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Research Thinking and Writing
Toolbox
DVA403
Artifactual and Natural Intelligence
Symbolic, Sub-symbolic and Agent-based
Gordana Dodig Crnkovic
School of Innovation, Design and Engineering, Mälardalen University, Sweden
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Thinking and Intelligence
In our course, Research Thinking and Writing Toolbox,
research thinking, along with writing, is a central topic.
Research thinking as thinking in general are based on a
set of abilities that we call intelligence, so let us start from
learning some basics about how we today understand
intelligence, the ways we think, acquire knowledge and
produce knowledge.
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What is Intelligence?
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Intelligence
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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Intelligence
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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Artifactual/Artificial
Intelligence
– 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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Artifactual/Artificial
Intelligence
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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Artifactual/Artificial
Intelligence
– 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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Symbolic Intelligence:
Deduction, Reasoning and
Problem Solving
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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Symbolic Intelligence:
Deduction, Reasoning and
Problem Solving
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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Symbolic Intelligence:
Deduction, Reasoning and
Problem Solving
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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Symbolic Intelligence:
Deduction, Reasoning and
Problem Solving
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 –
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seeing, motion, feelings, etc.
Symbolic Intelligence:
Deduction, Reasoning and
Problem Solving
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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Symbolic Intelligence:
Deduction, Reasoning and
Problem Solving
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 subsymbolic 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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Sub-symbolic 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
The Blue Brain Project simulation by reverse-engineering the
mammalian brain. 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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Integrating the Approaches: Intelligent
Agent Paradigm
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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Integrating the Approaches: Intelligent
Agent Modelling
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 Based Modelling Approach
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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References
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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