The distinction between being and non-being, one and multiple, unchangeable and constant could not but be accompanied by reflection on their essence and interconnection. Along with the dogmatism of religious faith, thinking consciousness developed, the area accessible to it steadily expanded, techniques and methods of cognition multiplied and improved. If, say, in the Brahmins knowledge was ritual, that is, it came down to memorizing certain formulas, recalling this or that image (mythologem), accompanied by ritual action, then the ancient Indian philosophical schools were characterized by the desire to develop methods of rational knowledge. Rationalism was fueled by doubt regarding the inflexibility of the authority of the Vedic tradition.

The heated debate that characterized especially the 7th-1st centuries BC led, on the one hand, to the development of methodological schemes capable of rejecting any position (ajnanavada - literally “agnosticism”), and on the other, to the development of pramanavada - the doctrine of means achievements of knowledge. The Samkhya school recognized three pramana: perception (pratyaksha), logical conclusion (anuman) and evidence of authority (shabda), the Nyaikas added a fourth pramana - “comparison”, and mimansa - “supposition”, etc.

The variety of epistemological theories of Ancient India is difficult to structure. And yet, the Russian scientist F.I. Shcherbatskaya, still the most authoritative researcher of Buddhist logic in the world, believed it was possible to divide them into two groups: realistic and non-realistic epistemology. The first included almost all directions of ancient Indian thought, except Buddhism. Realistic epistemology boiled down to the recognition of the knowability of the external world in its true reality: “The entire structure of the external world, its relationships and causality - all this is knowable through the senses. Intelligence, or reason, is a quality created in the soul of an individual with the help of special factors: it is not the essence of the soul. By inference the intellect cognizes the same objects which have already been cognized through the senses, but it cognizes them with a much greater degree of clarity and distinctness." Realist epistemology was most thoroughly developed and defended in the Nyaya Vaisesika.

The most decisive opposition to the realistic theory of knowledge was represented by Buddhist logic, the outstanding representative of which was Nagarjuna (2nd century) from the Madhyamika school. Buddhist logicians argued quite convincingly that there is truly nothing that is not relative in a certain aspect, and therefore the ultimate reality of everything that exists can be denied. The “own nature” of things is not involved in cause-and-effect relationships, and therefore it is located outside the world of phenomena, in the realm of eternal, unchanging essences, “participation” with which is impossible. Hence their unreality or emptiness: “There is no dharma that is not sunya.”

The lack of a clear distinction between being and non-being, reflecting the dominant position of naturalism in ancient Chinese philosophy, affected the peculiarities of the development of the theoretical-cognitive process here. Of the competing “hundred schools,” the school of Mohists (founder Mo-tzu, 468-376 BC) was most occupied with epistemology, striving to understand such metaphysical categories as being, space, time, quality, causality and etc. The Mohists tried to characterize the process of cognition as a process of revealing causality, identifying the similarities and differences of phenomena, and dividing things into categories. They put forward three rules for testing the truth of knowledge. The first is “foundation,” which meant the experience and judgment of the ancient sages. The second is the “source,” that is, “the facts that common people heard and saw.” The third is “applicability,” otherwise practical use.

Epistemological and logical problems were also considered in the school of names and by Xunzi (c. 313-238 BC). However, the naturalistic attitude of ancient Chinese philosophy, with its inherent underdevelopment of ideas about the ideal and the transcendental, hindered the development of formal logic and dialectics as independent disciplines. Chinese philosophers practically did not operate with the concept of “contradiction” (and without this, dialectics is impossible). But Chinese philosophers widely used the concept of opposition.

Philosophical terms were most widely used as a means of classification, establishing a hierarchical order of things. The lack of logical methodology was compensated by numerological methodology (xiang shu xue), consisting of numerical complexes and spatial structures connected to each other symbolically, associatively, aesthetically, etc. The standard numerological schemes were based on three fundamental numbers: 2, 3 and their sum 5. From birth, things are inherent in binary, trinity and quintuple, Scribe Mo argued back in 510 BC. The ontological equivalents of the two are the forces of yin and yang, the trinity is heaven, earth and man, the pentad is the five elements.

Thus, the methodology of knowledge in Ancient China developed along two main directions - numerological, genetically dating back to archaic culture and fortune-telling practice, on the one hand, and protological, on the other. The studies of Soviet sinologists show that the formally universal nature of numerological methodology created certain advantages for it compared to the protological tradition. As a result, the latter gradually by the end of the 3rd century BC. left the intellectual scene.

Epistemology is a branch of philosophy associated with the theory of knowledge, or the theory of reflection of reality in human consciousness.

Knowledge engineering as a science, so to speak, is doubly epistemological - reality (O) is first reflected in the consciousness of the expert (M 1), and then the activities and experience of the expert are interpreted by the consciousness of the knowledge engineer (M 2), which serves as the basis for the construction of the third interpretations (P z) – knowledge fields of the expert system (Fig. 17.8). The process of cognition is essentially aimed at creating an internal representation of the surrounding world in the human mind.

Rice. 17.8. Epistemological aspect of knowledge extraction

In the process of knowledge extraction, the analyst is mainly interested in the knowledge component associated with the non-canonical individual knowledge of experts, since subject areas with this type of knowledge are considered most susceptible to the implementation of expert systems. These areas are usually called empirical, since they have accumulated a large volume of individual empirical facts and observations, while their theoretical generalization is a matter for the future.

Knowledge is always associated with the creation of new concepts and theories. It is interesting that often an expert generates new knowledge “on the fly”, right in the context of a conversation with an analyst. Such generation of knowledge can also be useful to the expert himself, who until that moment may not have been aware of a number of relationships and patterns of the subject area. The analyst, who is the “midwife” at the birth of new knowledge, can be helped here by the tools of system methodology, which allows the use of well-known principles of the logic of scientific research and the conceptual hierarchy of science. This methodology forces him to see the general behind the particular, i.e. build chains:

FACT ® GENERALIZED FACT ® EMPIRICAL LAW ® THEORETICAL LAW

The knowledge engineer will not always reach the last link of this chain, but the very desire to move can be extremely fruitful. This approach is fully consistent with the structure of knowledge itself, which has two levels:

empirical (observations, phenomena);

theoretical (laws, abstractions, generalizations).

More on the topic GNOSEOLOGICAL ASPECT The essence of the epistemological aspect:

1. The concept of communication as a methodological principle for constructing an epistemological structure of the sociocultural space of the city

Ontology and epistemology are philosophical terms. Ontology defines the doctrine of being, of existence. The subject of ontology is the study of abstract and general philosophical categories, such as substance, cause, action, phenomenon.

Epistemology in philosophy is the theory of knowledge. The epistemological aspect combines methodological problems of obtaining new scientific knowledge, since the process of cognition is often accompanied by the emergence of new concepts and theories.

The applied nature of artificial intelligence has determined a number of meanings of ontology in its field:

Ontology as a system of categories that are a consequence of a certain view of the world;

Ontology as an informal system of conceptualization of knowledge, which involves constructing a description of a set of selected objects, concepts, connections and relationships in a given field of knowledge using formal or informal means;

Ontology is the representation of a conceptual system in the form of a logical theory, which means the use of a certain syntax to represent knowledge.

We see our peculiarities in the application of epistemological apparatus to the process of developing artificial intelligence knowledge bases.

In the process of developing a knowledge base, experts quite often form some patterns for the first time based on accumulated empirical experience. The main criteria for the quality of new knowledge are internal consistency, consistency, objectivity, and historicism.

In the process of knowledge extraction, analysts are primarily interested in the empirical knowledge of experts, which represents the results of observations that may be inconsistent. The internal consistency of empirical knowledge is characterized by the concept of modality, inconsistency, and incompleteness. The modality of knowledge refers to the possibility of its existence in various categories. Inconsistency is a natural property of empirical knowledge and cannot always be eliminated. On the contrary, inconsistency can serve as a starting point in experts' reasoning. Incomplete knowledge is associated with the possibility of an exhaustive description of any subject area.

Systematic knowledge is based on determining the place of new knowledge in a multi-level hierarchical organization. In this case, it is necessary to find answers to the questions: what concepts detail or generalize new knowledge and what relationships do they have with known facts and patterns?

It is almost impossible to determine the objectivity of knowledge. The processes of accumulation, description, presentation, processing, interpretation and assessment of the quality of knowledge are carried out by specific people, therefore their results are subjective. The objectivity of certain laws is often associated with the breadth of their applicability. The boundaries of this area can be established experimentally or theoretically, but not always. As indirect evidence of objectivity, the coincidence of the views of various experts and the confirmation of the put forward hypotheses by known facts are sometimes assumed.

Historicism of knowledge is associated with the development and change of ideas about the subject area over time.

The process of cognition can be represented by the following stages:

1. Description and generalization of facts;

2. Identifying connections between facts, formulating rules and patterns;

3. Construction of a knowledge model of the subject area;

4. Explanation and prediction of phenomena based on the model.

At the initial stages, a knowledge engineer, studying the structure of an expert’s inferences, can use different theories and approaches to build a formal model of domain knowledge. The most well-known and frequently used techniques are mathematical logic, association theory and Gestalt psychology.

An idealized model of domain knowledge is built on the basis of established logical connections between concepts. The model is formalized using a categorical apparatus, formal sign systems of mathematics and logic. To adequately display the real picture of the world in a model, a knowledge engineer must master such techniques as idealization, abstraction, and coarsening. The criteria for the quality of the constructed model is the ability of the information system to make predictions and explain many phenomena from a given subject area. The knowledge engineer must strive to ensure that the resulting knowledge model is sufficiently complete, coherent and consistent.

A short answer to the exam question for the course AI - artificial intelligence systems (all questions).

Epistemology is a branch of philosophy associated with the theory of knowledge, or the theory of reflection of reality in human consciousness. The epistemological aspect (A3) of knowledge extraction unites the methodological problems of obtaining new scientific knowledge, since when creating a knowledge base, an expert often formulates for the first time some patterns that until that moment constituted his personal experience.

Knowledge engineering as a science, so to speak, is doubly epistemological - first, reality (O) is reflected in the consciousness of the expert (M1), and then the activities and experience of the expert are interpreted by the consciousness of the knowledge engineer (M2), which serves as the basis for constructing the third interpretation ( P) - knowledge fields of the expert system.

Epistemological chain: fact -> generalized fact -> empirical law -> theoretical law.

This approach is fully consistent with the structure of knowledge itself, which has two levels:

1. Empirical (observations, phenomena).
2. Theoretical (laws, abstractions, generalizations).

Methodological criteria:

A3 = (S31, S32, 533) = (internal consistency, consistency, objectivity, historicism).

Internal consistency (S31)

Main characteristics of empirical knowledge:

S31 = (s31_i) = (modality, inconsistency, incompleteness)

The modality of knowledge means the possibility of its existence in various categories, that is, in the constructions of existence and obligation. In addition, we have to distinguish between such shades of modality as:

• the expert knows that...;
• the expert thinks that...;
• the expert wants...;
• the expert believes that... .

The possible inconsistency of empirical knowledge is a natural consequence of the basic laws of dialectics, and these contradictions should not always be resolved in the field of knowledge, but on the contrary, it is the contradictions that most often serve as the starting point in the reasoning of experts.

Incomplete knowledge is associated with the impossibility of a complete description of the subject area. The analyst’s task is to limit this incompleteness to a certain framework of “completeness,” that is, to narrow the boundaries of the subject area or introduce a number of restrictions and assumptions that simplify the problem.

Systematicity (S32)

The system-structural approach to cognition orients the analyst to consider any subject area from the standpoint of the laws of the systemic whole and the interaction of its constituent parts. Modern structuralism proceeds from a multi-level hierarchical organization of any object, that is, all processes and phenomena can be considered as many smaller subsets (features, details) and, conversely, any objects can (and should) be considered as elements of higher classes of generalizations.

Objectivity (S33)

The process of cognition is deeply subjective, that is, it significantly depends on the characteristics of the knowing subject himself. Thus, subjectivity begins with the description of facts and increases as the idealization of objects deepens.

Consequently, it is more correct to talk about the depth of understanding than about the objectivity of knowledge. Understanding is co-creation, the process of interpreting an object from the point of view of the subject. This is a complex and ambiguous process that takes place in the depths of human consciousness and requires the mobilization of all intellectual and emotional abilities of a person. The analyst should focus all his efforts on understanding the problem.

There is a well-known result in psychology that confirms the fact that people who quickly and successfully solve intellectual problems spend most of their time understanding it, while poor solvers quickly start looking for a solution and most often cannot find it.

Historicism (S34)

This criterion is related to development. Knowledge of the present is knowledge of the past that gave birth to it. And although most expert systems provide a “horizontal” slice of knowledge - without taking into account time (in statics), a knowledge engineer must always consider processes taking into account time changes - both the connection with the past and the connection with the future.

Having considered the main criteria for the scientific nature of knowledge, we will now try to describe its structure. The methodological structure of cognition can be presented as a certain sequence of stages.

The parameters (S3i) fit organically into this structure of cognition, which can be represented as a sequence of stages described below from the perspective of a knowledge engineer:

• E_1: description and generalization of facts;
• E_2: establishing logical and mathematical connections, deduction and induction of laws;
• E_3: construction of an idealized model;
• E_4: explanation and prediction of phenomena.

Epistemology is a branch of philosophy associated with the theory of knowledge, or the theory of reflection of reality in human consciousness.

Knowledge engineering as a science, so to speak, is doubly epistemological - reality (O) is first reflected in the consciousness of the expert (M1), and then the activities and experience of the expert are interpreted by the consciousness of the knowledge engineer (M2), which serves as the basis for constructing the third interpretation ( Pz) - knowledge fields of the expert system (Figure 11). The process of cognition is essentially aimed at creating an internal representation of the surrounding world in the human mind.

Figure 11 - Epistemological aspect of knowledge extraction

In the process of knowledge extraction, the analyst is mainly interested in the knowledge component associated with the non-canonical individual knowledge of experts, since subject areas with this type of knowledge are considered most susceptible to the implementation of expert systems. These areas are usually called empirical, since they have accumulated a large volume of individual empirical facts and observations, while their theoretical generalization is a matter for the future.

Knowledge is always associated with the creation of new concepts and theories. It is interesting that often an expert generates new knowledge “on the fly”, right in the context of a conversation with an analyst. Such generation of knowledge can also be useful to the expert himself, who until that moment may not have been aware of a number of relationships and patterns of the subject area. The analyst, who is the “midwife” at the birth of new knowledge, can be helped here by the tools of system methodology, which allows the use of well-known principles of the logic of scientific research and the conceptual hierarchy of science. This methodology forces him to see the general behind the particular, i.e. build chains:

FACT = - >= GENERALIZED FACT = - > EMPIRICAL LAW = - > THEORETICAL LAW.

The knowledge engineer will not always reach the last link of this chain, but the very desire to move can be extremely fruitful. This approach is fully consistent with the structure of knowledge itself, which has two levels:

1. empirical (observations, phenomena);

2. theoretical (laws, abstractions, generalizations).

Criteria of scientific knowledge

Theory is not only a coherent system for generalizing scientific knowledge, it is also a certain way of producing new knowledge. The main methodological criteria for being scientific, allowing new knowledge itself to be considered scientific, and the method of obtaining it are:

· internal consistency and consistency;

· systematic;

· objectivity;

· historicism.

Internal consistency. At first glance, this criterion simply does not work in empirical areas: in them, facts often do not agree with each other, definitions are contradictory, diffuse, etc. An analyst who knows the peculiarities of empirical knowledge, its modality, inconsistency and incompleteness, has to smooth out these “roughnesses” of empirics.

The modality of knowledge means the possibility of its existence in various categories, i.e. in the constructions of existence and obligation. Thus, some of the patterns are possible, others are obligatory, etc. In addition, we have to distinguish between such shades of modality as: the expert knows that...; the expert thinks that...; the expert wants...; the expert believes that...

The possible inconsistency of empirical knowledge is a natural consequence of the basic laws of dialectics, and these contradictions should not always be resolved in the field of knowledge, but on the contrary, it is the contradictions that most often serve as the starting point in the reasoning of experts.

Incomplete knowledge is associated with the impossibility of a complete description of the subject area. The analyst’s task is to limit this incompleteness to a certain framework of “completeness”, i.e. narrow the boundaries of the subject area, or introduce a number of restrictions and assumptions that simplify the problem.

Systematicity. The system-structural approach to knowledge (going back to Hegel) orients the analyst to consider any subject area from the standpoint of the laws of the systemic whole and the interaction of its constituent parts. Modern structuralism comes from a multi-level hierarchical organization of any object, i.e. all processes and phenomena can be considered as many smaller subsets (signs, details) and, conversely, any objects can (and should) be considered as elements of higher classes of generalizations.

Objectivity. The process of cognition is deeply subjective, i.e. it essentially depends on the characteristics of the knowing subject itself. Subjectivity begins with the description of facts and increases as the idealization of objects deepens.

Consequently, it is more correct to talk about the depth of understanding than about the objectivity of knowledge. Understanding is co-creation, the process of interpreting an object from the point of view of the subject. This is a complex and ambiguous process that takes place in the depths of human consciousness and requires the mobilization of all intellectual and emotional abilities of a person. The analyst should focus all his efforts on understanding the problem. Psychology confirms the fact that people who quickly and successfully solve intellectual problems spend most of their time understanding it, while those who quickly start looking for a solution most often cannot find it.

Historicism. This criterion is related to development. Knowledge of the present is knowledge of the past that gave birth to it. And although most expert systems provide a “horizontal” slice of knowledge - without taking into account time (in statics), a knowledge engineer must always consider processes taking into account time changes - both the connection with the past and the connection with the future. For example, the structure of the knowledge field and the knowledge base must allow adjustment and correction, both during the development period and during operation of the expert system.

Having considered the main criteria for the scientific nature of knowledge, we will now try to describe its structure. The methodological structure of cognition can be presented as a sequence of stages (Figure 12), which we will consider from the perspective of a knowledge engineer.

Description and synthesis of facts. This is like the “dry residue” of conversations between an analyst and an expert. Carefulness and completeness of keeping protocols during the extraction process and punctual “homework” on them are the key to a productive first stage of cognition.

In practice, it turns out to be difficult to adhere to the principles of objectivity and consistency described above. Most often, at this stage, facts are simply collected and, as it were, thrown into a “common bag”; An experienced knowledge engineer often immediately tries to find a “shelf” or “box” for each fact, thereby implicitly preparing for the conceptualization stage.

Figure 12 - Structure of cognition

Establishing connections and patterns. In the expert's head, connections are established, although often implicitly; The engineer’s task is to identify the framework of the expert’s conclusions. When reconstructing an expert's reasoning, a knowledge engineer can rely on the two most popular theories of thinking - logical and associative. At the same time, if the logical theory, thanks to its ardent admirers in the person of mathematicians, is widely cited and exploited in every possible way in works on artificial intelligence, then the second, associative theory, is less known and popular, although it also has ancient roots. The beauty and harmony of logical theory should not obscure the sad fact that people rarely think in terms of mathematical logic.

Associative theory represents thinking as a chain of ideas connected by common concepts. The main operations of such thinking are associations acquired on the basis of various connections; recalling past experiences; trial and error with occasional success; habitual (“automatic”) reactions, etc.

Construction of an idealized model. To build a model that reflects the subject’s understanding of the subject area, a specialized language is needed with which one can describe and construct those idealized models of the world that arise in the process of thinking. This language is created gradually with the help of the categorical apparatus adopted in the corresponding subject area, as well as formal symbolic means of mathematics and logic. For empirical, subject areas, such a language has not yet been developed, and the field of knowledge, which the analyst will describe in a semi-formalized way, may be the first step towards creating such a language.

Explanation and prediction of models. This final stage of the structure of knowledge is at the same time a partial criterion for the truth of the acquired knowledge. If the identified expert knowledge system is complete and objective, then on its basis it is possible to make predictions and explain any phenomena from a given subject area. Typically, knowledge bases of expert systems suffer from fragmentation and modularity (unrelatedness) of components. All this does not allow us to create truly intelligent systems that, being like humans, could predict new patterns and explain cases not explicitly indicated in the database. The exception here is knowledge generation systems, which are focused on generating new knowledge and “prediction”.

In conclusion, we list the most common failures associated with epistemological problems of knowledge engineering:

· scrappy, fragmented knowledge (due to violations of the principle of consistency or errors in choosing the focus of attention);

· inconsistency of knowledge (due to the natural inconsistency of nature and society, the incompleteness of the extracted knowledge, the incompetence of the expert);

· erroneous classification (due to incorrect determination of the number of classes or inaccurate description of the class);

· erroneous level of generalization (due to excessive detail or generalization of object classes).