Artificial Intelligence and Cognitive Modeling

Laboratory for Cognitive Modeling

4.11.2011

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Terminology, terminology…

Artificial Intelligence Machine Learning

Data Mining

Cognitive Modeling

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Data modeling

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Cat. % nBad 52.01 168

Good 47.99 155Total (100.00) 323

Credit ranking (1=default)

Cat. % nBad 86.67 143

Good 13.33 22Total (51.08) 165

Paid Weekly/MonthlyP-value=0.0000, Chi-square=179.6665, df=1

Weekly pay

Cat. % nBad 15.82 25Good 84.18 133Total (48.92) 158

Monthly salary

Cat. % nBad 90.51 143

Good 9.49 15Total (48.92) 158

Age CategoricalP-value=0.0000, Chi-square=30.1113, df=1

Young (< 25);Middle (25-35)

Cat. % nBad 0.00 0Good 100.00 7Total (2.17) 7

Old ( > 35)

Cat. % nBad 48.98 24Good 51.02 25Total (15.17) 49

Age CategoricalP-value=0.0000, Chi-square=58.7255, df=1

Young (< 25)

Cat. % nBad 0.92 1Good 99.08 108Total (33.75) 109

Middle (25-35);Old ( > 35)

Cat. % nBad 0.00 0Good 100.00 8Total (2.48) 8

Social ClassP-value=0.0016, Chi-square=12.0388, df=1

Management;Clerical

Cat. % nBad 58.54 24

Good 41.46 17Total (12.69) 41

Professional

Data model

Different types of datafrom different sources

Data mining

Background knowledge

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Models and their use

• Supervised and unsupervised modeling

• Model types:– decision trees and

decision rules– artificial neural networks– regression trees– nearest neighbors– association rules– random forests– …

• Different models, different use:– model structure

(presentation of the relationship between inputs and outputs)

– prediction– associations (relationships)

between input values– clustering– outlier detection– …

Example:applications in medical diagnostics and prognostics

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• modeling the knowledge and skills of specialist physicians

• using models for decision support

• scintigraphy of the skeleton and heart, oncology, traumatology, …

Medical diagnostics and prognostics

• Input: background knowledge, descriptions of patients with subsequently confirmed diagnosis

• How to diagnose?• How to predict the occurrence of a disease

or its recurrence?• Very good results in specialized areas

(significantly better than specialists).

• What characteristics have the greatest impact on the disease?

• What is the reliability of computer predictions (diagnosis and prognosis)?

• How to explain predictions and bring them closer to doctors?

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Reliability estimation for medical diagnosis

General methods for estimating the reliability of individual predictions are developed.

Diagnosis explanation

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General methods for explaining predictions are developed.

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Skeletal pathology detection

• Skeletal scintigraphy

• Background knowledge of human anatomy

• Known diagnoses

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Diagnosis of coronary artery disease

• Heart scintigraphy• Input data in the

form of images• Medical records• Reliability estimates

Marketing

• How do customers decide what products to buy?

• How to arrange ads in an optimal way?

• When is the best time to broadcast television ads?

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Advanced sports analysis

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• in collaboration with the Faculty of Sport in Ljubljana :– analysis of the impact of rules

changes in 2010/11 season

• A basketball match simulation

And more...

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• prediction market

• prediction intervals

• clickstream analysis

• façade analysis

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• Versatile applicability of artificial intelligence methods, especially data mining– ability to process large amounts of data– variety of data types– inclusion of background knowledge

• However: Artificial Intelligence (still) is not intelligence

Conclusion

Scientific and developmental competence

We are the authors of numerous papers in scientific journals and books

(over 700 citations)

We are the authors of numerous papers in scientific journals and books

(over 700 citations)

We regularly participateat scientific conferences

and present our work

We regularly participateat scientific conferences

and present our work

We are members of editorial boards and program committees

We are members of editorial boards and program committees

We have a long experiencein the field of medicine, marketing,

financial sector, telecommunications ...

We have a long experiencein the field of medicine, marketing,

financial sector, telecommunications ...

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Institute of Oncology AD Consulting Bion Institute Jožef Stefan Institute-department of knowledge technologiesStarcom The Laboratory of NeuroendocrinologyClinic for Nuclear Medicine Intensio Faculty of sports

ASCR Institute of Computer Science

University of MalagaUniversity of Ioannina

University of Hasselt

Collaboration with other institutions

University of Porto

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University of Kragujevac

Who are we?

prof. dr. Igor Kononenko

izr. prof. dr. Marko Robnik Šikonjadoc. dr. Matjaž Kukar

doc. dr. Zoran Bosnić

dr. Erik Štrumbelj as. mag. Petar Vračar

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Darko Pevec

as. Matej PičulinMiha DroleDomen Košir