Igor Sheremet

By Igor Sheremet

This chapter is dedicated to a new knowledge representation model, providing convergence of classical operations research and modern knowledge engineering. Kernel of the introduced model is the recursively generated multisets, selected according to the predefined restrictions and optimization criteria. Sets of multisets are described by the so-called multiset grammars (MGs), being projection of a conceptual background of well-known string-generating grammars on the multisets universum. Syntax and semantics of MGs and their practice-oriented development—unitary multiset grammars and metagrammars—are considered.

Part of the book: Enhanced Expert Systems

By Igor Sheremet

The chapter is dedicated to the algorithmics of unitary multiset grammars and metagrammars. Their application to some actual problems from the area of large-scale sociotechnical systems (STS) assessment and optimization is also considered: estimation of capabilities of the producing STS; amounts of resources, necessary to such STS for various orders completion; assessment of STS sustainability/vulnerability to various destructive impacts (natural disasters, technogenic catastrophes, mutual sanctions, etc.); and STS profit maximization, as well as works optimal distribution among non-antagonistic competing STS, operating in the market economy.

Part of the book: Enhanced Expert Systems

By Igor Sheremet

The chapter describes multiset-based approach to the assessment of resilience/vulnerability of the distributed sociotechnological systems (DSTS) to natural hazards (NH). DSTS contain highly interconnected and intersected consuming and producing segments, and also resource base (RB), providing their existence and operation. NH impacts may destroy some local elements of these segments, as well as some parts of RB, thus initiating multiple chain effects, leading to negative consequences far away from the NH local strikes. To assess DSTS resilience to such impacts, multigrammatical representation of DSTS is used. A criterion of DSTS sustainability to NH, being generalization of similar criterion, known for industrial (producing) systems, is proposed. Application of this criterion to critical infrastructures is considered, as well as solution of the reverse problem, concerning subsystems of DSTS, which may stay functional after NH impact.

Part of the book: Natural Hazards

By Igor Sheremet

The most complicated task for big data modeling in comparison with relational approach is its variety, being a consequence of heterogeneity of sources of data, accumulated in the integrated storage space. “Set of Strings” Framework (SSF) provides unified solution of this task by representation of database as updated finite set of facts, being strings, in which structure is defined by current metadatabase, which is also an updated set of the context-free generating rules. This chapter is dedicated to SSF formal and substantial description.

Part of the book: Introduction to Data Science and Machine Learning

By Igor Sheremet

Augmented Post systems (APS) are string-operating Prolog-like knowledge representation, affiliated with the “Set of Strings” Framework (SSF). APS descriptive and logical inference capabilities provide natural integration of Big Data with online analytic processing. This chapter is dedicated to strict formal definition of APS syntax, mathematical and operational semantics, and to its most valuable implementational issues, as well as to APS application to Big Data, Internet of Things, cyberphysical industry, and cybersecurity areas.

Part of the book: Introduction to Data Science and Machine Learning

By Igor Sheremet

Chapter is dedicated to the application of multi-agent technology to generation of sets of terminal multisets (TMS) defined by filtering multiset grammars (FMG). Proposed approach is based on creation of multi-agent system (MAS), corresponding to specific FMG in such a way, that every rule of FMG is represented by independently acting agent. Such MAS provides high-parallel generation of TMS and may be effectively used in any proper hardware environment. Directions of further development of the proposed approach are discussed.

Part of the book: Computational Optimization Techniques and Applications