The Combination of Paradoxical, Uncertain, and Imprecise Sources of Information based on DSmT and Neutro-Fuzzy Inference

The management and combination of uncertain, imprecise, fuzzy and even paradoxical or high conflicting sources of information has always been, and still remains today, of primal importance for the development of reliable modern information systems involving artificial reasoning. In this chapter, we present a survey of our recent theory of plausible and paradoxical reasoning, known as Dezert-Smarandache Theory (DSmT) in the literature, developed for dealing with imprecise, uncertain and paradoxical sources of information. We focus our presentation here rather on the foundations of DSmT, and on the two important new rules of combination, than on browsing specific applications of DSmT available in literature. Several simple examples are given throughout the presentation to show the efficiency and the generality of this new approach. The last part of this chapter concerns the presentation of the neutrosophic logic, the neutro-fuzzy inference and its connection with DSmT. Fuzzy logic and neutrosophic logic are useful tools in decision making after fusioning the information using the DSm hybrid rule of combination of masses.Comment: 20 page

Risk management in intelligent agents

University of Technology, Sydney. Faculty of Engineering and Information Technology.This thesis presents the development of a generalised risk analysis, modelling and management framework for intelligent agents based on the state-of-art techniques from knowledge representation and uncertainty management in the field of Artificial Intelligence (AI). Assessment and management of risk are well established common practices in human society. However, formal recognition and treatment of risk are not usually considered in the design and implementation of (most existing) intelligent agents and information systems. This thesis aims to fill this gap and improve the overall performance of an intelligent agent. By providing a formal framework that can be easily implemented in practice, my work enables an agent to assess and manage relevant domain risks in a consistent, systematic and intelligent manner. In this thesis, I canvas a wide range of theories and techniques in AI research that deal with uncertainty representation and management. I formulated a generalised concept of risk for intelligent agents and developed formal qualitative and quantitative representations of risk based on the Possible Worlds paradigm. By adapting a selection of mature knowledge modelling and reasoning techniques, I develop a qualitative and a quantitative approach of modelling domains for risk assessment and management. Both approaches are developed under the same theoretical assumptions and use the same domain analysis procedure; both share a similar iterative process to maintain and improve domain knowledge base continuously over time. Most importantly, the knowledge modelling and reasoning techniques used in both approaches share the same underlying paradigm of Possible Worlds. The close connection between the two risk modelling and reasoning approaches leads us to combine them into a hybrid, multi-level, iterative risk modelling and management framework for intelligent agents, or HiRMA, that is generalised for risk modelling and management in many disparate problem domains and environments. Finally, I provide a top-level guide on how HiRMA can be implemented in a practical domain and a software architecture for such an implementation. My work lays a solid foundation for building better decision support tools (with respect to risk management) that can be integrated into existing or future intelligent agents

An Administrative View of Model Uncertainty in Public Health

Dr. Carrington reviews several ways to deal with model uncertainty, including those failing to acknowledge any use of models. He then evaluates six such methods with regard to, e.g., transparency and cost of execution

A practical risk management framework for intelligent information systems

This paper reports progress towards the development of a practical risk analysis and management framework for intelligent information systems based on the state-of-art techniques in uncertainty management. We provide an analysis of challenges raised by the need to manage risk and identify a set of key requirements for a practical framework that can support risk management in real environments that are open, complex and dynamic. We assess a number of relevant theories, approaches and techniques for their suitability in addressing the risk management challenges. Finally, we present our current multi-level risk analysis and modelling framework, and use benchmark problems in two entirely different domains to illustrate the broad range of our framework applicability