INDUCTION, TRADUCTION,

ABDUCTION AND DEDUCTION IN THE

PROCESSES OF HYPOTHESES

GENERATION AND JUSTIFICATION

INDUCTION, TRADUCTION,

ABDUCTION AND DEDUCTION IN THE

PROCESSES OF HYPOTHESES

GENERATION AND JUSTIFICATION

Valkman Y. R., Dembovskyy O. Y.The International Research and Training Center of

Information Technologies and Systems

yur@valkman.kiev.ua

THIS ISSUE ADRESSED THE THIS ISSUE ADRESSED THE FOLLOWING MATTERS:FOLLOWING MATTERS:

1.1. Levels of Inductive Conclusions

2.2. How the humans build, justify and refute hypotheses

3.3. One more view on Induction

4.4. Properties of hypotheses

5.5. A few words about about Data mining и Data Warehouses

6.6. Оn modeling hypotheses generation process

7.7. Inductive inference and heuristics

Inductive view on science was classically Inductive view on science was classically described by J.S.Mill in his "described by J.S.Mill in his "System of logicSystem of logic" " (1843); it presupposes that scientific researches (1843); it presupposes that scientific researches must begin from free and unprejudiced observation must begin from free and unprejudiced observation of facts, then have to be continued by the inductive of facts, then have to be continued by the inductive formulation of universal laws, which describe these formulation of universal laws, which describe these facts, and, finally, to come to the more general facts, and, finally, to come to the more general conclusions (conclusions (it is agreed to call them "theories"). ). But, if to imagine the science as the sequence of But, if to imagine the science as the sequence of infinite attempts of existing hypotheses' refutation infinite attempts of existing hypotheses' refutation and to replace them by another, and to replace them by another, non-falsifiable statements , it should be naturally to ask, where statements , it should be naturally to ask, where these hypotheses appear from.these hypotheses appear from.

INTRODUCTIOINTRODUCTIONN

K.Popper follows general view, when rejecting K.Popper follows general view, when rejecting any interest to the so-called «any interest to the so-called «CONTEXT OF DISCOVERY" (contrary to " (contrary to ""CONTEXT OF JUSTIFICATION") — a problem of the ") — a problem of the origins of scientific knowledge remains in the origins of scientific knowledge remains in the sphere of psychology or sociology of knowledge — sphere of psychology or sociology of knowledge — but, nevertheless, he persists that any source of but, nevertheless, he persists that any source of sciential generalization definitely does sciential generalization definitely does not represent the induction from separate cases..

For him, an induction simply is a myth: For him, an induction simply is a myth: the inductive hypotheses are not only the inductive hypotheses are not only illegitimate (as was shown by D. Hume illegitimate (as was shown by D. Hume long time ago), but are also impossible.long time ago), but are also impossible.

We cannot make unductive conclusions We cannot make unductive conclusions when starting from some series of when starting from some series of observations because at that moment of observations because at that moment of time, when the choice of certain kind of time, when the choice of certain kind of observations has been made, we observations has been made, we alreadyalready took the certain point of view, took the certain point of view, and this point of view is a theory itself, and this point of view is a theory itself, no matter how that theory is simple or no matter how that theory is simple or rough. rough. In other words, "rough" facts do not In other words, "rough" facts do not exist — exist — all they already contain some all they already contain some latent theory.latent theory.

M. Blaug ( M. Blaug ( The Methodology of Economics: Or how Economists The Methodology of Economics: Or how Economists

Explain. 2nd Ed. Cambridge University Press, 1992.Explain. 2nd Ed. Cambridge University Press, 1992.) ) goes further goes further when says that when says that general opinion about induction and general opinion about induction and deduction - as mutually inverted processes of deduction - as mutually inverted processes of thinking - is big misapprehension. He argues the thinking - is big misapprehension. He argues the necessity to bring into practice new term necessity to bring into practice new term „„adductionadduction“ - as the non-logical operation of “ - as the non-logical operation of “transition“ (of the „discover“ - in the best sense of “transition“ (of the „discover“ - in the best sense of this word) from the chaos prevailing in real world to this word) from the chaos prevailing in real world to intuitive guess or trial hypothesis concerning the intuitive guess or trial hypothesis concerning the factual interrelations between sets of relevant factual interrelations between sets of relevant variables.variables.

We take liberty to stand on position which We take liberty to stand on position which would "reconciled" J.S. Mill and K. Popper.would "reconciled" J.S. Mill and K. Popper.

1. Levels of Inductive 1. Levels of Inductive ConclusionsConclusions

It is proposed to consider different levels of hypotheses. It is proposed to consider different levels of hypotheses. Let distinguish, at least, two levels: Let distinguish, at least, two levels:

1)1) " "what does depend from whatwhat does depend from what" (Popperian " (Popperian conception, in the our opinion) and conception, in the our opinion) and 2)2) " " how it dependshow it depends" (position of J.S.Mill, we " (position of J.S.Mill, we suppose).suppose).

• Evidently, intuitive surmises, an experience, the talent of Evidently, intuitive surmises, an experience, the talent of researcher (i.e. the matters lying in sphere of psychology), researcher (i.e. the matters lying in sphere of psychology), and, sometimes, also metaphorical or associative and, sometimes, also metaphorical or associative conclusions, correspond to the conclusions, correspond to the first levelfirst level. On this level we . On this level we define a define a composition of properties which are means by those which are means by those the object (examined or designed) displayed itself.the object (examined or designed) displayed itself.• Inductive methods of Bacon-Mill and, certainly, methods of Inductive methods of Bacon-Mill and, certainly, methods of probability theory as well as mathematical statistics already probability theory as well as mathematical statistics already correspond to the correspond to the second levelsecond level. Here we define the structures . Here we define the structures of dependencies parametrically: of dependencies parametrically: which is the pattern of certain dependencies.

But refutaion of hypotheses generated on the first level But refutaion of hypotheses generated on the first level are also possible. Furthermore, on the first level it would are also possible. Furthermore, on the first level it would be reasonable to include "be reasonable to include "doubtful" (" (extra, additional) ) valiables (properties, parameters) specially.valiables (properties, parameters) specially. The first level would be defined as The first level would be defined as quantitative and the and the second one - as second one - as qualitative. That is why many of . That is why many of specialists-logicians consider the first level case as specialists-logicians consider the first level case as inductive reasonings (as they generate fundamentally new inductive reasonings (as they generate fundamentally new knowledge «in principle»); but they refuse to say in the knowledge «in principle»); but they refuse to say in the same way concerning the inference on the second levels. same way concerning the inference on the second levels. Logicians also propose to distinguish both Logicians also propose to distinguish both plausible and and probabilistic inference (which have strongly pronounced inference (which have strongly pronounced numeral measure of likelihood degreenumeral measure of likelihood degree).). Selected levels correspond to G. Klir's epistemological Selected levels correspond to G. Klir's epistemological levels of systems hierarchy.levels of systems hierarchy.

He considers five generalized levels of our He considers five generalized levels of our knowledge about systems: knowledge about systems:

0 0 – – source systems (describing the basic (describing the basic system properties), system properties),

11- - data systems (matrices of values that (matrices of values that correspond to properties of parameters), correspond to properties of parameters),

2 2 — — generative systems (models, rules, laws, (models, rules, laws, formulae etc., which describe the system's formulae etc., which describe the system's framework);framework); 3 3 — — structured systems (relations between (relations between the models for complex systems) the models for complex systems)

4 4 — — metasystems (relations between the (relations between the relations are biult below). relations are biult below).

y = f(x1, x2, x3, x4, x5, …), где y - … x1 - … x2 - … x3 - … x4 - … x5 - …

0 – source systems (describing the basic system properties)

у x1 x2 x3 x4 x5 …

1- data systems (matrices of values that correspond to properties of parameters)

y = a1x1 + a2x2 + a3x1x2 + a4x3 + …

2 — generative systems (models, rules, laws, formulae etc., which describe the system's framework);

Perhaps, it is not accidently that G. Klir called the Perhaps, it is not accidently that G. Klir called the

bottom level as "bottom level as "zero-levelzero-level". He does not indicate ". He does not indicate

which properties become the parameters of certain which properties become the parameters of certain

system. For him, probably, these matters refered to system. For him, probably, these matters refered to

the the BASIC AXIOMSBASIC AXIOMS. .

System analysis and general System analysis and general systems theorysystems theory also also

do not propose adequate methods. do not propose adequate methods.

Here we make remark that noted Russian Here we make remark that noted Russian

scientist in the field of inductive logic V.Finn scientist in the field of inductive logic V.Finn

developed and now uses his special method, named developed and now uses his special method, named

in honour of J.S.Mill and also quasi-axiomatic theory in honour of J.S.Mill and also quasi-axiomatic theory

for data systems, i.e. he for data systems, i.e. he considers that parameters considers that parameters

of system are defined previouslyof system are defined previously. .

Methods and aids of Data Mining also apply to Methods and aids of Data Mining also apply to

data matrices. These technologies are used for the data matrices. These technologies are used for the

knowledge generation when knowledge generation when some preliminary some preliminary

hypothesis about set of parameters that hypothesis about set of parameters that

characterizes the process examined or framework characterizes the process examined or framework

studied, is already known. studied, is already known.

It concerns to all methods based on It concerns to all methods based on

mathematical statistics, fir instance, maximum mathematical statistics, fir instance, maximum

likelihood and least squares methods, GMDH likelihood and least squares methods, GMDH

(„Group Method of Data Handling“) and others, („Group Method of Data Handling“) and others,

because because they work with the data matricesthey work with the data matrices. .

Thus, logic, mathematics, system analysis,

cybernetics, artificial intelligence and other more or

less "formalized" sciences examine generation

hypotheses' processes on the second level.

All such methods and are able to verify or refute All such methods and are able to verify or refute

hypotheses of the first level. hypotheses of the first level.

At present, processes of hypotheses At present, processes of hypotheses

synthesizing on the first level are subject of synthesizing on the first level are subject of

investigation in philosophy (gnoseology and investigation in philosophy (gnoseology and

epistemology), psychology, and, to bigger extent, in epistemology), psychology, and, to bigger extent, in

cognitive science.cognitive science.

Therefore it is interesting to investigate how Therefore it is interesting to investigate how

humans generate and justificate hypotheses. humans generate and justificate hypotheses.

2. How the humans build, 2. How the humans build, justificate and refute justificate and refute

hypotheseshypothesesBeyond dispute, an analysis of these processes Beyond dispute, an analysis of these processes

requires the separate deep and thorough requires the separate deep and thorough

investigation. investigation.

Here we will try to define, from our point of view, Here we will try to define, from our point of view,

only some basic positions. Earlier, one of us only some basic positions. Earlier, one of us

already touched these matters in already touched these matters in [Valkman Y.R. and

Bykov V. S. Deductive and non-deductive aspects of

imagery thinking modeling// Modeling and informational

technologies, The scientific works of IPME, Kiev. 2006,

Issue No 35, - pp. 87 – 96. (In Russian)].

In order to solve problems of different nature we make, In order to solve problems of different nature we make,

analyze and reject hypotheses (produced both by us and analyze and reject hypotheses (produced both by us and

other people). And at that time our thinking do not other people). And at that time our thinking do not

proceed in proceed in inductive, deductive, traductive or abductive

manner separately. For instance, „manner separately. For instance, „deductive methoddeductive method“ of “ of

Sherlok Holmes contained, Sherlok Holmes contained, per seper se, a very few of true , a very few of true

deduction — deduction — inductiveinductive and and abductiveabductive reasoning prevailed reasoning prevailed

in his conclusions. in his conclusions.

All such lexical labels were brought in operation by logicians All such lexical labels were brought in operation by logicians

to make classification and formalization of corresponding to make classification and formalization of corresponding

methods of reasoning. The introducing of one more term methods of reasoning. The introducing of one more term

""adductionadduction" (see " (see Blaug M. The Methodology of Economics: Or

how Economists Explain. 2nd Ed. Cambridge University Press, 1992..) )

is proposed by some researchers, but it is necessary to is proposed by some researchers, but it is necessary to

define the proper class of reasoning for this term. define the proper class of reasoning for this term.

We suppose these four classes of logic would be We suppose these four classes of logic would be enough for our analysis. In further studies we shall also enough for our analysis. In further studies we shall also consider consider retroduction ( (almost abductionalmost abduction) and ) and reduction ( (an an explanation of complicated things by more simple ones; explanation of complicated things by more simple ones; simplification or almost analogysimplification or almost analogy) in the processes of ) in the processes of production and justification of hypotheses. production and justification of hypotheses. Note that Note that abduction due to Peirce is a due to Peirce is a reasoning reasoning leading to leading to acception of hypotheses, which explain facts or which explain facts or input datainput data, аnd , аnd testing of introduced hypothesestesting of introduced hypotheses was called was called retroduction . In fact, according to C.S. Peirсe, the . In fact, according to C.S. Peirсe, the cognitive activity is a synthesis ofcognitive activity is a synthesis of ABDUCTION, INDUCTION ABDUCTION, INDUCTION and DEDUCTIONand DEDUCTION Obviously, at present time it is necessary to build the Obviously, at present time it is necessary to build the

general classifiergeneral classifier of existing types of logics and to of existing types of logics and to explore the set of certain formal constructions with explore the set of certain formal constructions with subseqent setting the accordance between them and the subseqent setting the accordance between them and the processes of natural thinking. processes of natural thinking.

Artificial intelligence deals with Artificial intelligence deals with • classicalclassical and and non-classical non-classical logic, logic, • monotonicmonotonic and and non-monotonicnon-monotonic reasoning, reasoning, • deductivedeductive and and non-deductivenon-deductive conclusions. conclusions.

Here we are interested, in a greater extent, in Here we are interested, in a greater extent, in CERTAIN and and PLAUSIBLE kinds of logical inference. kinds of logical inference. Certain inference is produced by Certain inference is produced by deductive reasoning, , whereas the plausible one is generated by all whereas the plausible one is generated by all others kinds of reasoning. . It is not accident that any mathematical proof represents It is not accident that any mathematical proof represents the deductive "chain". the deductive "chain". It also concerns the criminal evidence (see, for instance, a It also concerns the criminal evidence (see, for instance, a final of nearly every detective novel).final of nearly every detective novel). All other versions of reasoning are only plausibility. All other versions of reasoning are only plausibility.

Certainly, the hypothesis is always only Certainly, the hypothesis is always only plausible. But there are some exceptions.plausible. But there are some exceptions. For For example, the full (in particular, mathematical) induction.example, the full (in particular, mathematical) induction.

Earlier it was widely accepted to consider that only

inductive conclusions allow to generate a hypothesis. Then

C. S. Peirce proved "inconsistency" of the induction in many

cases and introduced [6] the notion of abductive conclusion.

Moreover, he believed that just this class of reasoning is

basic in hypotheses formation .

We shall emphasize the main thing, from our point of

view, the difference of abduction from induction.

By means of abduction the hypothesis is formed as the

cause of some (observable) event. There would exist more

than one such a cause. More often they (causes) are

connected by the operator OR.

With the help of induction the generalization of several

events is made. These events are bounded by operator AND.

From our point of view, From our point of view, TRADUCTIVE CONCLUSIONSTRADUCTIVE CONCLUSIONS are are not less important during process of generation of not less important during process of generation of hypotheses. We shall remind, that hypotheses. We shall remind, that traduction ( (from Latin traductio - moving) is the inference, where the premises and ) is the inference, where the premises and the conclusions are judgements of identical commonness, the conclusions are judgements of identical commonness, that is, the inference goes from knowledge of the certain that is, the inference goes from knowledge of the certain extent of commonness to new knowledge, but of the same extent of commonness to new knowledge, but of the same extent of commonness. extent of commonness. The The ANALOGY, which we frequently use both during , which we frequently use both during synthesis of a hypothesis, and in its justification or synthesis of a hypothesis, and in its justification or refutation, is traductive conclusion (refutation, is traductive conclusion (remind the dialogues of remind the dialogues of SoсratesSoсrates). Both ). Both METAPHOR and and ASSOCIATION are versions are versions of the conclusion by analogy. of the conclusion by analogy. And the power of these versions of conclusions in And the power of these versions of conclusions in formation of hypotheses (especially, original) is difficult to formation of hypotheses (especially, original) is difficult to overestimate.overestimate.

Relation of similarity lies in the basis of any model

Hence, the Hence, the modeling is the inference by analogymodeling is the inference by analogy.

It is not accidently that the theory of models (and

theory of categories) in mathematics, generally

deals with morphisms (conformity). In general, it is

possible to construct an opposition scale

"INFORMAL - FORMAL" concerning the similarity similarity

relationsrelations. • A metaphor would correspond to one

pole on this scale • and mathematical model - to another

pole.

Representation of “polar” (oppositional)” scale of SIMILARITY

(analogy)

(x, y)

(1, 0) (0, 1)

x, y (0, 1)x = 1 - y

«Mathematicalmodel»

«Metaphor»

Level of Formality for similarity relation

SФНFORMAL INFORMAL

Certainly, in practice any models can be the basis of

hypothesis concerning the process modeled.

We shall notice that the data matrix is also the model. Abduction is also a sort of traduction

It looks quite obviously that the person during generation

of hypotheses uses continuously all versions of available

reasoning, dynamically passing from one to another.

Thus we not always clearly realize what logical procedures

do help us to come to one or another hypothesis and how we

prove it.

Lawyers often use precedents (analogies) to prove their

argumentations. Humanitarian scientists give examples,

metaphors etc.

Remind, how we solve complicated tasks and problems.

The scheme of the relations between some classes of reasoning

In time

In space

Non-complete induction

ANALOGYABDUCTION

TRADUCTIONINDUCTION

REDUCTION

RETRODUCTION

By contiguity

By similarity By contrast

Statistical generalization

Analogy of objects ( things)

Structural analogy

Functional analogy

Causal («cause-effect») analogy

ASSOCIATION

ОБОБЩЕННАЯ СХЕМА ВИДОВ ИНДУКЦИИ

Math. induction Math. induction

KINDS OF INDUCTIONKINDS OF INDUCTION

Distinction method Distinction method

ScientificScientific

System-defined(by factors' selection)

System-defined(by factors' selection)

Popular(by simple enumeration)

Popular(by simple enumeration)

Complete induction Complete inductionIncomplete inductionIncomplete induction

Conjoint method oflikeness-distinction

Conjoint method oflikeness-distinctionLikeness methodLikeness method

Method of residuals Method of residuals

Method of attendant changes Method of attendant changes

3. One more view on induction. One more view on induction In philosophy and logic it is considered that the induction In philosophy and logic it is considered that the induction is higher form of the thinking as compared with deduction; is higher form of the thinking as compared with deduction; and it is directly related to creative, innovative style of and it is directly related to creative, innovative style of thinking resulted in new knowledge. thinking resulted in new knowledge. But in modern logic there is no unequivocal definition of induction.. Generally, the approaches to understanding the inductive Generally, the approaches to understanding the inductive inference are based on inference are based on contents analysis of situation. In In accordance to this view the methods of inductive inference accordance to this view the methods of inductive inference are grounded on some common philosophic prerequisites are grounded on some common philosophic prerequisites like as “like as “induction is the searching of the phenomenon's cause”" or "" or "deduction is the transition from general to particular, and induction is contrary to deduction". ". Despite their attraction, these prerequisites give us a very Despite their attraction, these prerequisites give us a very little benefit from standpoint of mathematical formalism of little benefit from standpoint of mathematical formalism of inductive inference, moreover, they are obviously deficient inductive inference, moreover, they are obviously deficient to map some features of innovative thinking by means of to map some features of innovative thinking by means of formal logic.formal logic.

Kulik BA.Kulik BA. proposes another way of inductive inference: proposes another way of inductive inference: formation of plausible hypotheses is accomplished by multivariate reconstruction of missing links in some deductive structure. That kind of deductive structure in view of its features That kind of deductive structure in view of its features “allows” to employ just a limited quantity of hypotheses of “allows” to employ just a limited quantity of hypotheses of infinite set of statements. It is clear that in this case at once infinite set of statements. It is clear that in this case at once several acceptable hypotheses can appear. Then the final several acceptable hypotheses can appear. Then the final choice of proper hypothesis can be made not on basis of choice of proper hypothesis can be made not on basis of the probability computations, but on basis of their the probability computations, but on basis of their contents contents analysisanalysis, when the knowledge that contained not apparently , when the knowledge that contained not apparently (implicitly) in initial deductive structure is used. (implicitly) in initial deductive structure is used. From our point of view, such an understanding of induction lies closer to Sharlock Holmes's reasoning manner and, per se, it appears as abduction.

4. Properties of hypotheses4. Properties of hypotheses It is impossible to take actions for problem solving without some hypothesis. Even in case of evident practical tasks their decision is made on the basis of the previous experience and skills acquisited, which forms a preliminary imagination or a pattern (an idea) of possible ways of solving. That is the hypothesis is such an imagination or an idea. It is necessary to note, that the hypothesis always contain bigger contents and greater explanatory power than data, which support hypothesis. As the hypothesis does not concern to individual judgments of experience and always exceeds them in contents, it cannot be proved only on the assumption of data. The empirical data just are able to disprove a hypothesis, but not to verify it. The hypothesis is prejudiced even though it contradict at least one fact. But each new hypothesis, as a rule, does not reject the contents of former hypotheses fully, but uses all rational considerations. The new hypothesis acts basically as perfected previous one.

In order to separate the In order to separate the most credible hypotheses from the from the initial conjectures some limitations are put upon their initial conjectures some limitations are put upon their formulations: formulations:

1.1. Тhe hypothesis has to be both Тhe hypothesis has to be both syntactically right and semantically understandable statement within certain text;;

2.2. The hypothesis has to be proved, to some extent, on The hypothesis has to be proved, to some extent, on previos knowledge or, in a case of its complete or, in a case of its complete originality, originality, not to contradict scientific knowledge;;

3.3. The hypothesis has to be not only verifiable in The hypothesis has to be not only verifiable in principle principle when the knowledge changes, but also must be checkable by available methods, i.e. it should , i.e. it should comply with development of scientific tools.comply with development of scientific tools.

The restriction mentioned above are both necessary and The restriction mentioned above are both necessary and sufficient conditions to qualify a hypothesis as the scientific sufficient conditions to qualify a hypothesis as the scientific utterance regardless of its truth or falsity in the future.utterance regardless of its truth or falsity in the future.

Scientific (and any other) idea does not start from

scratch. In order to submit a hypothesis to

consideration, somebody have to relate it to knowledge

existed before; just in that case this hypothesis could be

a subject of investigation and further testing.

Indeed, such a substantiation is not final, often the

different grounds are found for identical hypotheses.

But, this fact is only evidence that validity of the

hypothesis is the necessary requirement of its

acceptability — absence of due validity discredits the

hypothesis to such a degree, that it cannot then remain

the point of further discussion.

Thus, during the generation of hypotheses it is necessary to work not only with data, but to use knowledge bases where the experience of different experts from various knowledge domains accumulated, structured and systematized. These knowledge bases have to contain models, which are collected and systematized from previous case studies (both adequate to a problem and "not quite" adequate; see also above, about the third and fourth Klir's levels). Besides, for generation of hypotheses not only inductive reasonings (or methods of mathematical statistics), but also abduction and traduction should be used.

5. Some words on Data mining and Data Warehouses

Humans commonly try to understand their environment by simplification (reduction). In process of learning the human observes surrounding environment and defines interrelations between objects and events in this environment. Then person makes the grouping of similar objects into classes and builds rules, which predict the behaviour of such objects within certain class. Thus, such a model is always related to

• hypotheses generation, on the one hand• classification and recognition the other hand.

By similar way it is possible to learn computer. Studying and simulation of the process of learning is one of the research fields in Artificial Intelligence (AI) has the name machine learning.

As a rule, machine learning systems do not operate with isolated data, but they deal with the complete set of observations at once. Such a set named the learning set or learning sample. Data mining (analysis) from databases is one of having practical value methods during the searching the inductive dependencies from raw data. Although knowledge extraction from databases is a sort of machine learning, there exist some aspects of its considerable distinction from other machine learning applications.

1.1. The first and main distinction is that databases are designed without regard of needs of specialists in sphere of generalization. Knowledge extraction systems have to operate with fully prepared databases have been designed for the needs of other applications. There should not be program modules to ease the learning process in such systems. In decision support systems this problem led to In decision support systems this problem led to development ofdevelopment of Data Warehouse (DWH) ideology; they are they are specially oriented on information support of certain specially oriented on information support of certain processes.processes.

2.2. The second important distinction is that The second important distinction is that real databases often contain errors. While thoroughly matched data are . While thoroughly matched data are used for machine learning, algorithms of data extraction used for machine learning, algorithms of data extraction from databases have to deal with from databases have to deal with noisy and sometimes conflicting data.

6. Оn modeling hypotheses 6. Оn modeling hypotheses generation processgeneration process

Development of such a model is equivalent to Development of such a model is equivalent to creation of the truly universal problem solver or creation of the truly universal problem solver or creation of fully artificial intelligence. But sych a creation of fully artificial intelligence. But sych a target is not set.target is not set. As usually in computer technologies, we shall As usually in computer technologies, we shall allocate allocate resource (information) and (information) and procedural components. components.

☻ INFORMATION ENVIRONMENT.INFORMATION ENVIRONMENT. Any hypothesis Any hypothesis is a model. And the context of any model has the is a model. And the context of any model has the great impotnace foer it. We include in context great impotnace foer it. We include in context knowledge of model developer, information about knowledge of model developer, information about object modeled etc.; and all that should reflect object modeled etc.; and all that should reflect these matters in the knowledge base mentioned.these matters in the knowledge base mentioned.

Also it is necessary to Also it is necessary to Кalso it is necessary to Кalso it is necessary to gather and maintain in actual state the gather and maintain in actual state the databases of databases of input (mesuring) information input (mesuring) information , to analize and correct , to analize and correct its structure using procedures of data cleaning, its structure using procedures of data cleaning, consolidation and harmonization. consolidation and harmonization. And here, using the means and methods of And here, using the means and methods of inductive description of this basic (for synthesis and inductive description of this basic (for synthesis and approbation of hypothesis) information we would be approbation of hypothesis) information we would be able to speak about able to speak about Intelligent Data Warehouse Intelligent Data Warehouse (IDWH)(IDWH). .

Such a warehouse (by analogy with DWH) is Such a warehouse (by analogy with DWH) is oriented on hypotheses generation processes.oriented on hypotheses generation processes.

☻PROCEDURAL ENVIRONMENT.PROCEDURAL ENVIRONMENT. Here we mention Here we mention about very special component of intellectual about very special component of intellectual modeling, which V. Finn has namedmodeling, which V. Finn has named " "REASONER"". It . It is responsible for support of processes of the full is responsible for support of processes of the full life cycle of hypotheses. .

In the "In the "formalized” sciencesformalized” sciences it is reasonable to it is reasonable to consider six separate lasses of the processes consider six separate lasses of the processes connected to the life-cycle: connected to the life-cycle:

• generation of hypotheses, • their verification, • adjustment, • confirmation (substantiation), • use • refutation.

In modeling of all these processes the classes of In modeling of all these processes the classes of logic reasoning mentioned above are used.logic reasoning mentioned above are used.

Evolutionary epistemology considers the knowledge change process where the transition from non-knowledge to knowledge and from approximate solving of some problems to new problems statement. The basic formula of evolutionary epistemology is represented in the following way:

Р1 → ТТ → ЕЕ → Р2

Р2 new

problem

Р1 initial

problem

ТТ trial theory

ЕЕ elimination of errors from ТТ

Structure of intelligent system (Finn V. К.)

INTELLIGENT SYSTEMINTELLIGENT SYSTEM

Reasoner

Synthesizer

Calculator

Database(base of

facts)

Knowledgebase

Dialogue

Representation of results

(including grafic

interpretation)

Learning to work with system

Problemssolver

Informationenvironment

Intelligentinterface

Thus, use of Thus, use of multi-agent systems ideology is is preferable. Abductive conclusion is carried out by preferable. Abductive conclusion is carried out by one agent, inductive - by another one, and so on... one agent, inductive - by another one, and so on... The "reasoner" The "reasoner" plays a role of supervising systems. .

Basic difference of offered approach from others Basic difference of offered approach from others lies in creation of flexible integrated environment for lies in creation of flexible integrated environment for the complex objects modeling.the complex objects modeling. For modeling of these processes in computer For modeling of these processes in computer technologies it is necessary to develop the formal technologies it is necessary to develop the formal methodology, which provides the integration of all methodology, which provides the integration of all classes of inference models. classes of inference models. Such a methodology has to support synthesis Such a methodology has to support synthesis and analysis of hypotheses by means of continuous and analysis of hypotheses by means of continuous interaction of corresponded coherent processes of interaction of corresponded coherent processes of reasoning.reasoning.

Conclusion This issue concern the some actual problems of hypotheses generation. Several important aspect of the generation processes are considered. We suppose that deficient attention to the formation and justification hypotheses processes is one of important reason of low efficiency of many computer technologies. The clear understanding of different kinds of the logical inference as well as better vision of fundamental relations between logical, cognitive matters and theoretical grounds of systemological disciplines, we hope, could help to move forward . It seemed to be necessary the broad scientific (interdisciplinary) discussion on problems and matters

stated in this paper.

THANK YOU THANK YOU for attention!for attention!

Questions? Questions? Remarks?Remarks?