Rich Interfaces for Dependability: Compositional Methods for Dynamic Fault Trees and Arcade models

This paper discusses two behavioural interfaces for reliability analysis: dynamic fault trees, which model the system reliability in terms of the reliability of its components and Arcade, which models the system reliability at an architectural level. For both formalisms, the reliability is analyzed by transforming the DFT or Arcade model to a set of input-output Markov Chains. By using compositional aggregation techniques based on weak bisimilarity, significant reductions in the state space can be obtained

An Executable System Architecture Approach to Discrete Events System Modeling Using SysML in Conjunction with Colored Petri Net

This paper proposes an executable system architecting paradigm for discrete event system modeling and analysis through integration of a set of architecting tools, executable modeling tools, analytical tools, and visualization tools. The essential step is translating SysML-based specifications into colored Petri nets (CPNs) which enables rigorous static and dynamic system analysis as well as formal verification of the behavior and functionality of the SysML-based design. A set of tools have been studied and integrated that enable a structured architecture design process. Some basic principles of executable system architecture for discrete event system modeling that guide the process of executable architecture specification and analysis are discussed. This paradigm is aimed at general system design. Its feasibility was demonstrated with a C4- type network centric system as an example. The simulation results was used to check the overall integrity and internal consistency of the architecture models, refine the architecture design, and, finally, verify the behavior and functionality of the system being modeled

Formalizing Cyber--Physical System Model Transformation via Abstract Interpretation

Model transformation tools assist system designers by reducing the labor--intensive task of creating and updating models of various aspects of systems, ensuring that modeling assumptions remain consistent across every model of a system, and identifying constraints on system design imposed by these modeling assumptions. We have proposed a model transformation approach based on abstract interpretation, a static program analysis technique. Abstract interpretation allows us to define transformations that are provably correct and specific. This work develops the foundations of this approach to model transformation. We define model transformation in terms of abstract interpretation and prove the soundness of our approach. Furthermore, we develop formalisms useful for encoding model properties. This work provides a methodology for relating models of different aspects of a system and for applying modeling techniques from one system domain, such as smart power grids, to other domains, such as water distribution networks.Comment: 8 pages, 4 figures; to appear in HASE 2019 proceeding

Temporal Data Modeling and Reasoning for Information Systems

Temporal knowledge representation and reasoning is a major research field in Artificial Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to model and process time and calendar data is essential for many applications like appointment scheduling, planning, Web services, temporal and active database systems, adaptive Web applications, and mobile computing applications. This article aims at three complementary goals. First, to provide with a general background in temporal data modeling and reasoning approaches. Second, to serve as an orientation guide for further specific reading. Third, to point to new application fields and research perspectives on temporal knowledge representation and reasoning in the Web and Semantic Web

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline

Model-Based Scenario Testing and Model Checking with Applications in the Railway Domain

This thesis introduces Timed Moore Automata, a specification formalism, which extends the classical Moore Automata by adding the concept of abstract timers without concrete delay time values, which can be started and reset, and which can change their state from running to elapsed. The formalism is used in real-world railway domain applications, and algorithms for the automated test data generation and explicit model checking of Timed Moore Automata models are presented. In addition, this thesis deals with test data generation for larger scale test models using standardized modeling formalisms. An existing framework for the automated test data generation is presented, and its underlying work-flow is extended and modified in order to allow user interaction and guidance within the generation process. As opposed to specifying generation constraints for entire test scenarios, the modified work flow then allows for an iterative approach to elaborating and formalizing test generation goals