Knowledge representation and reasoning

About: Knowledge representation and reasoning is a research topic. Over the lifetime, 20078 publications have been published within this topic receiving 446310 citations. The topic is also known as: KR & KR².

Handling uncertainty with possibility theory and fuzzy sets in a satellite fault diagnosis application

Abstract: The fault mode effects and criticality analyses (FMECA) describe the impact of identified faults. They form an important category of knowledge gathered during the design phase of a satellite and are used also for diagnosis activities. This paper proposes their extension, allowing a finer representation of the available knowledge, at approximately the same cost, through the introduction of an appropriate representation of uncertainty and incompleteness based on Zadeh's possibility theory and fuzzy sets. The main benefit of the approach is to provide a qualitative treatment of uncertainty where we can for instance distinguish manifestations which are more or less certainly present (or absent) and manifestations which are more or less possibly present (or absent) when a given fault is present. In a second step, the proposed approach is extended to handle fault impacts expressed as event chronologies. Efficient, real-time compatible discrimination techniques exploiting uncertain observations are introduced, and an example of satellite fault diagnosis illustrates the method. A brief rationale for the choice of possibility theory and fuzzy sets is provided.

ActiveMath: An Intelligent Tutoring System for Mathematics

Abstract: ActiveMath is a web-based intelligent tutoring system for mathematics. This article presents the technical and pedagogical goals of ActiveMath, its principles of design and architecture, its knowledge representation, and its adaptive behavior. In particular, we concentrate on those features that rely on AI-techniques.

Simulation-Based Design Using SysML Part 1: A Parametrics Primer

Abstract: OMG SysML™ is a modeling language for specifying, analyzing, designing, and verifying complex systems. It is a general-purpose graphical modeling language with computer-sensible semantics. This Part 1 paper and its Part 2 companion show how SysML supports simulation-based design (SBD) via tutorial-like examples. Our target audience is end users wanting to learn about SysML parametrics in general and its applications to engineering design and analysis in particular. We include background on the development of SysML parametrics that may also be useful for other stakeholders (e.g, vendors and researchers). In Part 1 we walk through models of simple objects that progressively introduce SysML parametrics concepts. To enhance understanding by comparison and contrast, we present corresponding models based on composable objects (COBs). The COB knowledge representation has provided a conceptual foundation for SysML parametrics, including executability and validation. We end with sample analysis building blocks (ABBs) from mechanics of materials showing how SysML captures engineering knowledge in a reusable form. Part 2 employs these ABBs in a high diversity mechanical example that integrates computer-aided design and engineering analysis (CAD/CAE). The object and constraint graph concepts embodied in SysML parametrics and COBs provide modular analysis capabilities based on multi-directional constraints. These concepts and capabilities provide a semantically rich way to organize and reuse the complex relations and properties that characterize SBD models. Representing relations as non-causal constraints, which generally accept any valid combination of inputs and outputs, enhances modeling flexibility and expressiveness. We envision SysML becoming a unifying representation of domain-specific engineering analysis models that include fine-grain associativity with other domain- and system-level models, ultimately providing fundamental capabilities for next-generation systems lifecycle management.

Hybrid approach using case-based reasoning and rule-based reasoning for domain independent clinical decision support in ICU

Abstract: This paper presents a hybrid approach of case-based reasoning and rule-based reasoning, as an alternative to the purely rule-based method, to build a clinical decision support system for ICU. This enables the system to tackle problems like high complexity, low experienced new staff and changing medical conditions. The purely rule-based method has its limitations since it requires explicit knowledge of the details of each domain of ICU, such as cardiac domain hence takes years to build knowledge base. Case-based reasoning uses knowledge in the form of specific cases to solve a new problem, and the solution is based on the similarities between the new problem and the available cases. This paper presents a case-based reasoning and rule-based reasoning based model which can provide clinical decision support for all domains of ICU unlike rule-based inference models which are highly domain knowledge specific. Experiments with real ICU data as well as simulated data clearly demonstrate the efficacy of the proposed method.