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BIO-INSPIRED AND COGNITIVE COMPUTINGfor data

mining, tracking, fusion, financial prediction,

language understanding, web search engines, and

diagnostic modeling of cultures

IEEE 2007 Fall Short Course Holiday Inn, Woburn

MA 700 900 pm, Nov. 8, 15, 22, Dec. 6

Leonid Perlovsky Visiting Scholar, Harvard

University Technical Advisor, AFRL

OUTLINE

- 1. Cognition, Complexity, and Logic
- 2. The Knowledge Instinct
- -Neural Modeling Fields and Dynamic Logic
- 3. Language
- 4. Integration of cognition and language
- 5. High Cognitive Functions
- 6. Evolution of cultures
- 7. Future directions

DETAILED OUTLINE

- 1. Cognition integration of real-time signals

and a priori knowledge - 1.1. physics and mathematics of the mind
- 1.2. genetic argument for the first principles
- 1.3. the nature of understanding
- 1.3.1. concepts chair
- 1.3.2. hierarchy
- 1.4. combinatorial complexity (CC) a

fundamental problem? - 1.5. CC since 1950s
- 1.6. CC vs. logic
- 1.6.1. formal, multivalued and fuzzy logics
- 1.6.2. dynamic logic
- 1.6.3. Aristotle vs. Godel Alexander the Great
- 1.7. mathematics vs. mind
- 1.8. structure of the mind concepts, instincts,

emotions, behavior - 1.9. the knowledge instinct
- 1.9.1. need for learning
- 1.9.2. knowledge emotion aesthetic emotion
- 2. Modeling Field Theory (NMF) of cognition

2.4. applications, examples, exercises 2.4.1

clustering 2.4.2 tracking and CRB - example

of tracking below clutter - complexity NMF vs.

MHT - models 2.4.3 recognition - example of

pattern in image below clutter - complexity NMF

vs. MHT - models 2.4.4 fusion - example of

fusion, navigation, and detection below

clutter - models 2.4.5 prediction - financial

prediction - models 2.5. block-diagrams 2.7.

hierarchical structure 3. Language 3.1.

language acquisition and complexity 3.1.2.

language separate from cognition 3.1.3.

hierarchy of language 3.1.4. application -

search engine based on understanding 3.2. NMF

of language 3.2.1 differentiating

non-differentiable qualitative functions

DETAILED OUTLINECONTINUATION

- 4. Integration of cognition and language
- 4.1. language vs. cognition
- 4.2. past AI and Chomskyan linguistics
- 4.3. integrated models
- 4.3. integrated hierarchies
- 4.4. Humboldts inner linguistic form
- 5. Prolegomena to a theory of the mind
- 5.1. higher cognitive functions
- 5.2. from Plato to Lock
- 5.3. from Kant to Grossberg
- 5.4. NMF vs. Buddhism
- 5.5. NMF vs. neuro-biology
- 5.5. NMF dynamics elementary thought process
- 5.6. consciousness and unconscious
- 5.7. aesthetics and beauty
- 5.8. intuition
- 5.9. why Adam was expelled from paradise
- 5.9. symbols and signs

6. Evolution of Culture 6.1. Culture

and language 6.2. KI differentiation and

synthesis 6.3. Spiritual cultural

measurements 6.4. Mathematical modeling and

predictions 6.4.1. dynamic culture 6.4.2. tradit

ional culture 6.4.3. terrorists

consciousness 6.4.4. interacting

cultures 6.5. Evolution of concepts and

emotions 6.6. Creativity 6.7.

Disintegration of cultures 6.8. Emotions

in language 6.9. English vs. Arabic 6.10.

Synthesis 6.11. Differentiation of

emotions 6.12. Role of music in evolution of

the mind and culture 7. Future directions

publications 7.1. Science and

Religion 7.2 Predictions and testing 7.3. Future

directions 7.4 Publications

INTRODUCTION

- Nature of the mind

PHYSICS AND MATHEMATICS OF THE MINDRANGE OF

CONCEPTS

- Logic is sufficient to explain mind
- Newell, Artificial Intelligence, 1980s
- No new specific mathematical concepts are needed
- Mind is a collection of ad-hoc principles,

Minsky, 1990s - Specific mathematical constructs describe the

multiplicity of mind phenomena - first physical principles of mind
- Grossberg, Zadeh, Perlovsky,
- Quantum computation
- Hameroff, Penrose, Perlovsky,
- New unknown yet physical phenomena
- Josephson, Penrose

GENETIC ARGUMENTSFOR THE FIRST PRINCIPLES

- Only 30,000 genes in human genome
- Only about 2 difference between human and apes
- Say, 1 difference between human and ape minds
- Only about 300 proteins
- Therefore, the mind has to utilize few inborn

principles - If we count a protein per concept
- If we count combinations 300300 unlimited gt

all concepts and languages could have been

genetically h/w-ed (!?!) - Languages and concepts are not genetically

hardwired - Because they have to be flexible and adaptive

COGNITION

- Understanding the world around
- Perception
- Simple objects
- Complex situations
- Integration of real-time signals and existing (a

priori) knowledge - From signals to concepts
- From less knowledge to more knowledge

EXAMPLE

- Example this is a chair, it is for sitting
- Identify objects
- signals -gt concepts
- What in the mind help us do this?

Representations, models, ontologies? - What is the nature of representations in the

mind? - Wooden chairs in the world, but no wood in the

brain

VISUAL PERCEPTION

- Neural mechanisms are well studied
- Projection from retina to visual cortex

(geometrically accurate) - Projection of memories-models
- from memory to visual cortex
- Matching sensory signals and models
- In visual nerve more feedback connections than

feedforward - matching involves complicated adaptation of

models and signals - Difficulty
- Associate signals with models
- A lot of models (expected objects and scences)
- Many more combinations modelslt-gtpixels
- Association adaptation
- To adapt, signals and models should be associated
- To associate, they should be adapted

ALGORITHMIC DIFFICULTIES A FUNDAMENTAL PROBLEM?

- Cognition and language involve evaluating large

numbers of combinations - Pixels -gt objects -gt scenes
- Combinatorial Complexity (CC)
- A general problem (since the 1950s)
- Detection, recognition, tracking, fusion,

situational awareness, language - Pattern recognition, neural networks, rule

systems - Combinations of 100 elements are 100100
- This number the size of the Universe
- gt all the events in the Universe during its

entire life

COMBINATORIAL COMPLEXITY SINCE the 1950s

- CC was encountered for over 50 years
- Statistical pattern recognition and neural

networks CC of learning requirements - Rule systems and AI, in the presence of

variability CC of rules - Minsky 1960s Artificial Intelligence
- Chomsky 1957 language mechanisms are rule

systems - Model-based systems, with adaptive models CC of

computations - Chomsky 1981 language mechanisms are model-based

(rules and parameters) - Current ontologies, semantic web are

rule-systems - Evolvable ontologies present challenge

CC AND TYPES OF LOGIC

- CC is related to formal logic
- Law of excluded middle (or excluded third)
- every logical statement is either true or false
- Gödel proved that logic is illogical,

inconsistent (1930s) - CC is Gödel's incompleteness in a finite system

- Multivalued logic eliminated the law of excluded

third - Still, the math. of formal logic
- Excluded 3rd -gt excluded (n1)
- Fuzzy logic eliminated the law of excluded

third - Fuzzy logic systems are either too fuzzy or too

crisp - The mind fits fuzziness for every statement at

every step gt CC - Logic pervades all algorithms and neural networks

- rule systems, fuzzy systems (degree of

fuzziness), pattern recognition, neural networks

(training uses logical statements)

LOGIC VS. GRADIENT ASCENT

- Gradient ascent maximizes without CC
- Requires continuous parameters
- How to take gradients along association?
- Data Xn (or?) to object m
- It is a logical statement, discrete,

non-differentiable - Models / ontologies require logic gt CC
- Multivalued logic does not lead to gradient

ascent - Fuzzy logic uses continuous association

variables, but no parameters to differentiate - A new principle is needed to specify gradient

ascent along fuzzy associations dynamic logic

DYNAMIC LOGIC

- Dynamic Logic unifies formal and fuzzy logic
- initial vague or fuzzy concepts dynamically

evolve into formal-logic or crisp concepts - Dynamic logic
- based on a similarity between models and signals
- Overcomes CC of model-based recognition
- fast algorithms

ARISTOTLE VS. GÖDEL logic, forms, and language

- Aristotle
- Logic a supreme way of argument
- Forms representations in the mind
- Form-as-potentiality evolves into

form-as-actuality - Logic is valid for actualities, not for

potentialities (Dynamic Logic) - Thought language and thinking are closely linked
- Language contains the necessary uncertainty
- From Boole to Russell formalization of logic
- Logicians eliminated from logic uncertainty of

language - Hilbert formalize rules of mathematical proofs

forever - Gödel (the 1930s)
- Logic is not consistent
- Any statement can be proved true and false
- Aristotle and Alexander the Great

OUTLINE

- Cognition, complexity, and logic
- Logic does not work, but the mind does
- The Mind and Knowledge Instinct
- Neural Modeling Fields and Dynamic Logic
- Language
- Integration of cognition and language
- Higher Cognitive Functions
- Future directions

STRUCTURE OF THE MIND

- Concepts
- Models of objects, their relations, and

situations - Evolved to satisfy instincts
- Instincts
- Internal sensors (e.g. sugar level in blood)
- Emotions
- Neural signals connecting instincts and concepts
- e.g. a hungry person sees food all around
- Behavior
- Models of goals (desires) and muscle-movement
- Hierarchy
- Concept-models and behavior-models are organized

in a loose hierarchy

THE KNOWLEDGE INSTINCT

- Model-concepts always have to be adapted
- lighting, surrounding, new objects and situations
- even when there is no concrete bodily needs
- Instinct for knowledge and understanding
- Increase similarity between models and the world
- Emotions related to the knowledge instinct
- Satisfaction or dissatisfaction
- change in similarity between models and world
- Related not to bodily instincts
- harmony or disharmony (knowledge-world)

aesthetic emotion

REASONS FOR PAST LIMITATIONS

- Human intelligence combines conceptual

understanding with emotional evaluation - A long-standing cultural belief that emotions are

opposite to thinking and intellect - Stay cool to be smart
- Socrates, Plato, Aristotle
- Reiterated by founders of Artificial Intelligence

Newell, Minsky

Neural Modeling Fields (NMF)

- A mathematical construct modeling the mind
- Neural synaptic fields represent model-concepts
- A loose hierarchy of more and more general

concepts - At every level bottom-up signals, top-down

signals - At every level concepts, emotions, models,

behavior - Concepts become input signals to the next level

NEURAL MODELING FIELDSbasic two-layer mechanism

from signals to concepts

- Signals
- Pixels or samples (from sensor or retina)
- x(n), n 1,,N
- Concept-Models (objects or situations)
- Mm(Sm,n), parameters Sm, m 1,
- Models predict expected signals from objects
- Goal learn object-models and understand signals

(knowledge instinct)

THE KNOWLEDGE INSTINCT

- The knowledge instinct maximization of

similarity between signals and models - Similarity between signals and models, L
- L l (x) l (x(n))
- l (x(n)) r(m) l (x(n) Mm(Sm,n))
- l (x(n) Mm(Sm,n)) is a conditional similarity

for x(n) given m - n are not independent, M(n) may depend on n
- CC L contains MN items all associations of

pixels and models (LOGIC)

SIMILARITY

- Similarity as likelihood
- l (x(n) Mm(Sm,n)) pdf(x(n) Mm(Sm,n)),
- a conditional pdf for x(n) given m
- e.g., Gaussian pdf(X(n)m) G(X(n)Mm,Cm)
- 2p-d/2 detCm-1/2 exp(-DmnTCm-1 Dmn/2) Dmn

X(n) Mm(n) - Note, this is NOT the usual Gaussian assumption

- deviations from models D are random, not the data

X - multiple models m can model any pdf, not one

Gaussian model - Use for sets of data points
- Similarity as information
- l (x(n) Mm(Sm,n)) abs(x(n))pdf(x(n)

Mm(Sm,n)), - a mutual information in model m on data x(n)
- L is a mutual information in all model about all

data

DYNAMIC LOGIC (DL) non-combinatorial solution

- Start with a set of signals and unknown

object-models - any parameter values Sm
- associate object-model with its contents (signal

composition) - (1) f(mn) r(m) l (nm) / r(m') l (nm')

- Improve parameter estimation
- (2) Sm Sm a f(mn) ?ln l

(nm)/?Mm?Mm/?Sm - (a determines speed of convergence)
- learn signal-contents of objects
- Continue iterations (1)-(2). Theorem MF is a

converging system - - similarity increases on each iteration
- - aesthetic emotion is positive during learning

OUTLINE

- Cognition, complexity, and logic
- Logic does not work, but the mind does
- The Mind and Knowledge Instinct
- Neural Modeling Fields and Dynamic Logic
- Application examples
- Language
- Integration of cognition and language
- Higher Cognitive Functions
- Future directions

APPLICATIONS

- Many applications have been developed
- Government
- Medical
- Commercial (about 25 companies use this

technology) - Sensor signals processing and object recognition
- Variety of sensors
- Financial market predictions
- Market crash on 9/11 predicted a week ahead
- Internet search engines
- Based on text understanding
- Evolving ontologies for Semantic Web
- Every application needs models
- Future self-evolving models integrated cognition

and language

APPLICATION 1 - CLUSTERING

- Find natural groups or clusters in data
- Use Gaussian pdf and simple models
- l (nm) 2p-d/2 detCm-1/2 exp(-DmnTCm-1

Dmn/2) Dmn X(n) Mm(n) - Mm(n) Mm each model has just 1 parameter, Sm

Mm - This is clustering with Gaussian Mixture Model
- For complex l(nm) derivatives can be taken

numerically - For simple l(nm) derivatives can be taken

manually - Simplification, not essential
- Simplify parameter estimation equation for

Gaussian pdf and simple models - ?ln l (nm)/?Mm ? (-DmnTCm-1 Dmn) /?Mm Cm-1

Dmn DmnT Cm-1 2 Cm-1 Dmn, (C is symmetric) - Mm Mm a f(mn) Cm-1 Dmn

EXERCISE (1) HOME WORK

- Code a simple NMF/DL for Gaussian Mixture

clustering - (1) Simulate data
- Specify true parameters, say
- Dimensionality, d 2
- Number of classes, 3, m 1, 2, 3
- Rates rm 0.2, 0.3, 0.5
- Number of samples, N 100 Nm Nrm 20, 30, 50

- Means, Mm 2-d vectors, (0.1, 0.1), (0.2, 0.8)

(0.8, 0.2) - Covariances, Cm unit matrixes
- Call a function that generates Gaussian data
- (2) Run NMF/DL code to estimate parameters (rm,

Mm, Cm) and association probabilities f(mn) - Initiate parameters to any values, say each r

1/3 means should not be the same, say M (0,0),

(0,1), (1,1) covariances should be initiated to

larger values than uncertainties in the means

(squared), say each C diag(2,2). - Run iterations estimate f(mn), estimate

parameters until, say (changes in M) lt 0.01 - (3) Plot results (2-d plots)
- Plot data for each class in different

color/symbols - Plot means

APPLICATION 2 - TRACKING

- 1) Example
- 2) Complexity of computations
- Exercise
- Cramer-Rao Bounds

Example 2 GMTI Tracking and Detection Below

Clutter

DL starts with uncertain knowledge and converges

rapidly on exact solution

18 dB improvement

EXAMPLE (2) note page

- Detection and tracking targets below clutter (a)

true track positions in 0.5km x 0.5km data set

(b) actual data available for detection and

tracking (signal is below clutter,

signal-to-clutter ratio is about 2dB for

amplitude and 3dB for Doppler 6 scans are shown

on top of each other, each contains 500 data

points). Dynamic logic operation (c) an initial

fuzzy model, the fuzziness corresponds to the

uncertainty of knowledge (d) to (h) show

increasingly improved models at various

iterations (total of 20 iterations). Between (c)

and (d) the algorithm fits the data with one

model, uncertainty is somewhat reduced. There are

two types of models one uniform model describing

clutter (it is not shown), and linear track

models with large uncertainty the number of

track models, locations, and velocities are

estimated from the data. Between (d) and (e) the

algorithm tried to fit the data with more than

one track-model and decided, that it needs two

models to understand the content of the data.

Fitting with 2 tracks continues till (f) between

(f) and (g) a third track is added. Iterations

stopped at (h), when similarity stopped

increasing. Detected tracks closely correspond to

the truth (a). Complexity of this solution is

low, about 106 operations. Solving this problem

by MHT (template matching with evaluating

combinations of various associations this is a

standard state-of-the-art) would take about MN

101700 operations, unsolvable.

TRACKING AND DETECTION BELOW CLUTTER (movie,

same as above)

DL starts with uncertain knowledge, and similar

to human mind does not sort through all

possibilities, but converges rapidly on exact

solution

3 targets, 6 scans, signal-to-clutter, S/C

-3.0dB

TRACKING EXAMPLE complexity and improvement

- Technical difficulty
- Signal/Clutter - 3 dB, standard tracking

requirements 15 dB - Computations, standard hypothesis testing

101700, unsolvable - Solved by Dynamic Logic
- Computations 2x107
- Improvement 18 dB

EXERCISE (2)

- Develop NMF/DL for target tracking
- Step 1 (and the only one)
- Develop models for tracking
- Model where do you expect to see the target
- Parameters, Sm position xm and velocity vm
- Mm(xm, vm , n) xm vm tn
- Time, tn is not a parameter of the model, but

data - More complex Keplerian trajectories
- Note typical data are 2-d (Q,F) or 3-d (R,Q,F)
- Models are 3-d (or 2-d, if sufficient)
- 3-d models might be not estimatable from 2-d

data - linear track in (x,y,z) over a short time appears

linear in (Q,F) - then, use 2-d models, or additional data, or

longer observation period

CRAMER-RAO BOUND (CRB)

- Can a particular set of models be estimated from

a particular (limited) set of data? - The question is not trivial
- A simple rule-of-thumb N(data points) gt

10S(parameters) - In addition use your mind is there enough

information in the data? - CRB minimal estimation error (best possible

estimation) for any algorithm or neural neworks,

or - When there are many data points, CRB is a good

measure (MLNMF) - When there are few data points (e.g. financial

prediction) it might be difficult to access

performance - Actual errors gtgt CRB
- Simple well-known CRB for averaging several

measurements - st.dev(n) st.dev(1)/vn
- Complex CRB for tracking
- Perlovsky, L.I. (1997a). Cramer-Rao Bound for

Tracking in Clutter and Tracking Multiple

Objects. Pattern Recognition Letters, 18(3),

pp.283-288.

EXERCISE (2 cont.) HOMEWORK

- Homework code NMF/DL for tracking, simulate

data, run the code, and plot results - When simulating data add sensor error and

clutter - track sensor errors X(n,m) xm vm tn

emn - sensor error use sensor error model (or simple

Gaussian) for em,n - clutter X(n,m1) ec,n use clutter model for

ec,n, or a simple uniform or Gaussian simulate

required number of clutter data points - Do not forget to add clutter model to your NMF/FL

code - l (nmclutter) const the only parameter, rc

expected proportion of clutter - Note the resulting system is track-before-detect

or more accurately concurrent detection and

tracking - does not require a standard procedure
- (1) detect, (2) associate, (3) track
- association and detection is obtained together

with tracking - DL requires no combinatorial searches, which

often limits track-before-detect performance

APPLICATION 3

- FINDING PATTERNS IN IMAGES

IMAGE PATTERN BELOW NOISE

Object Image

Object Image Clutter

y

y

x

x

PRIOR STATE-OF-THE-ART Computational complexity

Multiple Hypothesis Testing (MHT) approach try

all possible ways of fitting model to the data

For a 100 x 100 pixel image

Number of Objects Number of

Computations 1

1010 2

1020

3 1030

NMF MODELS

- Information similarity measure
- lnl (x(n) Mm(Sm,n)) abs(x(n))ln pdf(x(n)

Mm(Sm, n)) - n (nx,ny)
- Clutter concept-model (m1)
- pdf(X(n)1) r1
- Object concept-model (m2 )
- pdf(x(n) Mm(Sm, n)) r2 G(X(n)Mm

(n,k),Cm) - Mm (n,k) n0 a(k2,k) (note k, K require

no estimation)

ONE PATTERN BELOW CLUTTER

Y

X

SNR -2.0 dB

DYNAMIC LOGIC WORKING

DL starts with uncertain knowledge, and similar

to human mind converges rapidly on exact solution

- Object invisible to human eye
- By integrating data with the knowledge-model DL

finds an object below noise

y (m) Range

x (m) Cross-range

MULTIPLE PATTERNS BELOW CLUTTER

Three objects in noise

object 1 object 2 object 3

SCR - 0.70 dB -1.98 dB -0.73 dB

3 Object Image Clutter

3 Object Image

y

y

x

x

IMAGE PATTERNS BELOW CLUTTER (dynamic logic

iterations see note-text)

IMAGE PATTERNS BELOW CLUTTER (dynamic logic

iterations see note-text)

Logical complexity MN 105000, unsolvable DL

complexity 107 S/C improvement 16 dB

APPLICATION (3) note page

- smile and frown patterns (a) true smile

and frown patterns shown without clutter (b)

actual image available for recognition (signal is

below clutter, signal-to-clutter ratio is between

2dB and 0.7dB) (c) an initial fuzzy model, the

fuzziness corresponds to the uncertainty of

knowledge (d) to (h) show increasingly improved

models at various iterations (total of 22

iterations). Between (d) and (e) the algorithm

tried to fit the data with more than one model

and decided, that it needs three models to

understand the content of the data. There are

several types of models one uniform model

describing the clutter (it is not shown), and a

variable number of blob models and parabolic

models, which number, locations, and curvatures

are estimated from the data. Until about (g) the

algorithm thought in terms of simple blob

models, at (g) and beyond, the algorithm decided

that it needs more complex parabolic models to

describe the data. Iterations stopped at (h),

when similarity stopped increasing. Complexity of

this solution is moderate, about 1010 operations.

Solving this problem by template matching would

take a prohibitive 1030 to 1040 operations. (This

example is discussed in more details in i.)

i Linnehan, R., Mutz, Perlovsky, L.I., C.,

Weijers, B., Schindler, J., Brockett, R. (2003).

Detection of Patterns Below Clutter in Images.

Int. Conf. On Integration of Knowledge Intensive

Multi-Agent Systems, Cambridge, MA Oct.1-3, 2003.

MULTIPLE TARGET DETECTION DL WORKING EXAMPLE

DL starts with uncertain knowledge, and similar

to human mind does not sort through all

possibilities like an MHT, but converges rapidly

on exact solution

y

x

COMPUTATIONAL REQUIREMENTS COMPARED

Dynamic Logic (DL) vs. Classical

State-of-the-art Multiple Hypothesis Testing

(MHT) Based on 100 x 100 pixel image

Number of Objects

Number of Computations DL vs. MHT

108 vs. 1010 2x108 vs.

1020 3x108 vs. 1030

1 2 3

- Previously un-computable (1030), can now be

computed (3x108 ) - This pertains to many complex

information-finding problems

APPLICATION 4

- SENSOR FUSION
- Concurrent fusion, navigation, and detection
- below clutter

SENSOR FUSION

- The difficult part of sensor fusion is

association of data among sensors - Which sample in one sensor corresponds to which

sample in another sensor? - If objects can be detected in each sensor

individually - Still the problem of data association remains
- Sometimes it is solved through coordinate

estimation - If 3-d coordinates can be estimated reliably in

each sensor - Sometimes it is solved through tracking
- If objects could be reliably tracked in each

sensor, gt 3-d coordinates - If objects cannot be detected in each sensor

individually - We have to find the best possible association

among multiple samples - This is most difficult concurrent detection,

tacking, and fusion

NMF/DL SENSOR FUSION

- NMF/DL for sensor fusion requires no new

conceptual development - Multiple sensor data require multiple sensor

models - Data n -gt (s,n) X(n) -gt X(s,n)
- Models Mm(n) -gt Mm(s,n)
- PDF(nm) is a product over sensors
- This is a standard probabilistic procedure,

another sensor is like another dimension - pdf(mn) -gt pdf(ms,n)
- Note this solves the difficult problem of

concurrent detection, tracking, and fusion

Source UAS Roadmap 2005-2030

UNCLASSIFIED

CONCURRENT NAVIGATION, FUSION, AND DETECTION

- multiple target detection and localization based

on data from multiple micro-UAVs - A complex case
- detection requires fusion (cannot be done with

one sensor) - fusion requires exact target position estimation

in 3-D - target position can be estimated by triangulation

from multiple views - this requires exact UAV position
- GPS is not sufficient
- UAV position - by triangulation relative to

known targets - therefore target detection and localization is

performed concurrently with UAV navigation and

localization, and fusion of information from

multiple UAVs - Unsolvable using standard methods. Dynamic logic

can solve because computational complexity scales

linearly with number of sensors and targets

GEOMETRY MULTIPLE TARGETS, MULTIPLE UAVS

UAV m

Xm X0m Vmt

UAV 1

Xm(Xm,Ym,Zm)

X1(X1,Y1,Z1)

X1 X01 V1t

CONDITIONAL SIMILARITIES (pdf) FOR TARGET k

Data from UAV m, sample number n, where ßnm

signature position and fnm classification

feature vector

Similarity for the data, given target k

signature position

where

classification features

Note Also have a pdf for a single clutter

component pdf(wnm k0) which is uniform in ßnm,

Gaussian in fnm.

Data Model and Likelihood Similarity

Total pdf of data samples is the summation of

conditional pdfs (summation over targets plus

clutter)

(mixture model)

classification feature parameters

UAV parameters

target parameters

Concurrent Parameter Estimation / Signature

Association (NMF iterations)

FIND SOLUTION FOR SET OF BEST PARAMETERS BY

ITERATING BETWEEN

Parameter Estimation and

Association Probability Estimation

(Bayes rule)

(probability that sample wnm was generated by

target k)

Note1 bracket notation Note2 proven to

converge (e.g. EM algorithm) Note 3 Minimum MSE

solution incorporates GPS measurements

Sensor 1 (of 3) Models Evolve to Locate Target

Tracks in Image Data

Sensor 2 (of 3) Models Evolve to Locate Target

Tracks in Image Data

Sensor 3 (of 3) Models Evolve to Locate Target

Tracks in Image Data

NAVIGATION, FUSION, TRACKING, AND DETECTION (this

is the basis for the previous 3 figures, all

fused in x,y,z, coordinatesdouble-click on the

blob to play movie)

Model Parameters Iteratively Adapt to Locate the

Targets

Error vs. iteration (4 targets)

Estimated Target Position vs. iteration (4

targets)

Parameter Estimation Errors Decrease with

Increasing Number of UAVs in the Swarm

Error in Parameter Estimates vs. clutter level

and of UAVs in the swarm

Target position

UAV position

(Note Results are based upon Monte Carlo

simulations with synthetic data)

Error falls off as 1/vM, where M UAVs in

the swarm

APPLICATION 5

- DETECTION IN SEQUENCES OF IMAGES

DETECTION IN A SEQUENCE OF IMAGES

Signature high noise level (SNR -6dB)

Signature low noise level (SNR 25dB)

signature is present, but is obscured by noise

DETECTION IN IMAGE SEQUENCETEN ROTATION FRAMES

WERE USED

Iteration 10

Iteration 100

Iteration 400

- Upon convergence of the model, important

parameters are estimated, including center of

rotation, which will next be used for spectrum

estimation. - Four model components were used, including a

uniform background component. Only one component

became associated with point source.

Compare with Measured Image (w/o noise)

Iteration 600

TARGET SIGNATURE

APPLICATION 6

- Radar Imaging through walls
- - Inverse scattering problem
- - Standard radar imaging algorithms (SAR) do not

work because of multi-paths, refractions, clutter

SCENARIO

RADAR IMAGING THROUGH WALLS

Standard SAR imaging does not work Because of

refraction, multi-paths and clutter

Estimated model, work in progress Remains -increa

se convergence area -increase complexity of

scenario -adaptive control of sensors

DYNAMIC LOGIC / NMF

INTEGRATED INFORMATION objects

relations situations behavior

Dynamic Logic combining conceptual analysis

with emotional evaluation

MODELS - objects - relations - situations -

behavioral

Data and Signals

CLASSICAL METHODOLGYno closure

Result Conceptual objects

- MODELS/templates
- objects, sensors
- physical models

Recognition

Input World/scene

signals

Sensors / Effectors

NMF closurebasic two-layer hierarchy signals

and concepts

Result Conceptual objects

Attention / Action

Correspondence / Similarity measures

signals Sim.signals

- MODELS
- objects, sensors
- physical models

signals

Sensors / Effectors

Input World/scene

APPLICATION 7

- Prediction
- - Financial prediction

PREDICTION

- Simple linear regression
- y(x) Axb
- Multi-dimensional regression y,x,b are vectors,

A is a m-x - Problem given y,x, estimate A,b
- Solution to linear regression (well known)
- Estimate means ltygt, ltxgt, and x-y covariance

matrix C - A Cyx Cxx-1 b ltygt - Altxgt
- Difficulties
- Non-linear y(x), unknown shape
- y(x) changes regime (from up to down)
- and this is the most important event (financial

prediction) - No sufficient data to estimate C
- required 10dxy3 data points, or more

NMF/DL PREDICTION

- General non-linear regression (GNLR)
- y(x) f(mn) ym(x) f(mn)

(Amxbm) - Amand bm are estimated similar to A,b in linear

regression with the - following change all () are changed into

f(mn)() - For prediction, we remember that f(mn) f(mx)
- Interpretation
- m are regimes or processes, f(mx) determines

influence of regime m at point x (probability of

process m being active) - Applications
- Non-linear y(x), unknown shape
- Detection of y(x) regime change (e.g. financial

prediction or control) - Minimal number of parameters 2 linear

regressions f(mn) are functions of the same

parameters - Efficient estimation (ML)
- Potential for the fastest possible detection of a

regime change

FINANCIAL PREDICTION Efficient Market Hypothesis

- Efficient market hypothesis, strong
- no method for data processing or market analysis

will bring advantage over average market

performance (only illegal trading on nonpublic

material information will get one ahead of the

market) - Reasoning too many market participants will try

the same tricks - Efficient market hypothesis, week
- to get ahead of average market performance one

has to do something better than the rest of the

world better math. methods, or better analysis,

or something else (it is possible to get ahead of

the market legally)

FINANCIAL PREDICTIONBASICS OF MATH. PREDICTION

- Basic idea train from t1 to t2, predict and

trade on t21 increment t1-gtt11, t2-gtt21 - Number of data points between t1 to t2 should be

gtgt number of parameters - Decide on frequency of trading, it should

correspond to your psychological makeup and

practical situation - E.g. day-trading has more potential for making

(or losing) a lot of money fast, but requires

full time commitment - Get past data, split into 3 sets (1)developing,

(2)testing, (3)final test (best, in real time,

paper trades) - After much effort on (1), try on (2), if work,

try on (3)

DETAILS OFFINANCIAL PREDICTION

- Develop best mathematical technique for market

prediction - Takes a lot of effort
- Test in up and down markets
- If your simulated portfolio goes up and up

smoothly (you are constantly making money in

computer simulation), look for an error - Simple errors in computing return
- Include spread and commission in return
- Illegal training using (t21) information for

training (or trading) - Technical details
- What to optimize in training/development?
- Performance (return or cum. return), ROR (end

- start) / start - Sharpe (return/risk ratio) Sh (ROR RORmarket)

/ std(RORmarket) - Sh gt 1 ok, Sh gt 3 look for errors (beware)
- Include penalizing factor for free parameters

(Akaike, Statistical Learning Theory (Vapnik),

Ridge regression) - Ridge regression min (y(t) p(t))2 a

(p(t) ltp(t)gt)2

FINANCIAL MARKET PREDICITION

BIOINFORMATICS

- Many potential applications
- combinatorial complexity of existing algorithms
- Drug design
- Diagnostics which gene / protein is responsible
- Pattern recognition
- Identify a pattern of genes responsible for the

condition - Relate sequence to function
- Protein folding (shape)
- Relate shape to conditions
- Many basic problems are solved sub-optimally

(combinatorial complexity) - Alignment
- Dynamic system of interacting genes / proteins
- Characterize
- Relate to conditions

NMF/DL FOR COGNITIONSUMMARY

- Cognition
- Integrating knowledge and data / signals
- Evolution from vague to crisp
- Knowledge concepts models
- Knowledge instinct similarity(models, data)
- Aesthetic emotion change in similarity
- Emotional intelligence
- combination of conceptual knowledge and emotional

evaluation - Applications
- Recognition, tracking, fusion, prediction

OUTLINE

- Cognition, complexity, and logic
- The Mind and Knowledge Instinct
- Language
- Integration of cognition and language
- Higher Cognitive Functions
- Future directions

LANGUAGE

- Integration of language-data and language-models
- Speech, text
- Language acquisition / learning
- Search engines
- Language is similar to cognition
- specific language data
- NMF of language

LANGUAGE ACQUISITIONAND COMPLEXITY

- Chomsky linguistics should study the mind

mechanisms of language (1957) - Chomskys language mechanisms
- 1957 rule-based
- 1981 model-based (rules and parameters)
- Combinatorial complexity
- For the same reason as all rule-based and

model-based methods

HIERARCHY OF LANGUAGE

- Speech is a (loose) hierarchy of objects
- Words are made of language sounds, phonemes
- Phrases are made of words
- Text is a (loose) hierarchy of objects
- Letters, words, phrase,
- Meanings of language objects
- Language objects acquire meanings in the

hierarchy - Phonemes acquire meaning in words
- Words acquire meaning in phrases
- Phrases acquire meaning in paragraphs,

APPLICATION SEARCH ENGINE BASED ON UNDERSTANDING

- Goal-instinct
- Find conceptual similarity between a query and

text - Analyze query and text in terms of concepts
- Simple non-adaptive techniques
- By keywords
- By key-sentences set of words
- Define a sequence of words (bag of words)
- Compute coincidences between the bag and the

document - Instead of the document use chunks of 7 or 10

words - How to learn useful sentences?

APPLICATION LEARN LANGUAGE UNDERSTANDING

- Goal-instinct
- Find conceptual similarity between a query and

text - Analyze query and text in terms of concepts
- Learn and identify model-concepts in texts
- Words from a dictionary
- Hierarchy
- phrases made up of words, paragraphs of phrases
- Language instinct knowledge instinct

OUTLINE

- Cognition, complexity, and logic
- The Mind and Knowledge Instinct
- Language
- - NMF of language
- Integration of cognition and language
- Higher Cognitive Functions
- Future directions

NMF OF LANGUAGE basic two-layer hierarchy words

and phrase-concepts

- Words and Concept-Models
- words w(n), n 1,,N
- model-phrase Mm(Sm,n), parameters Sm, m 1,
- Simplistic bag-model Mm(Sm,n) Sm nm

wm,1 wm,2 wm,s - nm is a position of the model-center in the text

s/2 (N-s/2) - Goal learn phrase-models
- associate sequences n with models m and find

parameters Sm - learn word-contents of phrases (and grammatical

relationships) - Maximize similarity between words and models, L
- Likelihood L l(w(n))
- l(w(n)) r(m) l(w(n) Mm(Sm,n))
- CC L contains MN items all associations of

words and models

DYNAMIC LOGIC (non-combinatorial solution)

- Start with a large body of text and unknown

phrase-models - any parameter values Sm
- associate fuzzy phrase-model with its contents

(words) - (1) f(mn) r(m) l(nm) / r(m')

l(nm') - Improve parameter estimation
- (2) Sm Sm a f(mn)

?lnl(nm)/?Mm?Mm/?Sm - (a determines speed of convergence)
- learn word-contents of phrases (and grammatical

relationships) - Continue iterations (1)-(2). Theorem NMF is

converging - - similarity increases on each iteration
- - aesthetic emotion is positive during

learning

DIFFERENTIATION OF QUALITATIVE FUNCTIONS

- Differentiation of bag-model
- The bag-model is non-differentiable
- This is a principal moment, learning

non-differentiable models requires sorting

through combinations - Lead to combinatorial complexity
- How to differentiate
- Non-continuous, non-differentiable, qualitative

functions - Also essential for the hierarchy
- Higher level are made up of bags of lower level

concepts

QUALITATIVE DERIVATIVE

- Define fuzzy conditional partial similarity as
- l(nm) Ss G(e(n,m,s) , sm )
- e(n,m,s) is a distance between n and the word

wm,s in Mm(n) that matches w(n), counted in the

number of words (if no match, e(n,m,s) S/21) - Fuzziness is determined by phrase-model length, S

- and matching st.dev. sm S / 3
- Parameter estimation
- Initialize with a large S and any values for

wm,s - On every iteration compute e(n,m,s) and sm
- From every model delete the least likely word
- Reduce the phrase length S by 1
- Thus, most likely words are gradually selected

for each model - Details in Perlovsky, L.I. (2006). Symbols

Integrated Cognition and Language. Book Chapter

in A. Loula, R. Gudwin, J. Queiroz, eds.,

Computational Semiotics. Idea Group, Hershey, PA.

OUTLINE

- Cognition, complexity, and logic
- The Mind and Knowledge Instinct
- Language
- Integration of cognition and language
- Higher Cognitive Functions
- Future directions

LANGUAGE vs. COGNITION

- Nativists, - since the 1950s
- - Language is a separate mind mechanism (Chomsky)
- - Pinker language instinct
- Cognitivists, - since the 1970s
- Language depends on cognition
- Talmy, Elman, Tomasello
- Evolutionists, - since the 1980s
- - Hurford, Kirby, Cangelosi
- - Language transmission between generations
- Co-evolution of language and cognition

WHAT WAS FIRST COGNITION OR LANGUAGE?

- How language and thoughts come together?
- Conscious
- final results logical concepts
- Language seems completely conscious
- However, a child at 5 knows about good and

bad guys - Are these conscious concepts?
- Unconscious
- fuzzy mechanisms of language and cognition
- Logic
- Same mechanisms for L. C.
- Did not work
- Sub-conceptual, sub-conscious integration

INTEGRATEDLANGUAGE AND COGNITION

- Where language and cognition come together?
- A fuzzy concept m has linguistic and

cognitive-sensory models - Mm Mmcognitive,Mmlanguage
- Language and cognition are fused at fuzzy

pre-conceptual level - before concepts are learned
- Understanding language and sensory data
- Initial models are fuzzy blobs
- Language models have empty slots for cognitive

model (objects and situations) and v.v. - Childs learning
- Language participates in cognition and v.v.
- L C help learning and understanding each other
- Help associating signals, words, models, and

behavior

INNER LINGUISTIC FORM HUMBOLDT, the 1830s

- In the 1830s Humboldt discussed two types of

linguistic forms - words outer linguistic form (dictionary) a

formal designation - and inner linguistic form (???) creative, full

of potential - This remained a mystery for rule-based AI,

structural linguistics, Chomskyan linguistics - rule-based approaches using the mathematics of

logic make no difference between formal and

creative - In NMF / DL there is a difference
- static form of learned (converged) concept-models
- dynamic form of fuzzy concepts, with creative

learning potential, emotional content, and

unconscious content

OUTLINE

- Cognition, complexity, and logic
- The Mind and Knowledge Instinct
- Language
- Integration of cognition and language
- Higher Cognitive Functions
- Future directions

HIGHER COGNITIVE FUNCTIONS

- Abstract models are at higher levels of hierarchy
- More vague-fuzzy, less conscious
- At every level
- Bottom-up signals are recognized

lower-level-concepts - Top-down signals are vague concept-models
- Behavior-actions (including learning-adaptation)

TOWARD A THEORY OF THE MIND

- From Plato to physics of the mind
- A mathematical theory describing first

principles of the mind - Corresponding to existing data and making

testable predictions

FROM PLATO TO LOCKE

- Realism
- Plato ability for thinking is based on a priori

Ideas - Aristotle
- ability for thinking is based on a priori

(dynamic) Forms - an a priori form-as-potentiality (fuzzy model)

meets matter (signals) and becomes a

form-as-actuality (a concept) - actualities obey logic, potentialities do not
- Nominalism
- Antisthenes (contemporary of Plato)
- there are no a priori ideas, just names for

similar things - Occam (14th c.)
- Ideas are linguistic phenomena devoid of reality
- Locke (17th c.)
- A newborn mind is a blank slate

FROM KANT TO GROSSBERG

- Kant three primary inborn abilities
- Reason understanding (models of cognition)
- Practical Reason behavior (models of behavior)
- Judgment emotions (similarity)
- We only know concepts, not things-in-themselves
- Jung
- Conscious concepts are developed based on

inherited structures of the mind, archetypes,

inaccessible to consciousness - Realists vs. nominalists introverts vs.

extroverts - Chomsky
- Inborn structures, not general intelligence
- Grossberg
- Models attaining a resonant state (winning the

competition for signals) reach consciousness - DL Aristotle
- A-priori models are vague-fuzzy and unconscious
- Understood models in a resonant state are

crisper and more conscious

NMF AND BUDDHISM

- Fundamental Buddhist notion of Maya
- the world of phenomena, Maya, is meaningless

deception - penetrates into the depths of perception and

cognition - phenomena are not identical to things-in-themselve

s - Fundamental Buddhist notion of Emptiness
- consciousness of bodhisattva wonders at

perception of emptiness in any object (Dalai

Lama 1993) - any object is first of all a phenomenon

accessible to cognition - value of any object for satisfying the lower

bodily instincts is much less than its value for

satisfying higher needs, knowledge instinct - Bodhisattvas consciousness is directed by the

knowledge instinct - concentration on emptiness does not mean

emotional emptiness, but the opposite, the

fullness with highest emotions related to the

knowledge instinct, beauty and spiritually

sublime

MIND VS. BRAIN

- We start understanding how to relate the mind to

the brain - Which neural circuits of the brain implement

which functions of the mind

NMF DYNAMICS

- A large number of model-concepts compete for

incoming signals - Uncertainty in models corresponds to uncertainty

in associations f(mn) - Eventually, one model (m') wins a competition for

a subset n' of input signals x(n), when

parameter values match object properties, and

f(m'n) values become close to 1 for n?n' and 0

for n?n' - Upon convergence, the entire set of input signals

n is divided into subsets, each associated with

one model-object - Fuzzy a priori concepts (unconscious) become

crisp concepts (conscious) - dynamic logic, Aristotelian forms, Jungian

archetypes, Grossberg resonance - Elementary thought process

CONSCIOUSNESS AND UNCONSCIOUS

- Jung conscious concepts and unconscious

archetypes - Grossberg models attaining a resonant state

(winning the competition for signals) reach

consciousness - NMF fuzzy mechanisms (DL) are unconscious, crisp

concept-models, adapted and matched to data are

conscious

AESTHETIC EMOTIONS

- Not related to bodily satisfaction
- Satisfy instincts for knowledge and language
- learning concepts and learning language
- Not just what artists do
- Guide every perception and cognition process
- Perceived as feeling of harmony-disharmony
- satisfaction-dissatisfaction
- Maximize similarity between models and world
- between our understanding of how things ought to

be and how they actually are in the surrounding

world Kant aesthetic emotions

BEAUTY

- Harmony is an elementary aesthetic emotion
- higher aesthetic emotions are involved in the

development of more complex higher models - The highest forms of aesthetic emotion, beautiful

- related to the most general and most important

models - models of the meaning of our existence, of our

purposiveness - beautiful object stimulates improvement of the

highest models of meaning - Beautiful reminds us of our purposiveness
- Kant called beauty aimless purposiveness not

related to bodily purposes - he was dissatisfied by not being able to give a

positive definition knowledge instinct - absence of positive definition remained a major

source of confusion in philosophical aesthetics

till this very day - Beauty is separate from sex, but sex makes use of

all our abilities, including beauty

INTUITION

- Complex states of perception-feeling of

unconscious fuzzy processes - involves fuzzy unconscious concept-models
- in process of being learned and adapted
- toward crisp and conscious models, a theory
- conceptual and emotional content is

undifferentiated - such models satisfy or dissatisfy the knowledge

instinct before they are accessible to

consciousness, hence the complex emotional feel

of an intuition - Artistic intuition
- composer sounds and their relationships to

psyche - painter colors, shapes and their relationships

to psyche - writer words and their relationships to psyche

INTUITION Physics vs. Math.

- Mathematical intuition is about
- Structure and consistency within the theory
- Relationships to a priori content of psyche
- Physical intuition is about
- The real world, first principles of its

organization, and mathematics describing it - Beauty of a physical theory discussed by

physicists - Related to satisfying knowledge instinct
- the feeling of purpose in the world

WHY ADAM WAS EXPELLED FROM PARADISE?

- God gave Adam the mind, but forbade to eat from

the Tree of Knowledge - All great philosophers and theologists from time

immemorial pondered this - Maimonides, 12th century
- God wants people to think for themselves
- Adam wanted ready-made knowledge
- Thinking for oneself is difficult (this is our

predicament) - Today we can approach this scientifically
- Rarely

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