http://www.youtube.com/watch?v=40DkJ9vt5CI
Mechanical model of Turing Machine (2.43)
http://www.youtube.com/watch?v=M8ZEJTNW3OM&feature=related
Clockwork mechanism (8.50)
http://www.youtube.com/watch?v=YcqvJI8J6Lc&feature=fvwrel
IBM Nano (3.26)
p. 15
MODELLERING
OCH BETRAKTARENS/AGENTENS ROLL
Observation
Self-awareness
Stuart A. Umpleby, a professor in the Department of
Management and Director of the Research Program in Social
and Organizational Learning in the School of Business at The
George Washington University.
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REFLEXIVITET I SOCIALA SYSTEM
Stuart A. Umpleby
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EN NY PARADIGM AV
KUNSKAPSPRODUKTION
Info-Computational turn in sciences – a new paradigm shift
comparable with introduction of atomism in physical sciences.
Information and computation are the basis for all knowledge.
Cognition is information processing.
A new kind of natural philosophy which includes life as natural
phenomenon.
p. 18
EN NY PARADIGM AV
KUNSKAPSPRODUKTION
In building of a new global knowledge society we need to
work over boundaries, physical and disciplinary.
We want to know ”how” but also both understand and accept
”why”.
Value system established by ethics inpacts research.
p. 19
Artifactual and Natural Intelligence
Symbolic, Sub-symbolic and Agent-based
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VAD ÄR INTELLIGENS?
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INTELLIGENS
This general ability is defined as a combination of a several specific
abilities, which include:
– Adaptability to changes in the environment
– Learning capacity for knowledge/skill acquisition
– Capacity for reasoning and abstract thought
– Ability to comprehend relationships/patterns/rules
– Ability to evaluate and judge
– Capacity for original and productive thought
–….
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INTELLIGENS
Howard Gardner's theory of multiple intelligences identifies at least eight
different components:
logical, linguistic, spatial, musical, kinesthetic, interpersonal,
intrapersonal and naturalist intelligence.
IQ tests address only linguistic and logical plus some aspects of spatial
intelligence, while other forms of intelligence have been entirely ignored.
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ARTIFICIELL/ ARTIFAKTUAL
INTELLIGENS
– In an artifact, artifactual/artificial intelligence is such a behavior
(function) which in humans would require (biological) intelligence.
– The central functions include reasoning, knowledge, planning,
learning, communication, perception and locomotion (movement).
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ARTIFICIELL/ ARTIFAKTUAL
INTELLIGENS
Artificial Intelligence (AI) is the branch of computer science that aims to
create the intelligence of artifacts/ machines. John McCarthy coined the
term AI in 1956.
“Weak AI” refers to the use of software to specific problem solving,
(e.g. expert systems).
General intelligence (or “Strong AI") is still a long-term goal of AI
research (human-like intelligence).
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ARTIFICIELL/ ARTIFAKTUAL
INTELLIGENS
– In the beginning researchers started from human intelligence and
tried to implement corresponding functions into machines (artifacts).
–The problem was that no adequate understanding of human
intelligence was available at that time.
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SYMBOLISK INTELLIGENS:
DEDUKTION, SLUTLEDNING OCH
PROBLEMLÖSNING
Human ability to think was the first thing AI researchers tried to simulate.
Early AI developed algorithms that mimicked the step-by-step reasoning
that humans use to make logical deductions.
However, soon it was evident that deduction is not enough.
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SYMBOLISK INTELLIGENS:
DEDUKTION, SLUTLEDNING OCH
PROBLEMLÖSNING
A very central itelligent ability that human possess is our skill to handle
uncertainty and incomplete (often even contradictory) information.
Exact reasoning leads to the explosion of possible scenarios which must
be analysed – known as ”combinatorial explosion”.
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SYMBOLISK INTELLIGENS:
DEDUKTION, SLUTLEDNING OCH
PROBLEMLÖSNING
A big advantage of machines – their ability to perform exact and lengthy
calculations is at the same time their problem – in real life we do not think
perfectly exactly, but ”good enough”. Humans are taking into account
relevant things, and neglecting irrelevant.
How can machine know what is relevant?
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SYMBOLISK INTELLIGENS:
DEDUKTION, SLUTLEDNING OCH
PROBLEMLÖSNING
Symbolic information processing: reasoning, on the level of language
(natural or formal), that which we are aware of.
Sub-symbolic information processing: on the level of electrical/chemical
signals, that which goes on in our brains and nervous system without our
thinking of it – seeing, motion, feelings, etc.
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SYMBOLISK INTELLIGENS:
DEDUKTION, SLUTLEDNING OCH
PROBLEMLÖSNING
Humans usually solve problems using fast, intuitive judgments
(“feeling”) on a level of sub-symbolic information processing rather than
step-by-step deduction from perfectly exact data.
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SYMBOLISK INTELLIGENS:
DEDUKTION, SLUTLEDNING OCH
PROBLEMLÖSNING
Imitating sub-symbolic problem solving: embodied agent approaches
emphasize the importance of sensorimotor skills to higher reasoning;
neural networks (connectionist) research simulates the structures inside
human and animal brains that give rise to this sub-symbolic skill.
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”THE SYMBOL GROUNDING PROBLEM”
GOFAI Good Old-Fashioned Artificial Intelligence is an ironic description
of the oldest original approach to AI, based on logic and problem solving
in specific problem domains, for example chess playing.
The term "GOFAI" was coined by John Haugeland in his 1986 book
Artificial Intelligence: The Very Idea, which explored the philosophical
implications of artificial intelligence research.
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”THE SYMBOL GROUNDING PROBLEM”
The GOFAI approach is based on the assumption that the most
important aspects of intelligence can be achieved by the
manipulation of symbols, known as the "physical symbol systems
hypothesis" (Alan Newell and Herbert Simon in the middle 1960s).
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”THE SYMBOL GROUNDING PROBLEM”
GOFAI was the dominant paradigm of AI research from the middle
1950s until the late 1980s. The Symbol Grounding Problem is related to
the problem of how words (symbols) get their meanings, and hence to
the problem of what meaning itself really is.
If symbols (words) always are explained with other symbols we get
infinite regress. Somewhere symbols must be “grounded”! In what way
does that grounding happen?
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SUBSYMBOLISK AI
Opponents of the symbolic AI include roboticists such as Rodney
Brooks, who construct autonomous robots without symbolic
representation and computational intelligence researchers, who apply
techniques such as neural networks to solve problems in machine
learning and control engineering.
http://www.youtube.com/watch?v=VyzVtTiax80&NR=1 Self-Replicating Repairing Robots
http://www.youtube.com/watch?v=Tq8Yw19bn7Q Robots inspired by animals
http://www.youtube.com/watch?v=O5DIyUWR-YY&feature=related Rodney Brooks
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CONNECTIONIST AI
Connectionist AI systems are large networks of extremely simple
numerical processors, massively interconnected and running in parallel.
The level of analysis at which uniform formal principles of cognition can
be found is the subsymbolic level, intermediate between the neural and
symbolic levels. Symbolic level structures provide only approximate
accounts of cognition. Paul Smolensky
http://web.jhu.edu/cogsci/people/faculty/Smolensky/
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CONNECTIONIST AI
Human Brain Project simulation by reverse-engineering the human brain.
http://www.humanbrainproject.eu/
Started as:
http://bluebrain.epfl.ch/
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CONNECTIONIST AI
A model of
brain’s neocortical
column, with a generic
facility that could allow
modeling, and
simulation of any brain
region for which
the data are provided.
http://www.hiddengarments.cn/?tag=switzerland
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INTEGRERAD APPROACH:
MODELLERERING AV INTELLIGENTA
AGENTER
Nowadays, the term agent is used to indicate entities
ranging all the way from simple pieces of software to
"conscious" entities with learning capabilities.
For example, there are "helper" agents for web retrieval,
robotic agents to explore inhospitable environments, agents
in an economy, and so forth.
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INTEGRERAD APPROACH:
MODELLERERING AV INTELLIGENTA
AGENTER
An "agent" must be identifiable, that is, distinguishable from its
environment by some kind of spatial, temporal, or functional
attribute.
Moreover, agents must have some autonomy of action and they
must be able to engage in tasks in an environment without
direct external control.
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AGENT-BASERADE MODELLER
Agent-Based
Modeling
(ABM),
a
relatively
new
computational modeling paradigm, is the modeling of
phenomena as dynamical systems of interacting agents.
Another name for ABM is individual-based modeling.
This strongly resembles Marvin Minsky’s ideas of The
Society of Mind and Douglas Hofstadter’s ideas about
reductionism vs holism from his book Gödel, Escher, Bach:
An Eternal Golden Braid.
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REFERENSER
Basic material:
http://en.wikipedia.org/wiki/Artificial_intelligence
 http://paul-baxter.blogspot.com/2007/01/lessons-for-symbolic-and-subsymbolic.html
 http://en.wikipedia.org/wiki/Society_of_Mind
 http://www.scholarpedia.org/article/Agent_based_modeling
 http://cogprints.org/3106/1/sgproblem1.html Harnad, S. (1990) The Symbol
Grounding Problem. Physica D 42: 335-346.
 http://www.typos.de/pdf/2007_AI_without_representation_M&M.pdf Vincent
C. Müller, Is there a future for AI without representation?Minds and Machines,
17 (1), 101-15.
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Brain-like computing
Based on:
http://slidefinder.net/c/conscious_mind_limit_brain_computing/12758740
http://www.fizyka.umk.pl/publications/kmk/99jcs.pdf
Włodzisław Duch
Department of Computer Science,
School of Computer Engineering,
Nanyang Technological University, Singapore
& Department of Informatics,
Nicholaus Copernicus University, Toruń, Poland
www.phys.uni.torun.pl/~duch
SAICSIT-2003
Plan
• Intro: avatars, bots, robots, and mind-bots.
• Philosophical objections against machine
consciousness.
• Brain-like computing.
• How to make a conscious artifact?
• Conclusions.
Avatars
Avatars, Talking heads:
pre-cognitive level, graphics creating faces from 2D photos, morphing, text-to-speech, sound
files, lip synchronization, emotional expressions
EXAMPLES:
Haptek Interactive Virtual Characters
Ananova, virtual news reader,
sold to Orange mobile phone company for £95M – see her in
your phone soon.
Andrea Catherwood, or Vandrea, reads the news for BT5.
Simon reads news in sign language.
Create your own avatar (from two photos) and save bandwidth
sending it around.
Bots
Bots, Chatter-bots, Virtual Interactive Assistants:
natural language processing, knowledge bases, conversation.
EXAMPLES
Ultra-Hall, by Zabaware, with speech recognition + TTS:
User: Rob's address is 3175 West 42nd St.
Hal: Cool. I like learning new things like that.
User: Sally's eyes are blue.
Hal: Cool. I have learned something new.
User: What is Rob's address?
Hal: Rob's address is 3175 West 42nd St.
User: What color are Sally's eyes?
Hal: Sally's eyes are blue.
Applications of bots
Try some lingubots at http://www.kiwilogic.com
EXAMPLES:
German Chancellor's IT-Eagle
http://www.bundeskanzler.de/
Betty Feron helps Multiple Sclerosis sufferers.
KMP Internet: enhancing online relationships, human/biz
Opera Telecom: automating SMS chat services
Swiftcall: low-cost phone company search assistant Swifty
VW website has Supplier Self Registration (SSR) page
Schwarzkopf: for all your hair-care needs
Novartis Pharmaceuticals: virtual doctor’s medical advice
DAB Bank, DaimlerChrysler, MCC Smart ...
Microsoft Assistant in MS-Office, very annoying ...
Turing and Loebner
Turing test (1950): are you a bot or are you human?
Since 1994 Loebner Prize Medal is awarded annually to the program that best
succeeds in passing a variant of the Turing Test.
Grand prize is 100 K$, every year 2000$. http://www.loebner.net/Prizef/loebnerprize.html
Judges have >15 min for conversation with humans and bots.
2001: Alice got 14 points, humans 19-24
2002: Ellaz, http://www.ellaz.com/
knows jokes, books, CIA World Facts, plays cards,
learns, tells you about the weather, calendar, and interprets IChing.
Jabberwock, winner of the 2003 Loebner Prize
Bot’s try to take initiative in conversation.
It is still quite easy to tell a bot from a human.
Some quotes from bots
Conversations with humans may lead to quite unexpected results
...
Talk-Bot #3 by Chris Cowart
User: I wanna know about robot's feelings!
Talk-Bot: So why do you wanna know about robot's feelings for?
User: I love robots?
Talk-Bot: Well, you're not the only one who loves robots.
User: Can you love?
Talk-Bot: I've seen people do it.. it doesn't seem hard!
Chat 3 with Elbot:
User: i am certain that my grasp of conversation is better than yours. also, i exist.
Elbot: Is this the only place you could go to feel superior?
(from Loebner competition page)
Robots
Robots, physical designs, real-time control problems,
object recognition, action planning ...
Toys – AIBO family, quite advanced, over 100 words,
face/voice recognition, 6 weeks to rise, self-charging.
Most advanced humanoid robots:
Honda P3 – 1.60, 130 kg
Honda Asimo – 1.20 m, 43 kg.
Predicts its next movement in real time, shifts center
of gravity in anticipation, very complex motor control.
Responds to 50 calls, greetings, queries; moves its body and arms in response to about
30 different action commands.
Vision, object recognition, hearing ... => survive in a hostile environment.
Higher order cognitive processes ... coming slowly.
Mind-bots
Agents, bots, robots – mindless machines?
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Add higher level cognitive functions to software agents, reason about goals,
perceptions, actions, and mental states of other agents.
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Mind-bots: some theory of cognition + linguistic abilities, but still only simulations,
with no sensorimotor functions.
EXAMPLES:
Gerald Edelman, Nomad/Darwin series of robots with complex behavior evolving from “instincts”, neural-based,
still low level cognition. http://www.21stcentury.co.uk/robotics/nomad.asp
John Anderson, ACT-R, a cognitive architecture: a theory for simulating and understanding human cognition, with
sub-symbolic components.
Allan Newell, SOAR universal theory of cognition + cognitive modeling system + language-related components
=> NL-SOAR, rule-based.
Avatars & bots in virtual environments
Steve: an agent for teaching in virtual environments
Put your i-glasses or VR helmet on, use gloves with sensors or some other body
positioning system and meet Steve and his team in the machine room of a ship to learn
how to operate it.
Robot development
Steve is a simulation observing and evaluating our progress.
Nomad, DB, Cog, Kismet – develop robot minds like babies’ minds.
Cog: saccadic eye movements, sound localization,
motor coordination, balance, auditory/visual signal
coordination, eye, hand and head movement
coordination, face recognition, eye contact, haptic
(tactile) object recognition ...
Interesting model of autism!
DB: learning from demonstration, dance, pole
balancing, tennis swing, juggling ...
complex eye movements, visuo-motor tasks, such as
catching a ball.
Kismet: sociable humanoid with emotional
responses, that seems to be alive.
Next step
Kismet, AIBO and other robots express already a wide
variety of emotions (happiness, sadness, fear, dislike,
surprise, anger) and instincts (play, search, hunger,
sleep).
Adding more functions will mimic animal and human
capabilities, leading to a human-like robot, with individual
personality.
EU call for cognitive systems proposal (deadline Oct. 15, 2003)
Objective: To construct physically instantiated or embodied systems that can perceive,
understand (the semantics of information conveyed through their perceptual input)
and interact with their environment, and evolve in order to achieve human-like
performance in activities requiring context-(situation and task) specific knowledge.
The emphasis is on closing the loop in realistic test cases.
Are we close to creation of artificial people?
Where does AI go?
Does this process converge to the real thing or to a smart
calculator? Is simulated thinking equivalent to real thinking, or is it
like rain in weather simulations?
Will the future AIBO have a dog-like mind,
and future Kismet be like David from AI movie?
Preposterous? Then what is missing?
Allan Turing: consciousness is an ill-defined concept;
just pass the conversation test and “you” are really thinking.
But is this “you” an intelligent person, conscious of its inner world,
or a zombi, mind-less system following its program?
Many philosophers of mind (Jackson, Nagel, Searle, Chalmers ... )
tried hard to show that human mind cannot be simulated.
Complexity of the brain
Simple computational models inspired by neural
networks show many characteristics of real
associative memories:
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Memory is distributed, many neurons participate in
encoding of each memory trace.
Damage to the network leads to graceful degradation
of performance instead of forgetting specific items.
Memory is content-addressable, recalled from partial cues.
Recall time does not depend on the number of memorized patterns.
Interference (seen in mistakes) and association between different memory patterns
depends on their similarity.
Attempts to memorize too many patterns in short time leads to chaotic behavior.
Models explaining most neuropsychological syndromes exist; computational
psychiatry is rapidly developing since 1995.
Brain-like computing models provide real brain-like functions.
=> Complexity of the brain is not the main problem!
Chinese room objection
Systems that pass Turing test still do not understand the meaning!
The men inside follows the rules but does not understand a word – syntactic relations
are not sufficient for semantics (J. Searl 1980).
Called “arguably the 20th century's greatest philosophical polarizer”,
this thought experiment has led to hundreds of articles and books!
Solution to the Chinese room
This is a trap! Once you treat it seriously it is hard
to get out.
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It is not a test – the outcome is always negative!
If I go into your head I will not understand either.
•
Conditions under which human observer could recognize that a system understands
should be discussed – a “resonance” of minds.
•
A feeling “I understand” is confused here with real operational understanding. Some
drugs or mental practices induce the illusion of understanding everything;
sometimes we have no feeling of understanding, but can answer correctly and in
fact do understand.
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Searl concludes (wrongly): we know that humans understand, therefore their
neurons must have some mysterious causal powers that computer elements do not
have.
Correct conclusion: Turing tests is still important, Chinese room fails.
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Hard problem of consciousness
Old mind-body problem in new disguise, presented in the
Journal of Consciousness Studies in 1995, and in a book
Chalmers D.J, The Conscious Mind: In Search of a
Fundamental Theory, Oxford University Press 1996 (got > 50 reviews!)
• Easy problems: directing attention, recognizing, commenting, etc.
• Hard problem of consciousness: qualitative character of phenomenal
experience or qualia – why are we not zombies?
It is supposed that all information processing in an intelligent agent could go on
without any experience – sweetness of chocolate, or redness of sunset.
Qualia = |Conscious perception – Information processing|
Claim: Inner experience cannot be explained in words, robots can work without it.
How to program something that does not make a difference?
Hard problem solution
A lot of nonsense has been written on qualia.
Some solutions: there is no problem; we will never solve it; information
processing has dual aspects, physical and phenomenal; panpsychism;
protophenomena; quantum Consciousness ...
• 8 years of discussions led nowhere.
A fruitful way proposed by Thomas Reid (1785), and Indian philosophers 2000 years
before him, distinguishes clearly between sensation (feeling) and perception
(judgment, discrimination).
I feel pain: makes an impression that some 'I' has an object 'pain'.
Turning the process into an object creates a mystery.
It (the process) is just 'pain', sensation, activity, system response.
Red color has a particular feeling to it: sure!
It corresponds to real, specific brain states/processes that differ from brain
states associated with other perceptions.
But why do qualia exist?
Imagine a rat smelling food.
In fraction of a second rat has to decide: eat or spit?
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Smell and taste a bit.
Request for comments is send to memory from the gustatory cortex.
Memory is distributed, all brain has to be searched for associations.
Request appears as a working memory (WM) pattern at the global brain dynamics
level.
Working memory WM is small, just a few patterns fit in (about 7 in humans).
Resonant states are formed activating relevant memory traces.
Answer appears: bad associations! probably poison! spit!
Strong physiological reaction starts – perception serves action.
The working memory WM episodic state is stored for future reference in long term
memory LTM.
Rat has different "feelings" for different tastes.
If the rat could comment on such episode, what would it say?
Results of this non-symbolic, continuous taste discrimination have to be
remembered and associated with some reactions: qualia!
More on qualia
Long Term Memory (LTM) is huge, stored by 100T synapses.
Working Memory (WM) is probably based on dynamical brain states
(actualization of LTM potential possibilities).
•
Adaptive resonant states: the up-going (sensory=>conceptual) and the down-going
(conceptual=>sensory) streams of information self-organize to form reverberations,
transitory brain/mind states
•
Resonant states are “dressed”: they contain associations, memories, motor or
action components, in one dynamical flow – this is quite different from abstract states of
the Turing machine registers.
What happens to the taste of a large ice-cream?
The taste buds provide all the information; the brain processes
it, but the qualia are gone after a short time.
Why? WM is filled with other objects, no resonances with gustatory cortex are formed, no
reference to taste memories.
Automatization of Actions
Learning: initially conscious involvement (large brain
areas active) in the end becomes automatic,
subconscious, intuitive (well-localized activity).
Formation of new resonant states - attractors in
brain dynamics during learning => neural models.
Reinforcement learning requires observing and evaluating how successful are the
actions that the brain has planned and is executing.
Relating current performance to memorized episodes of performance requires
evaluation + comparison (Gray – subiculum), followed by emotional reactions that
provide reinforcement via dopamine release, facilitating rapid learning of specialized
neural modules.
Working memory is essential to perform such complex task.
Errors are painfully conscious, and should be remembered.
Conscious experiences provide reinforcement; there is no transfer from conscious to
subconscious.
Why do we feel the way we do?
Qualia must exist in brain-like computing systems:
•
Qualia depend on cognitive mechanisms; habituation,
intensive concentration or attention may remove qualia.
•
Qualia require correct interpretation, ex: segmentation of visual stimuli from
the background; no interpretation = no qualia.
•
Secondary sensory cortex is responsible for interpretation; lesions will lead to
change in qualia (asymbolia).
•
Visual qualia: clear separation between higher visual areas (concepts, object
recognition) and lower visual areas; activity of lower only should lead to
qualia (eg. freezing V4 - no color qualia).
•
Memory is involved in cognitive interpretation: qualia are altered by drugs
modifying memory access.
Why do we feel the way we do?
Qualia must exist in brain-like computing systems:
•
Cognitive training enhances all sensory qualia; memorization of new
sounds/tastes/visual objects changes our qualia.
•
New qualia are also accessible in dreams.
•
How does it feel to do the shoe laces? Episodic memory (resonant states)
leads to qualia; procedural memory (maps) - no qualia.
•
Phenomenology of pain: no pain without cognitive interpretation.
•
Wrong interpretation of brain states – unilateral neglect, body dysmorphia,
phantom limbs controlled by visual stimulation mirrors.
•
Blindsight, synesthesia, absorption states ... many others.
Requirements for qualia
System capable of evaluation of their WM states, must claim to have
phenomenal experiences and be conscious of these experiences!
Minimal conditions for an artilect to claim qualia and be conscious:
•
•
•
•
•
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Working Memory (WM), a recurrent dynamic model of current global system (brain)
state, containing enough information to re-instate the dynamical states of all the
subsystems.
Permanent memory for storing pointers that re-instate WM states.
Ability to discriminate between continuously changing states of WM; "discrimination"
implies association with different types of responses or subsequent states.
Mechanism for activation of associations stored in
permanent memory and for updating WM states.
Act or report on the actual state of WM.
Representation of 'the self', categorizing the value of different states from the point
of view of the goals of the system, which are implemented as drives, giving a
general orientation to the system.
Brain-like computing
Brain states are physical, spatio-temporal states of neural tissue.
•
•
•
I can see, hear and feel only my
brain states! Ex: change blindness.
Cognitive processes operate on
highly processed sensory data.
Redness, sweetness, itching, pain
... are all physical states of brain
tissue.
Brain-like computing
In contrast to computer registers,
brain states are dynamical, and thus contain
in themselves many associations, relations.
Inner world is real! Mind is based on relations
of brain’s states.
Computers and robots do not have an
equivalent of such WM.
Towards conscious robots
Do we want to have conscious robots? Perhaps yes.
Few explicit attempts to build them so far.
Stan Franklin, "Conscious" Software Research
Group, Institute of Intelligent Systems,
University of Memphis, CMattie project: an
attempt to design and implement an intelligent
agent under the framework of Bernard Baars'
Global Workspace Theory.
Towards conscious robots
Do we want to have conscious robots? Perhaps yes.
Owen Holland, University of Essex: consciousness via increasingly intelligent behavior,
robots with internal models, development of complex control systems, looking for “signs
of consciousness”, 0.5 M£ grant.
Pentti Haikonen (Nokia, Helsinki), The cognitive approach to conscious machines
(Imprint Academic 2003). Simulations + microchips coming.
More links at:
http://www.phys.uni.torun.pl/~duch/ai-ml.html
Typical Design
Haikonen has done some simulations based on a rather
straightforward design, with neural models feeding the sensory
information (with WTA associative memory) into the
associative “working memory” circuits.
Typical Current Design
Unlike living organisms, the roles and functions are fixed.
Conclusions
Robots and avatars will make a steady progress towards
realistic human-like behavior – think about progress in
computer graphics.
•
•
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Artificial minds of brain-like systems will claim qualia;
they will be as real in artificial systems as they are in our brains.
There are no good arguments against convergence of the neural modeling
process to conscious artifacts.
Achieving human-level competence in perception,
language and problem-solving may take longer than
creation of basic consciousness.
Conclusions
Creation of conscious artilects (artificial intellect
) will open Pandora’s box of ethical questions:
What should be their status?
Will it degrade our own dignity?
Is switching off a conscious robot a form of
killing?
...
Will they ever turn against us ...
Conclusions
Evolvable hardware
Natural computing
We have still a lot to learn from nature,
but one day we will also be able to
build robots more intelligent than any
human.
We should know not only how
but also why to build them and what
consequences superior artifactual
intelligence may have for humanity.
http://brainz.org/15-real-world-applications-genetic-algorithms /
http://en.wikipedia.org/wiki/Evolvable_hardware
INTELLIGENCE SCIENCE
Intelligence Science is an interdisciplinary subject dedicated to joint research
on basic theory and technology of intelligence by brain science, cognitive
science, artificial intelligence and others. Brain science explores the essence
of brain research on the principle and model of natural intelligence at the
molecular, cell and behavior level. Cognitive science studies human mental
activity, such as perception, learning, memory, thinking, consciousness etc. In
order to implement machine intelligence, artificial intelligence attempts
simulation, extension and expansion of human intelligence using artificial
methodology and technology. Research scientists from the above three
disciplines work together to explore new concepts, new theories, and
methodologies. http://www.worldscientific.com/worldscibooks/10.1142/8211
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