For many years, at least since McCarthy and Hayes (1969), writers have lamented, and attempted to compensate for, the alleged fact that we often do not have adequate statistical knowledge for governing the uncertainty of belief, for making uncertain inferences, and the like. It is hardly ever spelled out what"adequate statistical knowledge"would be, if we had it, and how adequate statistical kn...

Risk analysis of production system, while the actual and appropriate data is not available, will cause wrong system parameters prediction and wrong decision making. In uncertainty condition, there are no appropriate measures for decision making. In epistemic uncertainty, we are confronted by the lack of data. Therefore, in calculating the system risk, we encounter vagueness that we have to use ...

The concepts of system load and capacity are pivotal in risk analysis. The complexity in risk analysis increases when the input parameters are either stochastic (aleatory uncertainty) and/or missing (epistemic uncertainty). The aleatory and epistemic uncertainties related to input parameters are handled through simulation-based parametric and non-parametric probabilistic techniques. The complex...

The BDI architecture, where agents are modelled based on their beliefs, desires and intentions, provides a practical approach to develop large scale systems. However, it is not well suited to model complex Supervisory Control And Data Acquisition (SCADA) systems pervaded by uncertainty. In this paper we address this issue by extending the operational semantics of CAN(PLAN) into CAN(PLAN)+. We s...

This paper proposes a method to generate trajectories that are optimal with respect multiple objectives and robust against epistemic uncertainty. Epistemic uncertainty is modelled probability boxes optimised the lower expectations on cost functions constraint satisfaction. The an approach calculation of expectation using Bernstein polynomials, efficient many-objective optimisation trajectories....

[1] With growing interest in understanding the magnitudes and sources of uncertainty in hydrological modeling, the difficult problem of characterizing model structure adequacy is now attracting considerable attention. Here, we examine this problem via a model-structureindependent approach based in information theory. In particular, we (a) discuss how to assess and compute the information conten...

New product development (NPD) processes are characterized by uncertainty and iteration making them difficult to plan and manage. A novel dynamic model of NPD that explicitly models communication techniques is used to examine ways to improve span time and effort performance through improved coordination. Results of simulations under various scenarios of epistemic and aleatory uncertainty illustr...

In this paper a multi-agent based simulation and optimization framework was put forward to solve the aleatory and epistemic uncertainty in engineering design. Agent structures were designed to extract engineering knowledge from experiments and simulation and to communicate an internal message and domain engineering knowledge base. The framework can deal with complicated time-varying structure a...

Recently we proposed a new form of imprecise probability based on the generalized interval, where the probabilistic calculus structure resembles the traditional one in the precise probability because of the Kaucher arithmetic. In this paper, we study the independence properties of the generalized interval probability. It resembles the stochastic independence with proper and improper intervals a...