Leszek Pacholski ENGINEERING WITH COMMERCE

Intelligent Management Support Systems

1. Choice of methodology of the management intelligent support in regard to formal complexity of the phenomenon and stage of understanding the dynamics of its course. 2. Artificial Inteligence – differences between traditional software and A.I. software. 3. Structure of an Expert System. 4. Instruments for creation of Expert System. 5. Informal methods of representation of knowledge. 6. Classical methods of managerial reasoning used in Knowledge Bases. 7. The concept of Fuzzy Sets in processes of managerial reasoning. 8. Course of Fuzzy Reasoning. 9. Artificial Neural Networks – definition, directions of applications. 10. The Kohonen Maps: Self Organizing Maps & Learning Vector Quantization. 11. The Recurrent Networks – exemplary architectures. 12. The Algorithms of Evolutional Processing.

1.Choice of methodology of the management intelligent support in regard to formal complexity of the phenomenon and stage of understanding the dynamics of its course.

- Algorythmic reasoning is used in cases in which there is a possibility of precise understanding and describing the phenomenon (there is knowledge enabling to create a model of particular phenomenon in a form of clearly defined and determined dependencies),

- Deductive reasoning can be applied to phenomenons that we can’t precisely describe in a mathematical form, yet basing direct observation we can find some stable patterns of determined phenomenon,

- Inductive reasoning is used when there is no possibility of direct precize determining constant patterns of the course of particular phenomenon.

COGNISION .

latent -

overt -

precize -

COMPLEXITY

Precize understanding of problems dynamics

Algorythmic reasoning Observable constant

paths of behaviour

Deductive reasoning

Expert systemsParapetric regression

Non-observable constant paths of behaviour

Inductive reasoning

Non-parametric regressionArtificial neural networsAdaptive fuzzy systemsGenetic algorythms

2a. Artificial Intelligence. Differences between traditional software and artificial intellgence software

Artificial Intelligence is a domain of computing science relating to methods and techniques of symbolic reasoning realized by a computer with help of symbolic representation of knowledge used in the process of reasoning.

The essence of artificial intelligence software is ascertainment of indequacy of most of modern computer systems with phenomenons apearing in real environment.

Mentioned inadequacy concerns using two-value logic for description of so-called fuzzy phenomenons. Such practice leads to limitation of areas of efficient support and increases costs of introducing computers in practice.

2b. Differences between traditional software and A.I. software.

Digital processing Symbolic processing

Algorythmic record of operations Declarative record of knowledge

Batch processing or interactive processing Interactive environment of integrated

development environment

Capability of verification of correctness Lack capability of full verification of

of operation of software correctness of operation of software

Development of software on base Development of software on basa of

of specification creation of prototypes and improving

them

Presentation and use of data Presentation and use of knowledge

Utilization of databases Utilization of knowledge

Traditional software programs A. I. software programs

3a. Structure of an Expert System

Expert system

Subsystem collecting knowledge

Knowledgebase

Interface

Explaining subsystem

Concluding subsystem

User

Interface Database

Expert of particular domain

Knowledge engineer

3b. Structure of an Expert SystemKnowledge engineer gathers knowledge from experts, transforms it acoordingly to methods of its prezentation in knowledge base and puts knowledge processed in such way into the knowledge base.

Knowledge base stores primal knowledge, procedural rules and

experiences necessary for expert system functioning.

Concluding subsystem enables creating new knowledge based on existing one. It is based on following features in its research of knowledge: - progressive reasoning (aiming to the required target from known conditions), - regressive reasoning (aiming to necessary targets from required conditions).

Explaining subsystem presents operations of an expert system through a list of rules used while the expertise was being formed.

4. Instruments for creation of expert system

Expert system tools can be divided into:

1. Expert system shells

2. Environmental programs facilitating implementation

system (ex. programs facilitating knowledge base

management, facilitating management of the

knowledge base, graphic editors)

3. Expert system languages (Clips, Flops, OPS5)

4. Symbolic programing languages (Lisp, Prolog)

5. Algorythmic languages (Basic. Pascal, C, C+

+ ...)

5a. Informal methods of representation of knowledge - conclusions

Conclusions are one of most important elements of knowledge base.

They concern: events, phenomenons, symptoms, activities.

Usuallu they are recorded in form of an ordered tripple:

(<OBJECT>, <ATTRIBUTE>, <VALUE>)

Dictionaries of names of objects and attributes and their values are used for simplification of proposition forms.

In some systems conclusions are presented in form of ordered four :

(<OBJECT>, <ATTRIBUTE>, <VALUE>,<CF>)

where CF is a Cerntainty Factor leading to a so-called approximate conclusions.

Levels of certainty are determined subjectively (ex. in an interval: [0,1] or [-1,1])

5b. Informal methods of representation of knowledge - Vectors of Knowledge & Semantic Nets

Vectors of knowledge are some kind of generalization of rule methods of presentation of knowledge, for which rules are being recorded in form of vectors.

The method uses three symbols illustrating knowledge:

* – particular condition/conclusion doesn’t

appear in the rule at all,

T – particular condition/conclusion is true,N – particular condition/conclusion is fals.

Semantic Net is a sort of logic, which presents in a graphic form relations between objects within this logic.In other words it is a kind of drawing of a deduction mechanism.Concluding corresponds here with „moving” on the illustration.Various conclusions result from an inspection of the net.

5c. Informal methods of representation of knowledge - Frame Systems

The idea of frame systems is based on analysis of man’s behaviour in situations he has never experienced and in new environment, of which he was having already some concept.

In such situations man taking out of his memory particular structure, i.e. frame, than he confronts the situation with the knowledge from the frame.

Yet, when man meets a totally new object, his first reaction would be a trial of remembering and naming this new phenomenon (i.e. creating a new frame).

6. Classical methods of managerial reasoning used in knowledge bases.

The idea of PROGRESSIVE REASONING consists generation of new facts (on base of accessible rules and facts) as long as the put aim (for example a hypothesis) would appear in the group of generated facts. The basic feature of such method of reasoning is the ability of increasing the base of facts.

REGRESSIVE REASONING reasoning is about presenting veracity of the main hypothesis basing on authenticity of prerequisites. If we don’t know if the prerequisite is true, we treat it as a new hypothesis and we try to prove it. In resultlt of such operations we would find a rule, in which all prerequisites are true, the conclusion of such rule would be also true. Such conclusion would be a basis for next rule, which has an unknown earlier prerequisite etc. Presented hypothesis is true if all considered prerequisites are presentable.

7. The concept of Fuzzy Sets in processes of managerial reasoning.

The definition of fuzzy set is generalizing the term of classic set, i.e. allowing the determing function (so-called membership function) to obtain values of extremal states of determined set (one or zero {0,1}), as well as intermediate values from this range (interval [0,1]).So, in a fuzzy set we observe a fluent passage from absolute membershipto no membership at all. Elements can also be part of the set in a certain degree.The concept of fuzzy sets is often used in processes of managerial reasoning. This idea, in the range of representation of knowledge, describes the problem with help of fuzzy sets theory because tresspassing limits of Aristotle’s logic rules enables better modeling of decisiv limits for fuzzy verbal terms.

8. Course of fuzzy reasoning

Determine values of function of membership for individual fuzzy notions appearing in conditions of rules

Determine fuzzy areas referring to variable included in rule’s conclusion basing on function of membership

Make the combination of fuzzy areas determined during former step

Basing on obtained fuzzy areas determine the result fuzzy area

Make the defuzzyfication (change the fuzzy set into a certain numerical value) of the result fuzzy area.

9a. Artificial Neural Networks – definition, directions of applications

Artificial Neural Networks remain a system of simple elements processing information linked together, called neurons, units or nods. ANN are classified as learning systems. There are weight factors assigned with each connection between elements, which determine the volume of those connections and define the set of parameters of this model. Weigh factors are assigned or determined by a training process, which aims to teach ANN to identify patterns or project transitions. Neural network obtains a certain structure. Its units are grouped into so-called layers (ANN architecture). Neural network is characterized by: - network’s architecture, i.e. localization of individual neurons and connections between them, - the process of searching, i.e. the method of transfer of information (from input to output), - the method of learning (training), which determine the initial set of weigh factors and the way, in which those factors should be changed along the process of learning.

9b. Artificial Neural Networks – definition, directions of applications

Unlike classical computing systems, ANN operate as learning systems and it is possible to separate two stages: training stage and stage of reaction to particular external stimulus.At first the model of solution might be unknown, hence it should be build by the network in its process of learning, basing on so-called training information that it has obtained. Such approach causes many changes in way of designing and building ANN systems, in comparison to traditional computing systems.In an approach typical for ANN calculations are realized by processing units-neurons. Each neuron is connected with a certain number of other neurons and becomes a part of a dispersional system. The ANN domain is also called connectionism, because it keeps information inside a structure of connections between neurons. Artificial neural network is not being programmed. It is being thought.

9c. Artificial Neural Networks – definition, directions of applications

Most often artificial neural networks realize following sorts of processing: - reminding (restoring or interpreting) of information stored in ANN system, - classification, - recognizing, - estimation (approximation, interpolation, filtration, forecasting, predicting), - optimization (including solutions of linear and non-linear equations), - intelligent steering (without the necessity of preparing a model, based exclusively on experience).

10a. The Kohonen Maps: Self Organizing Maps & Learning Vector Quantization

The Kohonen Maps are a specific group of neural networks, which usually has a function of classifiers that relatively simplify data grouping. They consist of an input layer and one layer of processing neurons. Each neuron in Kohonen layer is connected with all inputs. Kohonen Maps use an algorithm of learning, also called competitive learning. Neurons in the network compete with each other in response to the input signal. The winning neuron and its closest environment learn in a process that rest on approximation of theirs weights to the input vector.

10b. The Kohonen Maps: Self Organizing Maps & Learning Vector Quantization

The most commonly used type of Kohonen maps are so-called SOM (Self Organizing Maps), i.e. network maps. Competitive layer in those networks has usually a form of two or three dimensions table of neurons. Neurons are connected with all inputs, so each neuron has as many weight factors as number of inputs the system consists.

10c. The Kohonen Maps: Self Organizing Maps & Learning Vector

Quantization. The most commonly used type of Kohonen maps are so-called LVQ networks (Learning Vector Quantization). They illustrate a controlled competitive learning. Each training vector must be labeled by a tag of its class. Particular tags in LVQ network are assigned to one or several neurons in its competitive layer. Each unit is connected with all inputs.

11a. The recurrent networks - exemplary architectures.

TheThe topology of recurrent networks allows using regressive connections. The input signal from any unit can be transmitted to its input (directly or with help of other neurons). So the state of neuron becomes dependent not only from the value of the input signal, but also from a state of any unit, including the neuron itself. The most simple example to present is a modification of one direction network from the type of regressive propagation through adding to the input layer units of so-called context.

11b. The recurrent networks - exemplary architectures.

12. The algorithms of evolutional processing.

Models of algorithms of evolutional processing solve optimizational problems and tasks of searching with help of a method similar to the rule of real evolutional mechanism, i.e. Darwin’s strategy of survival of most adapted individuals. Such algorithms might be used in practical tasks for difficult questions of optimization and for research in cases when numeric or heuristic solutions are not possible to obtain or if those solutions drive to unsatisfactory results.Three basic streams of evolutional algorithms concern as follows: genetic algorithms, evolutional strategies, evolutional programming. Strategy of an evolutional algorithm consists translocating from one population of solutions into another one, next to the preceding population, in accordance to certain rules that have been established.An evolutional algorithm realizes generating next population and searching it and selecting new generation through following genetic operators: selection, crossing or mutation.