System simulation by SEMoLa

SEMoLa is a platform, developed at DISA since 1992, for system knowledge integration and modelling. It allows to create computer models for dynamic systems and to manage different types of information. It is formed by several parts, each dealing with different forms of knowledge, in an integrated way: a graphical user interface (GUI), a declarative language for modelling, a set of commands with a procedural scripting language, a specific editor with code highlighting (SemEdit), a visual modelling application (SemDraw), a data base management system (SemData), plotting data capabilities (SemPlot), a raster maps management system (SemGrid), a large library of random number generators for uncertainty analysis, support for fuzzy logic expert systems, a neural networks builder and various statistical tools (basic statistics, multiple and non-linear regression, moving statistics, etc.). The core part of the platform is the declarative modelling language (SEMoLa; simple, easy to use, modelling language). It relies on System Dynamics principles and uses an integrated view to represent dynamic systems through different modelling approaches (state/individual-based, continuous/discrete, deterministic/stochastic) without requiring specific programming skills. SEMoLa language is based on a ontology closer to human reasoning rather than computer logic and constitutes also a paradigm for knowledge management. SEMoLa platform permits to simplify the routinely tasks of creating, debugging, evaluating and deploying computer simulation models but also to create user libraries of script commands. It is able to communicate with other frameworks exchanging - with standard formats - data, modules and model components

Dynamic epistemic logics for abstract argumentation

AbstractThis paper introduces a multi-agent dynamic epistemic logic for abstract argumentation. Its main motivation is to build a general framework for modelling the dynamics of a debate, which entails reasoning about goals, beliefs, as well as policies of communication and information update by the participants. After locating our proposal and introducing the relevant tools from abstract argumentation, we proceed to build a three-tiered logical approach. At the first level, we use the language of propositional logic to encode states of a multi-agent debate. This language allows to specify which arguments any agent is aware of, as well as their subjective justification status. We then extend our language and semantics to that of epistemic logic, in order to model individuals' beliefs about the state of the debate, which includes uncertainty about the information available to others. As a third step, we introduce a framework of dynamic epistemic logic and its semantics, which is essentially based on so-called event models with factual change. We provide completeness results for a number of systems and show how existing formalisms for argumentation dynamics and unquantified uncertainty can be reduced to their semantics. The resulting framework allows reasoning about subtle epistemic and argumentative updates—such as the effects of different levels of trust in a source—and more in general about the epistemic dimensions of strategic communication

A Dynamic Epistemic Logic for Abstract Argumentation

This paper introduces a multi-agent dynamic epistemic logic for abstract argumenta- tion. Its main motivation is to build a general framework for modelling the dynamics of a debate, which entails reasoning about goals, beliefs, as well as policies of com- munication and information update by the participants. After locating our proposal and introducing the relevant tools from abstract argumentation, we proceed to build a three-tiered logical approach. At the first level, we use the language of propositional logic to encode states of a multi-agent debate. This language allows to specify which arguments any agent is aware of, as well as their subjective justification status. We then extend our language and semantics to that of epistemic logic, in order to model individuals’ beliefs about the state of the debate, which includes uncertainty about the information available to others. As a third step, we introduce a framework of dynamic epistemic logic and its semantics, which is essentially based on so-called event models with factual change. We provide completeness results for a number of systems and show how existing formalisms for argumentation dynamics and unquantified uncerSynthese tainty can be reduced to their semantics. The resulting framework allows reasoning about subtle epistemic and argumentative updates—such as the effects of different levels of trust in a source—and more in general about the epistemic dimensions of strategic communication

A logical analysis of soft systems modelling: implications for information system design and knowledge based system design

The thesis undertakes an analysis of the modelling methods used in the Soft Systems Methodology (SSM) developed by Peter Checkland and Brian Wilson. The analysis is undertaken using formal logic and work drawn from modern Anglo-American analytical philosophy especially work in the area of philosophical logic, the theory of meaning, epistemology and the philosophy of science. The ability of SSM models to represent causation is found to be deficient and improved modelling techniques suitable for cause and effect analysis are developed. The notional status of SSM models is explained in terms of Wittgenstein's language game theory. Modal predicate logic is used to solve the problem of mapping notional models on to the real world. The thesis presents a method for extending SSM modelling in to a system for the design of a knowledge based system. This six stage method comprises: systems analysis, using SSM models; language creation, using logico-linguistic models; knowledge elicitation, using empirical models; knowledge representation, using modal predicate logic; codification, using Prolog; and verification using a type of non-monotonic logic. The resulting system is constructed in such a way that built in inductive hypotheses can be falsified, as in Karl Popper's philosophy of science, by particular facts. As the system can learn what is false it has some artificial intelligence capability. A variant of the method can be used for the design of other types of information system such as a relational database

Modelling of Fuzzy Expert System for an Assessment of Security Information Management System UIS (University Information System)

Several methodologies based on the international standard ISO/IEC 27001 have been developed for modelling information security management systems within higher education. This paper transformed the ISO/IEC 27001 standard into a questionnaire, which was sent digitally to about 100 universities in Bosnia and Herzegovina, and to the EU, Norway and the USA. The questions are arranged by levels, and the levels have their numerical weights, derived from individual questions in the levels themselves. Otherwise, the questions are asked with Yes or No and thus are reduced to binary variables. The rules necessary for the functioning of the system have been calculated. The fuzzy logic method represents a new approach to the problems of managing complex systems, which is very difficult to describe with a certain mathematical model, as well as in systems with a large number of inputs and outputs where there are unclear interactions. Risk assessment is a major part of the ISMS process. Traditional risk calculation models are based on the application of probability and classical set theory. Here, we have converted the risk assessment into a system rating of 5 to 10 numerically or from five to ten descriptively. We perform fuzzy optimization by finding the values of the input parameters of a complex simulated system, which results in the desired output. We use the fuzzy logic controller to execute fuzzy inference rules from the fuzzy rule database in determining congestion parameters, obtaining warning information and appropriate action. Simulating the situation of an advanced system that evaluates the protection quality of such a system with fuzzy logic, we use MATLAB. The paper combines the original Visual Basic programming language and MATLAB\u27s Fuzzy Toolbox, to solve the complex problem of assessing compliance with the ISO/IEC 27001 standard, as one of the main standards for information systems security modelling. University information systems were used, but it is also applicable to all other information systems. The evaluation has been done for several universities and it has been proven that the system evaluates correctly, but these universities must not be publicly named. There was no such approach in the use of fuzzy logic and on such systems, and that is the originality of this work

Learning from AI : new trends in database technology

Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database