Soft Computing

Lection 2

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.

General characteristics of minds/brains that contemporary researchers in AI and cognitive science are trying to understand and replicate:

Adaptation and Learning - adapting behavior to better cope withchanging 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.

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 ofactions to take in order to accomplish the goals or directives (withoutexplicit 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.

Mitchell (Carnegie Mellon University):

The synergy between AI and Brain Sciences will yield profound advances in our understanding of intelligenceover the coming decade, fundamentally changing the nature of our field

The synergy between AI and Brain Sciences will yield profound advances in our understanding of intelligence over the coming decade (said in 2002).

1. Common goal: understand intelligence

2. Significant correspondences between AI methods and brain organization

3. New instrumentation is causing a revolution

Human brain

Two-level model of mind

Environment

Signs (symbols)

Associative (creative) thinking

Logical (verbal) thinking

Images

F A(D,D)

F F(K,K) f(K,A)

D - signs, K – images, A - actions

Forming of signs from images by classification and recognition

Control of associations by consciousness

“All models are wrong, but some are useful” (George Box, 1979).

Consciousness and subconsciousness

Sub-consciousness

consciousnessVisual images

Sound images

Smell imagesTaste images

Tactile images

Internal imagesi. g. pain

actions

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)

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

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)

Primary features from differentsensors

Process of classification-recognition

Influenceof context

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

Reasoningon logical level

Associative searchon image level

Neuron

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

Examples of similar images

Class “face ofwoman”

Class “face of man”

All images relates to class “faces”

Forming of mean of word or name (sign) of class

Associative link

Classification(rocognition) ofvisual images

Classification(recognition) ofacoustic images

face

Any formal definitionsSet of features K={pi}| i=1,Np, describing state of environmentand self intelligent system in time t, where Np – the number of features,

Set of combination of values of features on set K ={Pj} | Pj={pij} | j=1,No, i=1,Np, describing concrete images, where No – number of images,

Set of real images (it not includes full set of features) Ψ={Pkj} | j=1,No and k is integer from (1,Np),

Query (image, initializing associative search) P Ψ,Image-result of associative search R Ψ.

Any formal definitions

May be two different processes: 1) 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 modelto 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.

Any formal definitionsThe pair of images (P,R) may be called an association A or A(P,R)

Set of associations A={Ai(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) | PaP, RaR,, may be called elementary associative search.

Process of associative search

Used association

Result - image

Initial image

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• FramesAttempts to include in these models fuzziness:Fuzzy logic,Linguistic variables,Probabilistic reasoning.

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

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.

Other performance about brainStuart 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 “qualia”—our “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.

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

Quantum theory of mindActivities within cells ranging from single-celled organisms to the brain’s 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.

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 sec–1), microtubule automata switching can explain some 1027 operations per second (~1011 neurons with ~107 tubulins per neuron, switching at ~109 sec–1). 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.