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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

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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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 –….

Intelligence

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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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– 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).

Artifactual/Artificial Intelligence

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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).

Artifactual/Artificial Intelligence

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– 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.

Artifactual/Artificial Intelligence

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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.

Symbolic Intelligence:Deduction, Reasoning and

Problem Solving

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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”.

Symbolic Intelligence:Deduction, Reasoning and

Problem Solving

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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?

Symbolic Intelligence:Deduction, Reasoning and

Problem Solving

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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.

Symbolic Intelligence:Deduction, Reasoning and

Problem Solving

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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.

Symbolic Intelligence:Deduction, Reasoning and

Problem Solving

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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.

Symbolic Intelligence:Deduction, Reasoning and

Problem Solving

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.15

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?17

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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21http://www.hiddengarments.cn/?tag=switzerland

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.

Connectionist AI

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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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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Integrating the Approaches: Intelligent Agent Modelling

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.24

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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-

sub-symbolic.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.