Formal Model Of Interaction Research Articles

INNOVATIVE DEVELOPMENT OF EDUCATIONAL SYSTEMS IN THE BANI ENVIRONMENT

The rapid advancement of technology and the ever-changing global landscape have presented unique challenges and opportunities for educational systems worldwide. The introduction of the BANI (Brittle, Anxious, Nonlinear, Incomprehensible) framework as a response to the volatile and unpredictable nature of contemporary environments has further emphasized the need for innovative approaches to education. This paper explores the innovative development of educational systems within the BANI environment, focusing on the integration of emerging technologies, pedagogical strategies, and learner-centred approaches. The paper begins by providing a comprehensive overview of the BANI framework and its implications for educational systems. It highlights the key characteristics of the BANI environment, including its inherent brittleness, anxiety-inducing nature, nonlinearity, and incomprehensibility. The formal model of interaction between projects and the BANI environment can assess innovation project value for future optimisation. Furthermore, it elucidates the potential consequences of neglecting to adapt educational systems to these volatile conditions, emphasizing the importance of innovation in education. Drawing upon recent research and theoretical frameworks, the paper explores various innovative approaches to educational development in the BANI environment. It discusses the integration of emerging technologies, such as artificial intelligence, virtual reality, and augmented reality, into teaching and learning processes. Moreover, it investigates the implementation of learner-centred development strategies that foster critical thinking, problem-solving skills, creativity, and adaptability. The paper addresses the role of educators and institutions in supporting innovative development within the BANI environment. It emphasizes the need for professional development programs that empower educators to leverage emerging technologies and implement learner-centred approaches effectively. Key management of innovative project principles in the BANI environment is defined in the paper. Additionally, it highlights the significance of collaboration among educational stakeholders, including policymakers, administrators, teachers, students, and parents, to foster an ecosystem that nurtures innovation in education. The paper discusses potential challenges and ethical considerations associated with the innovative development of educational systems in the BANI environment. It explores issues related to equity, privacy, data security, and the digital divide, emphasizing the importance of responsible and inclusive approaches to educational innovation. Contributes to the existing literature by providing insights and recommendations for the innovative development of educational systems within the BANI environment. By embracing emerging technologies, learner-centred pedagogies, and collaborative efforts, educational systems can better prepare learners to thrive in uncertain and rapidly changing contexts.

Алгоритм валидации ограниченно-детерминированного поведения передатчика в канале частичного стирания

In the previous papers, the notions of a partial erasure structure and a partial erasure channel have been introduced. A partial erasure structure is a triplet consisting of an alphabet A, a family of partitions of the alphabet and a set of probabilities assigned to the partitions. A partial erasure channel functions as follows. Alice sends Bob a symbol a ∈ A, but Bob receives only partial information about the symbol. Bob only knows which partition has been choosen and which class of the partition the symbol belongs to. The set of pairs consisting of a partition and a class of the partition, i.e., the signals Bob can receive, is denoted as Bob’s alphabet B. There is a natural correspondence between the characters sent by Alice and received by Bob. For symbols a ∈ A and b ∈ B, a 7→ b is assumed to hold if there exists a partition T in the partial erasure structure such that b = (πT (a), T). The correspondence is also generalized to the words. We assume that a1 . . . an 7→ b1 . . . bn holds, if ai 7→ bi holds for all i = 1 . . . n. Suppose that Alice has an input tape from which she reads symbols of some finite alphabet S. Bob has an output tape, on which he can print symbols of alphabet S and also specially reserved (not in S) erasure symbol “*”. Thus, a problem of transmitting information via the descibed channel (i.e., a problem of construction of Alice’s and Bob’s behaviour) arises. In the previous papers of the author, a formal model of interaction between a transmitter (Alice) and a receiver (Bob), called a protocol, is presented. A protocol is a pair of functions (F, G), where F : S ∗ → A∗ is the Alice behaviour function and G : B∗ → S ∪ {∗,Λ} is the Bob behaviour function. Suppose that Alice has just read another symbol s ∈ S from the input tape, having previously read the word ˆs = s1, . . . , sm. Then we assume that during the following |F(ˆss)| steps Alice sends the word F(ˆss) symbol by symbol via the channel of partial erasure. Bob receives the symbol b ∈ B on each step. After receiving the symbol, he has to decide what to print on the output tape. He can print a symbol of the alphabet S, or the erasure symbol “*”, or nothing. Bob’s decision is determined by the function G. The protocol (F, G) is said to be valid, if Bob prints on the output tape the same content what originally is on the input tape, while, perhaps, with the replacement of the significant symbols (i.e., the symbols of the alphabet S) to the erasure symbol “*”. Among all Alice’s behavior functions, a class of correct functions is defined. A function F is said to be correct, if F(Λ)F(s 1 1 )F(s 1 1 s 1 2 ). . . F(s 1 1 . . . s1 n ) 7→ β1, F(Λ)F(s 2 1 )F(s 2 1 s 2 2 ). . . F(s 2 1 . . . s2 m) 7→ β2 and β1 is a prefix to β2, where β1, β2 ∈ B∗ . The question is investigated, for which functions of Alice’s behavior F there exists a function of Bob’s behavior G such that the pair (F, G) is a correct protocol. The theorem is proved that for this it is necessary and sufficient that F belongs to the class of correct functions. This paper presents an algorithm for verifying that Alice’s behavior function F belongs to the class of correct functions. The complexity of the algorithm is O(L|Q| 3 |S| 4 ), where |Q| is the number of states of the automaton representing the function F, L is the maximum length of the word Alice throws out, |S| is the number of symbols of the alphabet S.

SOFTWARE AGENTS IN E-LEARNING SYSTEMS

The usage of the multi-agent approach for solving resource management problems in e-learning systems is discussed in this paper. The advantages of multi-agent system comparing with traditional systems are emphasized. Such systems provide simplicity of implementation, transferability, and scalability, allow parallel computing, are guided by systems based on knowledge, have the ability to self-organize and evolve. The formalization of dynamic distribution of resources based on the network of the needs and opportunities is proposed. Two types of autonomous interacting agents are identified. They need agents and opportunity agents. All agents act in accordance with their own goals and according to certain rules, allowing them to act independently and interact with each other. As a relation between two types of agents was used conformity. The basic principles of network construction are defined. The formal model of interaction between agents is described. It was defined that one of the most often problems that can arise in the e-learning system is the problem of coordination. Partly this problem can be solved by the detailed elaboration of a set of decision-making rules. The article proposes an algorithm that allows flexible and prompt solving of information resources distribution tasks in accordance with the needs of rapidly changing environments. The specialized components for the agents in the system are defined: the agent communication component, the decision making component, and the component of the flow calculations. The main obstacles for multi-agent approach implementation are defined. They are: difficulties to evaluate the optimality of the decision; solutions are sensitive to the history of events; small changes in the system input can lead to significant changes in the output; there are some difficulties in adjusting the solution in the "manual" mode; misunderstandings in explaining the results as a consequence of complex causal relationships are possible; delays of solutions are liable to occur because of long chains of changes; it is possible to obtain non-identical solutions under the same conditions of input when re-launching the model.

Evolving reaction systems

Reaction systems were introduced as a formal model of interactions between biochemical reactions. These interactions, which are based on two mechanisms: facilitation and inhibition, determine the functioning of the living cell. Processes taking place in a reaction system A are driven by the fixed set A of available reactions provided by A. In this paper we generalize this setup: as a process progresses from a state W to its successor W′, the set of available reactions may change from A in W to A′ in W′. This new framework of evolving reaction systems is introduced and studied in this paper. Also, the notion of enabling equivalence between sets of reactions and the notion of a transformation of a set of reactions are introduced and thoroughly studied.

FUNCTIONS DEFINED BY REACTION SYSTEMS

Reaction systems are a formal model of interactions between biochemical reactions. They consist of sets of reactions, where each reaction is classified by its set of reactants (needed for the reaction to take place), its set of inhibitors (each of which prevents the reaction from taking place), and its set of products (produced when the reaction takes place) – the set of reactants and inhibitors form the resources of the reaction. Each reaction system defines a (transition) function on its set of states. (States here are subsets of an a priori given set of biochemical entities.) In this paper we investigate properties of functions defined by reaction systems. In particular, we investigate how the power of defining functions depends on available resources, and we demonstrate that with small resources one can define functions exhibiting complex behavior.

Introducing time in reaction systems

Reaction systems are a formal model of interactions between biochemical reactions. The main observation underlying the formulation of this model is that such interactions are based on two basic mechanisms: facilitation and inhibition. This paper continues the investigation of reaction systems, and in particular, it proposes a formal framework for introducing time into reaction systems. Within this framework one can formally define and investigate notions such as reaction times, creation times of compounds, their life spans, etc.

Events and modules in reaction systems

Reaction systems are a formal model of interactions between biochemical reactions. They are based on the observation that two basic mechanisms behind the functioning of biochemical reactions are facilitation and inhibition. In this paper we continue the investigation of reaction systems, and in particular we introduce the notion of a module, and then we investigate the formation and evolution of modules. Among others we prove that reaction systems can be viewed as self-organizing systems, where the organizing goal is to ensure a specific property of the set of all modules (of a state of a process).

Event model of human-system interaction

Interaction objects (interactors) are abstract structures that provide a link between rigorous approaches to software development and user-oriented aspects of interaction. The focus of the paper is on the second role of interactors; in particular, how a formal model of interaction can be used to classify and clarify various properties of interactive behaviour that are important from a user-oriented viewpoint. The formal model used in this discussion abstracts away from distinctions between states, events, and renderings; when needed, these concepts are introduced as various projections on the basic model.