VARIOUS ASPECTS OF INTELLIGENT COLLABORATIVE EDUCATIONAL SYSTEMS

Introduction: We discuss the modern ways of developing intelligent problem solvers, focusing on their shortcomings in terms of the efficiency of their application for planning purposeful behavior of autonomous mobile intelligent systems in a priori undescribed conditions of a problem environment. Purpose: Developing a model of knowledge representation and processing which would provide the ways to organize purposeful activity of autonomous intelligent mobile systems in uncertain environment. Methods: Synthesis of frame-like behavior scenarios in the form of polyvariable conditionally dependent predicates whose structure includes complex variables as well as related variables of types “object”, “event” and “relationship”; synthesis of heuristic rules for knowledge representation in the process of purposeful behavior planning. In order to represent complex variables in polyvariable conditionally dependent predicates, fuzzy semantic networks are used which can represent knowledge of variously purposed intelligent systems without regard to particular knowledge domains, being adaptable to a priori undescribed operational conditions. Results: We have proposed a structure of various polyvariable conditionally dependent predicates. On their base, an autonomous intelligent mobile system can organize various activities in a priori undescribed and unstable problem environments. Specially developed knowledge processing tools allow such a system to automatically plan its purposeful behavior in a space of subtasks during the fulfilment of tasks formulated for it. Practical relevance: The obtained results can be efficiently used in building intelligent problem solvers for autonomous intelligent mobile systems of various purpose, capable of performing complex tasks in a priori undescribed operational conditions.

The subject of research in the article is the processes of constructing explanations in intelligent systems based on the use of causal dependencies. The aim is to develop a hierarchical representation of causal relationships between the actions of an intelligent system to form an explanation of the process of the system's operation with a given degree of generalization or detailing. Representation of the hierarchy of cause-and-effect relationships allows you to form an explanation at a given level of detail using the input data in the form of a temporally ordered sequence of events reflecting the known actions of an intelligent system. Tasks: structuring the hierarchy of cause-and-effect relationships for known variants of the decision-making process in an intelligent information system, considering the temporal ordering of the corresponding actions; development of a model of a multi-level representation of causal dependencies for description for explanations in an intelligent system. The approaches used are: counterfactual analysis of causality, used to describe alternative dependencies for possible decision-making options; linear temporal logic to reflect the temporal aspect of causation. The following results were obtained. A generalized hierarchy of cause-and-effect relationships is highlighted for the known variants of the process of obtaining recommendations in an intelligent information system based on the temporal ordering of the corresponding decision-making actions. A model of hierarchical representation of causal dependencies has been developed to describe explanations in an intellectual system with a given degree of detail. Conclusions. The scientific novelty of the results obtained is as follows. A model of hierarchical representation of time-ordered causal relationships is proposed to describe the explanations of the operation of an intelligent system with a given degree of detail. At the top level of the hierarchy, the model defines a generalized causal relationship between the event of using the input data and the event of the result of the system's operation. This connection describes the current task that the intelligent information system solves. At the lower level, cause-and-effect relationships are set between events sequential in time, between which there are no other events. At intermediate levels of the hierarchical representation, the causal dependencies of pairs of events are determined, between which there are other events. The developed model creates conditions for constructing explanations with a given degree of detailing of the actions of the decision-making process in an intelligent system. The model also provides the ability to describe early and late anticipation of alternative sequences of the decision-making process by describing causal dependencies for events between which there are other events.

The paper presents an approach to the development of a software environment for creation of intelligent systems. This environment is based on an integrated knowledge representation model that combines both classic knowledge representation means and constraint programming methods with agent-based techniques. In contrast to other systems following constraint programming methods, the environment presented here allows one to manipulate objects with imprecise (subdefinite) values of attributes. The process of refining such subdefinite attributes is performed by means of a special data-driven mechanism. Another feature of the environment is the use of an agent-based technique instead of the traditional production rule technique, in order to increase efficiency of the processes of logical inference and data processing. Due to a natural combination of the data-driven and event-driven mechanisms, the environment can be used for the development of efficient intelli-gent systems for various applications.

The aim of the study is to develop a model of consistent knowledge about the decision-making process in intelligent systems, which would consider their logical consistency and provide an opportunity to explain the process and outcome of such systems. The object of study is the process of constructing explanations in intelligent systems. Research objectives: development of a generalized approach to the harmonization of knowledge to build explanations in intelligent information systems; development of a model for the presentation of consistent knowledge, considering their logical consistency. The following results were obtained. A generalized approach to the harmonization of knowledge to build explanations in intelligent information systems has been developed. The approach provides the possibility of forming a set of agreed explanations that reflect different aspects of the decision-making process and the result obtained by the intelligent system, which simplifies the application of these results in the subject area of the user. The model of representation of the coordinated knowledge considering their logical consistency is offered. The model makes it possible to check the consistency of knowledge directly in the formation of explanations, which creates conditions for the formation of explanations on the principle of the black box and allows to supplement functioning systems with explanations, as well as to adapt explanations when changing their functionality.

One of the problems that slow down intelligent information systems development and industry- wide spread is so-called knowledge bottleneck. Machine learning for various uncertain knowledge representations and models used in intelligent systems is a promising way to cope with the bottleneck. Algebraical Bayesian networks are a probabilistic graphical model that allows for representing and processing interval estimates of probabilities. The paper goal is to describe the machine learning task in regard to a knowledge pattern of an algebraical Bayesian network as well as to present a few ways to solve the task and to outline obstacles related to those ways.

Acoustics is emerging as a significant complementary modality to be explored and exploited in the development of Intelligence and surveillance systems that traditionally rely on technology rooted in electro-magnetic field phenomena. Acoustics is emerging as a significant complementary modality to be explored and exploited in the development of Intelligence and surveillance systems that traditionally rely on technology rooted in electro-magnetic field phenomena. An application of the current interest is the detection and localization of the sound sources on battlefield using Acoustic sensors in ground and on board unmanned aerial vehicle. In this work a nonlinear least-square cepstrum and auto correlation methods are made to estimate the motion parameters of a low flying aircraft whose narrowband acoustic energy emissions were received by a ground-based Acoustic vector sensor. The data obtained from the sensor were processed and analyzed using digital signal processing approach. This passive technique is applied to real acoustic sensor data under the condition that the vehicle flies at a constant velocity and the trajectory is a straight line. The performances of both methods are evaluated and compared using actual acoustic data. Defence Science Journal, 2014, 64(2), pp. 95-98. DOI: http://dx.doi.org/10.14429/dsj.64.3924

Knowledge representation is the core of artificial intelligence research. Knowledge representation methods include predicate logic, semantic network, computer programming language, database, mathematical model, graphics language, natural language, etc. To establish the intrinsic link between various knowledge representation methods, a unified knowledge representation model is necessary. According to ontology, system theory, and control theory, a standard model of knowledge representation that reflects the change of the objective world is proposed. The model is composed of input, processing, and output. This knowledge representation method is not a contradiction to the traditional knowledge representation method. It can express knowledge in terms of multivariate and multidimensional. It can also express process knowledge, and at the same time, it has a strong ability to solve problems. In addition, the standard model of knowledge representation provides a way to solve problems of non-precision and inconsistent knowledge.

Computer-based educational systems are becoming increasingly important in all levels and types of education. Intelligent Educational Systems (IESs) are computer-based educational systems encompassing Artificial Intelligence schemes to increase the effectiveness of the learning process. In this paper, we survey patents regarding IESs. As IESs we consider either Intelligent Tutoring Systems (ITSs) or Adaptive Educational Hypermedia Systems (AEHSs) incorporating intelligent techniques. We first present indicative functions implemented in the three basic components of an IES: the domain knowledge, the user (or student) modeling unit and the pedagogical module. Afterwards, we focus on representative patents concerning IESs and present the corresponding work. At the end, we outline current and future developments regarding IESs. Keywords: Intelligent tutoring systems, adaptive educational hypermedia systems, intelligent educational systems, artificial intelligence in education, intelligent E-learning systems, intelligent computer-based training systems, artificial intelligence in Elearning

In this paper two observations are the starting points for a proposal as to how learning, rather than computing considerations, can be made the main focus in the development of Intelligent Educational Systems (IESs). These observations are: a) the predominant use of computers in education is for the running of general tool software; and b) the symbolic approach to Artificial Intelligence is being questioned from a situated cognition perspective.Learning interactions are seen as consisting of a Task Level, and a higher‐level Discussion Level comprising planning and evaluation of, and reflection about, Task Level activity. It is proposed that IESs should be developed by adding a module to support Discussion Level interaction with the learner. The Mayday project is described, in which expert human teachers are being studied as they support Discussion Level with a learner working at computer‐based lexical activities. It is argued that such studies of human learning is a necessary step towards the development of the proposed IESs.

Computational semiotics is a quite new field of research, which emerged from advanced studies on artificial intelligence and intelligent systems. It is an alternative to the mainstream approaches - those based either on cognitivism or on biologically inspired techniques. Instead, computational semiotics seeks its inspiration on semiotics, a tradition on the philosophy of mind dealing with the concepts of representation and communication from a more technical perspective. The whole story besides this new field of research is usually unknown from many researchers working on the field of intelligent systems. This story started during the 1970's, where two main precursors first published works relating semiotics to intelligent systems development, almost at the same time in Russia and in the U.S. These pioneering studies were performed by Dmitri Pospelov in Russia and Eugene Pendergraft in U.S. Despite promising research results, for diverse reasons they stayed ignored from the mainstream in artificial intelligence for about 25 years. However, around 1995 they were rediscovered, motivating the development of this new paradigm of research. The goal of this paper is to briefly review the main contributions in this area.

This paper concerns a new class of systems that are, on the one hand, dynamic systems, and on the other, intelligent systems, as their dynamics is described by knowledge representation, which is a common practice to use in intelligent systems, and the states are described by parameters and variables that have a logical or linguistic character. The case is considered when the rule system is used as a method of knowledge representation.

This paper mainly studies the planning of intelligent transportation system in the era of artificial intelligence. The research content mainly includes: intelligent transportation system implementation mechanism, system application and empirical research. The research conclusion is as follows: First of all, the implementation mechanism and system application of China's intelligent transportation system are constantly improving in both technology and process. Second, Yunnan is the province with the highest public satisfaction of intelligent transportation system. Finally, relevant departments should strengthen the main role of construction, improve the traffic information platform, strengthen data management and enhance technological innovation, and strive to make China's intelligent transportation system development in the forefront of the world.

With the popularization of computers and various mobile intelligent terminals, intelligent teaching systems based on learners are becoming more and more popular among learners. The above phenomenon has greatly affected and changed the current teaching quality diagnosis methods and models. However, the author found through investigation that the current intelligent teaching quality diagnosis still has different degrees of deficiencies in the design and implementation. In response to the above problems, this paper proposes a teaching quality intelligent diagnosis model based on the combination of wireless sensor networks and fuzzy comprehensive evaluation algorithms. First of all, this article is based on the wireless sensor network to link various levels of intelligent teaching systems, and constructs the information transmission structure of the teaching intelligent diagnosis system. Secondly, this article uses fuzzy comprehensive evaluation and convolutional neural network algorithms to evaluate and excavate intelligent teaching information. Finally, the model successfully passed the simulation test and simulation application, which can provide intelligent diagnosis of teaching quality for modern intelligent teaching system.

In instructional design, decisions about which instructional methods to use are made well in advance of their implementation. While expert teachers select methods while instruction is underway and are well placed to adapt instruction to changing conditions, designers must try to predict ahead of time how instructional conditions will change. This is difficult to do because design procedures are non-deterministic and instructional theory is incomplete. In intelligent instructional systems, it is possible to shift certain aspects of decision-making back to the implementation phase of instruction. This enables systems to adapt to changing conditions and to present information in a variety of formats. The designer becomes less concerned with selecting methods and puts more effort into writing rules that allow systems to select methods once instruction has begun.

In this paper, we consider the results of research on some approaches to decision-making in irrigated agriculture - Intelligent Systems. The traditional resources for increasing the efficiency of agriculture have practically dried up. Achieving a qualitatively new level of agricultural development is possible only with the use of modern digital and intelligent technologies, because of traditional resources for increasing efficiency do not lead to a result. Global climate change and the need for agriculture in risky farming areas necessitate decision support systems for effective governance and sustainable agricultural development. The rapid development of data science and artificial intelligence has led to a new stage in the development of decision support systems - intellectualization. We have proposed to put the Kohonen neural network (Self-organizing map) as the basis for an intelligent decision support system for agro-climatic resources. At the same time, the use of modern software makes it possible to implement complex mathematical models, while new tools for creating intelligent systems such as remote sensing, cloud technologies, big data processing systems and machine learning allow more accurate management decisions at all levels of agricultural production.KeywordsIntellectualizationDecision supportIrrigated agriculture