Knowledge Representation Model Research Articles

Construction of a fuzzy model for managing the process of forming IT-competences

The relevance of this study is predetermined by the lack of models and algorithms for managing the formation of IT competencies in children of senior preschool age in the information and educational environment of a preschool organization. The object of the study is the process of managing the formation of IT competencies in children of senior preschool age. The problem under study is the need to build an effective system for assessing and managing the level of IT competencies, taking into account the uncertainty and subjectivity of assessments, as well as ensuring flexibility and adaptability of the process of developing competencies. The constructed model based on fuzzy logic takes into account the uncertainty of assessments and decomposes competencies into indicators of achievement (low, medium, high levels). The production model of knowledge representation based on fuzzy inference rules enables control adaptability. As a result, recommendations were compiled for assessing and developing competencies taking into account the individual characteristics of children and the educational environment. The approaches and tools proposed in this study help improve the quality of preschool education by providing teachers with effective tools for managing the educational process in the context of digitalization. The essence of the results is to build a model that makes it possible to integrate uncertain and subjective data into the process of assessing IT competencies, providing more accurate and objective assessments. The results could be used in the information and educational environments of preschool organizations, as well as in education systems that introduce modern technologies for assessing and managing competencies

Knowledge Inference Combining Convolutional Feature Extraction and Path Semantics Integration

Many knowledge representation models extract local patterns or semantic features using fact embeddings but often overlook path semantics. There is room for improvement in pathbased approaches that rely solely on single paths. A customized convolutional neural network (CNN) architecture is proposed to encode multiple paths generated by random walks into vector sequences. For each path, the feature sequence is then merged into a single vector using bidirectional long short-term memory (LSTM) by concatenating both forward and backward hidden states. Semantic relevance between different paths and candidate relations is computed using the attention mechanism. The state vectors of the relations are calculated using weighted paths. These paths help determine the probabilities of the candidate relations, which are then used to assess the validity of the triples. Link prediction experiments on two benchmark datasets, NELL995 and FB15k-237, demonstrate the advantages of our solution. Our model shows a 7.19% improvement at Hits@3 on FB15k-237 compared to Att-Model + Type, another advanced model. The model is further applied to a large complex dataset, FC17, as well as a sparse dataset, NELL-One, for few-shot reasoning.

An open source knowledge graph ecosystem for the life sciences.

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

The Construction of Urban Rainstorm Disaster Event Knowledge Graph Considering Evolutionary Processes

Rainstorm disasters pose a significant threat to the sustainable development of urban areas, and effectively organizing diverse information sources about them is crucial for emergency management. In light of recent advances in knowledge graph theory and application technology, their notable knowledge integration and representation capabilities may offer support for dynamic monitoring and decision-making processes concerning urban rainstorm disaster events. However, conventional models do not adequately capture the spatiotemporal characteristics of these events. To fill this gap, we analyze the essence of urban rainstorm disaster events and divide their evolution into four stages, namely, pregnant, development, continuous, and decline stages. On this basis, a multilevel knowledge representation model is proposed from four layers, which are event, object–state, feature, and relationship layers, by analyzing the components and dynamic characteristics from the mechanism of urban rainstorm disaster events. The proposed model can not only express the comprehensive structure and relationships within urban rainstorm events, but also emphasize the evolution of disaster events through a series of ordered states. Moreover, we test the utility of the constructed knowledge graph through a case study of the Zhengzhou 720 rainstorm. The case study first validates that the selected machine learning models can extract the urban rainstorm disaster event information accurately by comparing them with some mainstream models. Then, it demonstrates that the knowledge graph is practical in the field of disaster knowledge representation, and disaster condition retrieval. Additionally, since the knowledge graph can show the evolution of a disaster event throughout its full life cycle, it can promote the understanding of the mechanisms of urban rainstorm disasters and pave the way for future applications of disaster prevention and reduction.

Development of local knowledge cube of knowledge management system

The article presents the results of a study on the development of a knowledge management system in an enterprise based on the concept of knowledge management. Considered guesses on the functional scheme in the concept of the system. A scheme of the system is proposed by analogy with a local OLAP cube. A heuristic algorithm for detecting knowledge in data is constructed. A heuristic algorithm for detecting knowledge in data is constructed. A mathematical description of the algorithm based on the methods of first-order logic, the production model of knowledge representation and methods of knowledge discovery in data and intellectual analysis is given. Based on previous studies on the development of methods for extracting knowledge from data, a combined method is proposed that, when processing tables, forms a knowledge base. The inference engine checks the statement (hypothesis) in the received request from the request source. The algorithm is written in Python.

DEVELOPMENT OF AN ONTOLOGICAL MODEL OF DEEP LEARNING NEURAL NETWORKS

This research paper examines the challenges and prospects associated with the integration of artificial neural networks and knowledge bases. The focus is on leveraging this integration to address practical problems. The paper explores the development, training, and integration of artificial neural net- works, emphasizing their adaptation to knowledge bases. This adaptation involves processes such as in- tegration, communication, representation of ontological structures, and interpretation by the knowledge base of the artificial neural network's representation through input and output. The paper also delves into the direction of establishing an intellectual environment conducive to the development, training, and integration of adapted artificial neural networks with knowledge bases. The knowledge base embedded in an artificial neural network is constructed using a homogeneous semantic network, and knowledge processing employs a multi-agent approach. The representation of artificial neural networks and their specifications within a unified semantic model of knowledge representation is detailed, encompassing text-based specifications in the language of knowledge representation with theoretical semantics. The models shared with the knowledge base include dynamic and other types that vary in their capabilities for knowledge representation. Furthermore, the paper conducts an analysis of approaches to creating artificial neural networks across various libraries of the high-level programming language Python. It explores techniques for developing arti- ficial neural networks within the Python development environment, investigating the key features and func- tions of these libraries. A comparative analysis of neural networks created in object-oriented programming languages is provided, along with the development of an ontological model for deep learning neural net- works.

Разработка интеллектуальной системы с нечеткой стабилизацией управления в условиях неопределенности входных воздействий

Abstract. The problems of complex control systems functioning under conditions of input influences uncertainty are discussed. Existing approaches to solving these problems, based on the use of fuzzy control methods, adaptive control and intelligent algorithms, are described. The synthesis of an intelligent system with fuzzy stabilization control under conditions of input influences uncertainty is carried out to increase the efficiency of the object’s functioning. The use of fuzzy control makes it possible to take into account the input influences uncertainty and stabilize control based on a production model of knowledge representation, which makes the system more flexible and resistant to change. Optimal defuzzification method dynamic selection for the purpose of fuzzy stabilization control ensures the effective functioning of each specific system. An example is considered that implements an intelligent system with fuzzy stabilization of electric current control for an electroplating process with uncertainty about its duration, part area, temperature and acidity of the electrolyte. To confirm the effectiveness of the developed intelligent system, a computational experiment is carried out on the example of controlling the electroplating process of applying nickel coating in a Watts electrolyte using fuzzy inference by the Mamdani algorithm with the triangular norm and the Zadeh conorm. The results show that using an intelligent system with fuzzy stabilization control leads to a more accurate result (in terms of coating thickness) compared to using the most common defuzzification methods (centroid; bisector; smallest, middle and largest of maximum) on your own.

Planning Purposeful Activities Autonomous Intelligent Robot with Knowledge Update in Short-Term Memory

The main problems associated with the creation of autonomous intelligent robots capable of performing various complex tasks in a priori undescribed unstable problematic environments, based on the processing of knowledge presented in an abstract way, are outlined. To store typical elements of an abstract knowledge representation model, the article recommends using long-term and short-term memory. Long-term memory with associative search and data retrieval is designed to permanently store information necessary for planning a variety of purposeful activities that provide the robot with the ability to solve various complex behavioral tasks. In short-term memory, submodels of knowledge representation are entered from long-term memory, which are necessary for solving the current task of a certain type in the short term, related to the fulfillment of the task formulated for the autonomous intelligent robot. At the same time, with each change in the type of the current task of behavior being solved by an autonomous intelligent robot, a corresponding update of knowledge stored in short-term memory is simultaneously carried out. Original constructions of typical elements of the model for representing abstract knowledge in the form of various behavioral skills, set regardless of a particular subject area, have been developed. This approach to building a knowledge representation model allows autonomous intelligent robots to adapt to the current operating conditions and, on this basis, organize purposeful activities in complex unstable problematic environments. Various tools and rules for processing abstract knowledge are proposed, which endow autonomous intelligent robots with the ability to eliminate the differences between the current and target situation of the problem environment both in terms of the values of structurally equivalent relations of the same name in them, and in the current states of objects in the environment. This, in turn, makes it possible to create intelligent problem solvers for autonomous intelligent robots for various purposes, capable of performing complex tasks in unstable a priori uncertain conditions of a problematic environment.

Deep Fuzzy Cognitive Maps for Defect Inspection in Antenna Assembly

Regardless of the tremendous technological development in manufacturing processes and equipment, assembly errors are an emerging concern. Hence, quality inspection software is of high importance for capturing defects at the source and preventing further error propagation. The advantages of Fuzzy Cognitive Maps (FCMs) as knowledge representation models with explainable capabilities can be compounded with the advantages of Deep Convolutional Neural Networks (CNNs) in order to acquire accurate and interpretable inspection results in Smart Manufacturing. In this work, an explainable binary classification approach is performed, namely DeepK-FCM, and appears to be able to assess sufficiently industrial images from TELEVES. The suggested DeepK-FCM methodology, inheriting the powerful characteristics of deep learning and FCMs, includes a number of steps as follows: fine-tuning of well-known CNNs for feature extraction with transfer learning, feature clustering by performing K-Means algorithm, definition of causal relationships through fuzzy measures on similarities produced and FCM model for the decision-making. Through the experimental analysis on a real industrial data set, it is being proved that the current approach with DeepK-FCM is more efficient for the task of defect inspection in the antenna assembly than the straightforward binary classification accomplished by CNNs. The attained accuracy of 80% is improved by 3% compared to the state-of-art CNN classifiers and indicates a high potential even when the data are scarce. In addition, the decision-making with FCMs levitates the interpretability of the system which contributes to the efforts towards a more explainable AI method for quality control inspection.

Low-code development using requirements and knowledge representation models

Low-code development using requirements and knowledge representation models

AugGKG: a grid-augmented geographic knowledge graph representation and spatio-temporal query model

ABSTRACTAs an emerging knowledge representation model in the domain of knowledge graphs, geographic knowledge graph can take full advantage of semantic, spatial and temporal information to facilitate answering spatio-temporal questions and completing relations. However, the representation of geographic knowledge graphs still has issues such as the difficulty of unified heterogeneous spatio-temporal data modelling, weak ability to answer spatio-temporal queries for dynamic multiobjective problems, and low efficiency of graph querying. This paper presents a grid-augmented geographic knowledge graph (AugGKG) based on the GeoSOT global subdivision grid model and time slice subgraph architecture. AugGKG discretely normalizes the spatio-temporal data of the graph, which involves five types of nodes and two types of relations. By using the geo-hidden layer of the graph and geocoding algebraic operations, the AugGKG can quickly answer complex multiobjective spatio-temporal queries and complete implicit spatio-temporal relations. Compared with existing geographic knowledge graphs (YAGO, GeoKG and GEKG), the comparative experiments verified the obvious advantages of AugGKG in terms of uniformity of accuracy, completeness, and efficiency. Hence, AugGKG is expected to be regarded as an innovative and robust geographic knowledge graph that can perform fast computation and relation completion for complex spatio-temporal queries in future geospatial question answering applications.

Design of a Cognitive Knowledge Representation Model to Assess the Reasoning Levels of Primary School Children

In recent days, the research on student’s intelligence level modelling is a challenging Artificial Intelligence (AI) task, which gains more attraction because it provides actionable insights to the tutor by analyzing the intelligence level of the learners. Each learner’s knowledge, comprehension, and intellectual capacities are unique. It is critical to identify these capacities and provide learners, particularly slow learners, with the necessary knowledge. Cognitive Performance Test (CPT) is an essential component for assessing the knowledge level of students. The reasoning level or coefficient deals with the analysis of the thinking capability in a logical way. It also reflects the child’s learning potential. The main aim of the proposed system is to design a Cognitive Knowledge Representation Model (CKRM), which fuses Cognitive Performance Metrics (CPM) calculation and Reasoning Coefficient Calculation (RCC) algorithms to assess the student’s intelligence level. The result of the proposed system is stratification of students to three different ranges of reasoning coefficient. The CKRM consists of the following phases: data collection, statistical Exploratory Data Analysis (EDA), model building and analysis, which involve the assessment of the knowledge level using CPT and calculation of reasoning coefficient using First Order Logic (FOL), and finally model evaluation using cognitive evaluation metrics. CPM and RCC algorithms have been proposed in this paper to calculate the student’s reasoning coefficient by using the forward chaining FOL inference engine. The dataset is a real time data which consists of the academic and cognitive performance details of school students from classes 1 to 6 for the year 2019 to 2020. The academic data are collected from the Educational Management Information System (EMIS) maintained by the school. The cognitive performance data are collected by conducting the tests for the students using the memory training application called Lumosity. The proposed system’s performance is evaluated using ten Machine Learning (ML) algorithms in which the Quadratic Discriminant Analysis achieved an accuracy of 0.97 for classes 1, 2, and 3. For classes 4, 5, and 6, nearly twelve ML algorithms are evaluated in which Random Forest (RF) Classifier achieved an accuracy of 0.98. Six math expert committee teachers concluded that the reasoning coefficient value was acceptable with an average accuracy of 0.92 for classes 1, 2, 3 and 0.9 for classes 4, 5, 6. In comparison to the pre-existing models employed in the prior research, it was determined that the created CKRM (academic and cognitive) was superior. The cognitive metrics such as taskability, Response Time (RT), knowledge capacity and utilization has also been evaluated. The average values of taskability, RT, knowledge capacity and knowledge utilisation are 0.85, 0.81, 0.55, and 0.44. The ultimate goal is to make customised teaching easier; hence, this article involves determining a student’s cognitive level by estimating their reasoning coefficient. The suggested approach analyses and categorises students’ cognitive abilities, such as memory, reasoning, problem solving, thinking, and logical reasoning, using three different reasoning coefficients. This approach assists teachers in determining the degree of intelligence of their students.

An analysis of knowledge representation methods in intelligent decision-making support systems

The scientific task, which is solved in the research, is the analysis of knowledge representation methods in intelligent decision-making support systems. The problem is explained by the fact that the form of knowledge representation significantly affects the characteristics and properties of the system. In order to operate all kinds of knowledge from the real world with the help of a computer, it is necessary to carry out their simulation. In such cases, it is necessary to distinguish knowledge intended for processing by computational devices from knowledge used by humans. In addition, with a large amount of knowledge, it is desirable to simplify the sequential management of individual elements of knowledge. A homogeneous representation leads to a simplification of the logic management mechanism and a simplification of knowledge management. The research is aimed at the analysis of knowledge representation methods in intelligent decision-making support systems. Currently, many models of knowledge representation have been developed. The main models include: logical models; frame model; network models (or semantic networks); production models. Therefore, the object of research is the intelligent decision-making support system. The subject of research is an intelligent decision-making support system. The following is set: – the methods (models, approaches) presented in the research for presenting knowledge in intelligent decision-making support systems in a canonical form are not advisable to use for a number of objective reasons given in subsection 3.1 of the research; – it is necessary to develop new (improvement of existing) representations of knowledge in intelligent decision-making support systems, which will have the advantages of these approaches without their disadvantages. Further improvement of these approaches to reduce the number of shortcomings and limitations of their application should be considered as the direction of further research.

Knowledge Graph Construction to Facilitate Indoor Fire Emergency Evacuation

Indoor fire is a sudden and frequent disaster that severely threatens the safety of indoor people worldwide. Indoor fire emergency evacuation is crucial to reducing losses involving various objects and complex relations. However, traditional studies only rely on numerical simulation, which cannot provide adequate support for decision-making in indoor fire scenarios. The knowledge graph is a knowledge base that can fully utilize massive heterogeneous data to form a sound knowledge system; however, it has not been effectively applied in the fire emergency domain. This study is a preliminary attempt to construct a knowledge graph for indoor fire emergency evacuation. We constructed the indoor fire domain ontology and proposed a four-tuple knowledge representation model. A knowledge graph was constructed with 1852 nodes and 2364 relations from 25 indoor fire events. The proposed method was tested for the case study of Henan Pingdingshan ‘5.25’ Fire Accident in China. Results show that the proposed knowledge representation model and the corresponding knowledge graph can represent complicated indoor fire events and support indoor fire emergency evacuation.

TransRFT: A Knowledge Representation Learning Model Based on a Relational Neighborhood and Flexible Translation

The use of knowledge graphs has grown significantly in recent years. However, entities and relationships must be transformed into forms that can be processed by computers before the construction and application of a knowledge graph. Due to its simplicity, effectiveness, and great interpretability, the translation model lead by TransE has garnered the most attention among the many knowledge representation models that have been presented. However, the performance of this model is poor when dealing with complex relations such as one-to-many, many-to-one, and reflexive relations. Therefore, a knowledge representation learning model based on a relational neighborhood and flexible translation (TransRFT) is proposed in this paper. Firstly, the triples are mapped to the relational hyperplane using the idea of TransH. Then, flexible translation is applied to relax the strict restriction h + r = t in TransE. Finally, the relational neighborhood information is added to further improve the performance of the model. The experimental results show that the model has good performance in triplet classification and link prediction.

Visualization design of talent training paths in university education based on pattern knowledge

Abstract In the background of big data informatization, social and economic development needs talent support, and colleges and universities are the main positions of talent training. Using the features of self-organization, distributed multi-intelligence and positive feedback of the ant colony algorithm, a personalized learning path recommendation algorithm supporting pattern knowledge is proposed by combining the above learning feature model. The subject starts an independent solution search simultaneously at multiple points in the problem space, which increases the algorithm’s reliability and gives the algorithm a strong global search capability. When performing talent development path recommendation, the collaborative mechanism of distributed multi-subjects of the ant colony algorithm makes it possible to find an acceptable path quickly. Combined with the new knowledge representation model, the matching degree of students and learning objects is calculated from three dimensions, the heuristic information is obtained accordingly, and the Euclidean distance is used for matching degree calculation. Using the ant colony optimization algorithm to analyze the paths for cultivating talents in college education, it is concluded that in terms of the number of teachers: the total number of teachers with positive senior titles in five colleges and universities is 238, accounting for 8.88%, and the number of full-time teachers with senior associate titles is 618, accounting for 23.07%, all of which are lower than the average level in Fujian Province. This study has some reference significance to the cultivation of applied talents in local colleges and universities and has some reference value to the government in formulating the policy of cultivating talents in colleges and universities.

Construction and application of knowledge graph for construction accidents based on deep learning

PurposeLearning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.Design/methodology/approachThe ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.FindingsThe experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.Originality/valueThe proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.

Training Novel Adaptive Fuzzy Cognitive Map by Knowledge-Guidance Learning Mechanism for Large-Scale Time-Series Forecasting.

A fuzzy cognitive map (FCM) is a graph-based knowledge representation model wherein the connections of the nodes (edges) represent casual relationships between the knowledge items associated with the nodes. This model has been applied to solve various modeling tasks including forecasting time series. In the original FCM-based forecasting model, causal relationships among concepts of the FCM remain unchanged. However, causal relationships may change in time. Therefore, we propose a new learning method for training an FCM resulting in an adaptive FCM which consists of several sub-FCMs. It can select different sub-FCMs at different moments. In an active processing scenario, in which we deal with a large-scale time series with new data being continuously generated, a forecasting model built on the old data should be updated when the new data arrive. Furthermore, retraining an FCM from scratch entails increasing computing overhead that will become a serious obstacle in many practical scenarios. To overcome the above-mentioned shortcomings, this study offers an original design setting in which the FCM is updated by knowledge-guidance learning mechanism for the first time. Compared with the existing classical forecasting models, the proposed model shows higher accuracy and efficiency. Its increased performance is demonstrated through a series of reported experimental studies.

Логическая модель построения пошаговой контекстной помощи пользователю САПР

Automatic stepwise contextual help for CAD systems users reduces time to solve the application task since it saves time to search the prompt message in system documentation. Petri nets (PN) can be used to bind available actions of the user considering the state of application data (context). Application of the Petri net inversion method, which uses limited enumeration to construct chains of recommended actions is preferable than using the standard reachability analysis procedure based on exhaustive enumeration. However, the absence in the known implementations of an explicit separation of the axioms of inversion (knowledge) from the mechanism of their processing (inference) deprives the stepwise contextual help system of the necessary flexibility when changing the axioms to consider the assumptions associated with a particular model. Thus, the aim of this research is to provide the necessary flexibility of the contextual help system by separating the knowledge representation model from the inference engine. A colored PN is used as a model of user action scenarios. The inversion axioms are implemented in the PROLOG language. The standard inference engine of the PROLOG language is used as a tool to construct chains of recommended actions. The authors have proposed an axiomatic model of PN inversion and a method to construct a stepwise contextual help by the standard inference engine of the PROLOG language. The method differs by explicit separation of knowledge (inversion axioms) from the inference engine (stepwise recommendations). It reduces the computational costs of adapting the contextual help system when changing the inversion axioms. The proposed method allows to reduce the time spent on adapting the contextual help system, since the field of the changes is limited by the declarations of the inversion axioms. The reliability of the results is confirmed since the proposed method of contextual help is used for the CAD “Model and Archive” user of CSoft company. The results obtained allow creating contextual help services for existing applications with minimal changes to their code base.

Cognitive Tools for Abstract Thinking Autonomous Intelligent Mobile Systems

The actual problems of artificial intelligence related to the development of tools for abstract thinking of autonomous intelligent mobile systems are being solved, which allow planning purposeful behavior in hard-to-reach and aggressive environments for humans. Cognitive tools are proposed that provide intelligent systems with the ability to organize purposeful multi-stage activities related to solving complex problems, when a behavior plan is automatically built in some conditions of a problematic environment, and a given behavior goal is achieved in other operating conditions that are beyond the resolution of technical vision. An important feature of the proposed typical elements of knowledge representation and processing is that they allow intelligent systems to organize the output of solving complex problems, relying only on the data stored in the knowledge representation model and coming from the current operating conditions. In the general case, the developed knowledge model of intelligent systems for various purposes consists of declarative and procedural typical elements of their representation. For a formal description of typical elements of declarative knowledge representation, traditional semantic networks and various sets of restrictions are used, reflecting additional conditions for the future functioning of autonomous mobile intelligent systems. As for the formal description of the typical elements of the representation of procedural knowledge, regardless of a specific subject area, fuzzy semantic networks are used for this. This allows autonomous intelligent mobile systems to adapt to specific operating conditions in underdetermined problematic environments and perform complex tasks formulated by them on this basis. The practical significance of the results obtained lies in the effectiveness of their use for the development of intelligent problem solvers that provide autonomous intelligent mobile systems for various purposes with the ability to perform complex tasks in a priori underdetermined problematic environments by adapting the purposeful activity plan formed in general form to specific current operating conditions.