Soft Computing

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Soft Computing

• Lection 2

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General characteristics of minds/brains that
contemporary researchers in AI and cognitive
science are trying to understand and replicate
Perception - manipulation, integration, and
interpretation of data pro- vided by sensors (in
the context of the internal state of the system
including purposeful, goal-directed, active
perception). Action - coordination, control, and
use of effectors to accomplish a variety of tasks
including exploration and manipulation of the
environment, including design and construction of
tools towards this end. Reasoning - deductive
(logical) inference, inductive inference,
analogical inference including reasoning in the
face of uncertainty and incomplete information,
hypothetical reasoning, justication and
explanation of inferences, evaluation of
explanations, adapting explanations in the light
of falsied assumptions or changing world states.
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Adaptation and Learning - adapting behavior to
better cope with changing environmental demands,
discovery of regularities, explanation of
observations in terms of known facts and
hypotheses, construction of task-specic internal
representations of the environment, discovery of
procedures, learning to differentiate despite
similarities and generalize despite differences,
learning to describe specific domains in terms of
abstract theories and concepts, learning to use,
adapt, and extend language, learning to reason,
plan, and act. Communication - with other
intelligent agents including humans using
signals, signs, icons, symbols, sound, pictures,
touch, language and other communication media
including communication of goals, desires,
beliefs, narratives of real and imaginary
episodes, explanation of actions and events.
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Planning and goal-directed problem-solving -
Formulation of plans sequences or agenda of
actions to accomplish externally or internally
determined goals, evaluating and choosing among
alternative plans, adapting plans in the face of
unexpected changes in the environment, explaining
and justifying plans, modifying old plans to t
new tasks, handling complexity by abstraction and
simplification. Autonomy - Setting of goals,
deciding on the appropriate course of actions to
take in order to accomplish the goals or
directives (without explicit instructions from
another entity), executing the actions to satisfy
the goals, adapting the actions and/or goals as
necessary to deal with any unforeseen
circumstances (to the extent permitted by the
agent's physical capabilities and the
environmental constraints).
Creativity - exploration, modification, and
extension of domains (e.g., language,
mathematics, music) by manipulation of
domain-specific constraints, or by other
means. Reflection and awareness - of internal
processes (e.g., reasoning, goals, etc.) of self
as well as other agents. Aesthetics
articulation and use of aesthetic principles.
Organization - into social groups based on
shared objectives, development of shared
conventions to facilitate orderly interaction,
culture.
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Mitchell (Carnegie Mellon University)
The synergy between AI and Brain Sciences will
yield profound advances in our understanding of
intelligence over the coming decade,
fundamentally changing the nature of our field
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The synergy between AI and Brain Sciences will
yield profound advances in our understanding of
intelligence over the coming decade (said in
2002).

• Common goal understand intelligence
• Significant correspondences between AI methods
  and brain organization
• New instrumentation is causing a revolution

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Human brain
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Two-level model of mind
All models are wrong, but some are useful
(George Box, 1979).
Logical (verbal) thinking
F A(D,D)
Signs (symbols)
Forming of signs from images by classification
and recognition
Control of associations by consciousness
Associative (creative) thinking
F F(K,K)
f(K,A)
Images
Environment
D - signs, K images, A - actions
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Consciousness and subconsciousness
actions
Visual images
consciousness
Sub- consciousness
Sound images
Internal images i. g. pain
Tactile images
Smell images
Taste images
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Basic tasks of associative level

• Recognition relating of image (pattern) to any
  determined class
• Classification - The process of learning to
  relate of image (pattern) to one of set of
  determined classes.
• Clustering - The process of grouping similar
  images (patterns) together in cluster (may be
  named as class) and forming set of classes during
  learning
• Forming of associative links between images and
  between classes
• Associative search images or classes similar to
  any input image (pattern)

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Basic tasks of logical level

• Forming of signs (words, symbols, formulas and so
  on) and links between it and any class
• Forming of structures consists of signs (Trees,
  lists, formulas, sentences and so on) may be
  named as concepts
• Search signs connected start sign (inference)
• Here the concept of context appears

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Process of thinking
Division natural mind into two levels is relative.
Concept, class (may be using for reasoning and
to be on different length from sensors)
Influence of context
Process of classification- recognition
Primary features from different sensors
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Process of thinking
Process of thinking may be viewed as sequence of
firing of set of neurons on associative level
power of set is larger than on logical level
Reasoning on logical level
Neuron
Associative search on image level
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Associations, classification and fuzzy analogy

• Association link created when any different
  images were firing together during process of
  thinking,
• Could say that between these images exist fuzzy
  analogy (or similarity) (different from analogy
  in knowledge engineering based on formalized
  relation of similarity),
• Couple of fuzzy similar Images may be recognized
  as related to same class

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Examples of similar images
Class face of woman
Class face of man
All images relates to class faces
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Forming of mean of word or name (sign) of class
Associative link
face
Classification (rocognition) of visual images
Classification (recognition) of acoustic images
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Any formal definitions
Set of features Kpi i1,Np, describing state
of environment and self intelligent system in
time t, where Np the number of features, Set
of combination of values of features on set K
Pj Pjpij j1,No, i1,Np, describing
concrete images, where No number of images,
Set of real images (it not includes full set of
features) ?Pkj j1,No and k is integer from
(1,Np), Query (image, initializing associative
search) P? ?, Image-result of associative search
R? ?.
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Any formal definitions

• May be two different processes
• The process of restoration of image by partially
  determined
• features. Usually this process is simulated in
  different models of
• associative memory from memory based on Hopfield
  model
• to memory based on spike neurons
• 2) The process of searching of associatively
  connected images
• linked with different moments of time.
• These images mean reasons or consequences of
  initial
• image.

First variant is implemented in natural
intelligent systems in sensor subsystems of
brain. Second in neocortex and one is main
for forecasting and thinking of animal or man.
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Any formal definitions
The pair of images (P,R) may be called an
association A or A(P,R)
Set of associations AAi(Pi,Ri) i?(1,M)
forms memory or knowledge base of intelligent
system.
Predicate ?(Pa,Ra,Ta), describing process of
restoring of Ra Ra ? R by Pa Pa ? P, is
called as associative search, Pa initial image
of associative search and Ra - final image of
associative search, Ta duration of associative
search
Such associative search ?(Pa,Ra,Ta), as it use
only one association from memory A(P,R) Pa?P,
Ra?R,, may be called elementary associative
search.
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Process of associative search
Result - image
Used association
Initial image
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Models of logical (symbol) level in knowledge
engineering and simulation of mind top-down

• 1-order logic
• Other logics based on boolean logic
• Rules
• Semantic nets
• Frames
• Attempts to include in these models fuzziness
• Fuzzy logic,
• Linguistic variables,
• Probabilistic reasoning.

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Models of associative (image) level by neural
networks and simulation of mind bottom-up

• Different model of neural networks
• Attempts to include in neural network forming of
  signs (concepts, words)
• Semantic neural networks
• Fuzzy neural networks
• Ensemble neural networks

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Usual performance about correlation between
features of brain and consciousness

• (1) patterns of neural activity correlate with
  mental states
• (2) synchronous network oscillations of neuronal
  circuits in the thalamus and cerebral cortex
  temporarily binds information
• (3) consciousness emerges as a novel property of
  computational complexity among neurons.

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Other performance about brain
Stuart Hameroff, Roger Penrouse
However, these approaches appear to fall short in
fully explaining certain enigmatic features of
consciousness, such as the nature of subjective
experience, or qualiaour inner life
(Chalmers hard problem, 1996) the binding of
spatially distributed brain activities into
unitary objects in vision, and a coherent sense
of self, or oneness the transition from
preconscious processes to consciousness itself
noncomputability, or the notion that
consciousness involves a factor that is neither
random nor algorithmic, and that consciousness
cannot be simulated (Penrose, 1989, 1994, 1997)
free will subjective time flow.
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• However, in fitting the brain to a computational
  view, such explanations omit incompatible
  neurophysiological details, for example
• widespread apparent randomness at all levels of
  neural processes (is it noise or underlying
  levels of complexity?)
• glial cells (which accounts for some 80 percent
  of the brain)
• dendritic-dendritic processing
• cytoplasmic/cytoskeletal activities

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Quantum theory of mind
Activities within cells ranging from
single-celled organisms to the brains neurons
are organized by a dynamic scaffolding called the
cytoskeleton. A major component of the
cytoskeleton is the microtubule, a hollow,
crystalline cylinder 25 nm in diameter.
Microtubules are, in turn, composed of hexagonal
lattices of proteins, known as tubulin.
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Quantum theory of mind
Microtubule automaton switching offers a
potentially vast increase in the computational
capacity of the brain. While conventional
approaches focus on synaptic switching at the
neural level, which optimally yields about 1018
operations per second in human brains (1011
neurons per brain, with 104 synapses per neuron,
switching at 103 sec1), microtubule automata
switching can explain some 1027 operations per
second (1011 neurons with 107 tubulins per
neuron, switching at 109 sec1). Indeed, the
fact that all biological cells typically contain
approximately 107 tubulins could account for the
adaptive behaviors of single-celled organisms,
which have no nervous system or synapses. Rather
than simple switches, then, it seems that neurons
are actually complex computers.