An Introduction to Artificial An Introduction to Artificial Intelligence and KnowledgeIntelligence and Knowledge

Engineering Engineering

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Sub-topics:

Introduction to the AI paradigms (1.1; pp. 1-3) Heuristic problem solving (1.2; pp. 3-9) Genetic algorithms and evolutionary

programming (1.2.3; pp. 9-14)

Expert systems (1.3.1; pp. 14-15) Fuzzy systems (1.3.2; pp. 15-17) Neural networks (1.3.3; pp. 17-19) Hybrid systems (1.3.4; 1.9, pp. 65-68)

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Introduction to the AI Paradigms

AI objectives: to develop methods and systems for solving problems, usua

lly solved through intellectual activity of humans, eg. image recognition language and speech processing; planning, prediction, etc., thus enhancing the computer information systems

to improve our understanding on how the human brain works

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Introduction to the AI Paradigms (cont)

AI directions: developing methods and systems for solving AI problems

without following the way the humans do (expert systems) developing methods and systems for solving AI problems t

hrough modelling the human way of thinking, or the way the brain works (neural networks)

AI paradigms: symbolic or sub-symbolic (connectionist)

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Heuristic Problem Solving

Figure 1.1

Heuristics as means of obtaining restricted projections from the domain space D into the solution space S.

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Heuristic Problem Solving (cont)

Figure 1.2:

(a) Ill-informed and (b) well-informed heuristics. They are represented as `patches' in the problem space. The patches have different forms (usually quadrilateral) depending on the way of representing the heuristics in a computer program.

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Heuristic Problem Solving (cont)

Figure 1.3:

The problem knowledge maps the domain space into the solution space and approximates the objective (goal) function: (a) a general case; (b) two dimensional case.

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Genetic Algorithms and Evolutionary Programming

Gene Chromosome Population Crossover Mutation Fitness function Selection

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Genetic Algorithms and Evolutionary Programming...

Figure 1.4: An outline of a genetic algorithm

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Genetic Algorithms and Evolutionary Programming...

Figure 1.5: A graphical representation of a genetic algorithm

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Genetic Algorithms and Evolutionary Programming...

Figure 1.6: An example of a genetic algorithm applied to the game "guess the number"

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Second player

Answer (according to the criterion 001010)

Produced variants (individuals)

A) 010101 1

B) 111101 1

C) 011011 4 *

D) 101100 3 *

Using the criterion, the best ones are chosen - C (mother) and D (father).

Mating New variants Evaluation

C) 01:1011 E) 01:1100 3

D) 10:1100 F) 10:1011 4 *

C) 0110:11 G) 0110:00 4 *

D) 1011:00 H) 1011:11 3

Selection of F (mother) and G (father)

Mating New variants Evaluation

F) 1:01011 H) 1:11000 3

G) 0:11000 I) 0:01011 5 *

F) 101:011 J) 101:000 4 *

G) 011:000 K) 011:011 4

Selection of I (mother) and J (father)

Mating New variants Evaluation

I) 0010:11 L) 0010:00 5

J) 1010:00 M) 1010:11 4

I) 00101:1 N) 00101:0 6 (success) * END

J) 10100:0 O) 10100:1 3

Expert Systems

Expert systems are knowledge-based systems which contain expert knowledge and can provide an expertise, similar to the one provided by an expert in a restricted application area. For example, an expert system for diagnosis of cars has a knowledge base containing rules for checking a car and finding faulty elements, as it would be done by a specialised engineer.

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Expert Systems...

Figure 1.7: The two sides of an expert system

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Expert Systems... An expert system consists of the following main blocks:

• knowledge base

• data base

• inference engine

• explanation module

• user interface

• knowledge acquisition module.

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Expert Systems...

Example of an Expert System:

Rule 1:

IF (CScore is high) and (CRatio is good) and (CCredit is good)

then (Decision is approve)

Rule 2:

IF (CScore is low) and (Cratio is bad) or (CCredit is bad)

then (Decision is disapprove)

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Fuzzy Systems

fuzzy sets fuzzy input and output variables fuzzy rules fuzzy inference mechanism

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Fuzzy Systems...

Figure 3.1: Membership functions representing three fuzzy sets for the variable "height".

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Fuzzy Systems...

Figure 3.2: Representing crisp and fuzzy sets as subsets of a domain (universe) U.

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Fuzzy Systems...

Figure 1.8: A simple fuzzy rule for the smoker and the risk of cancer case example.

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Fuzzy Systems... Figure 3.16:

(a) Membership functions for fuzzy sets for the Smoker and the Risk of Cancer case example. (b) The Rc implication relation: "heavy smoker > high risk of cancer" in a matrix form.

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Neural Networks

neural network structure learning generalization

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Neural Networks...

Figure 4.1

A structure of a typical biological neuron. It has many inputs (in) and one output (out). The connections between neurons are realized in the synapses.

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Neural Networks...

Figure 4.2

A model of an artificial neuron.

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Neural Networks...

Figure 4.5

A simple neural network with 4 input nodes, two intermediate nodes and one output node. The connection weights are shown, presumably a result of training. The activation value of node n5 is shown too.

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Hybrid Systems Hybrid systems - A

general approach to knowledge engineering

Figure 1.37

Different "pathways" can be used for knowledge engineering and problem solving to map the domain space into the solution space.

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996

Hybrid Systems...

Figure 1.38

Usability of different methods for knowledge engineering and problem solving depending on availability of data and expertise (theories) on a problem.

N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, 1996