Modelling the behaviour of state-based systems can be challenging, especially when modeller is not entirely certain about its intended interactions with user or environment. Currently, it possible to associate a stated level uncertainty given event by attaching probabilities transitions (producing ‘Probabilistic State Machines’). This captures ‘First-order uncertainty’ - (un-)certainty that wil...

Combining climate change, crop growth and crop disease models to predict impacts of climate change on crop diseases can guide planning of climate change adaptation strategies to ensure future food security. This review summarises recent developments in modelling climate change impacts on crop diseases, emphasises some major challenges and highlights recent trends. The use of multi-model ensembl...

Uncertainty is an intrinsic feature of data containing the underlying information of reliability engineering realities. Randomness and fuzziness are two different type uncertainties although there is certain link between them. Cox’s PH (Proportional Hazards) models and Lawless and Thiagarajah’s CIF (Conditional Intensity Function) models addressed the random uncertainty in a very general format...

Monitoring selectivity is a key challenge faced by agents when modelling other agents(1) — agents cannot continually monitor others due to the computational burden of such monitoring and modelling, but lack of such monitoring and modelling leads to increased uncertainty about the state of other agents. Such monitoring selectivity is also crucially important when agents engage in planning in the...

Uncertainty analysis is the part of risk analysis that focuses on the uncertainties in the data characteristics. Important components of uncertainty analysis include qualitative analysis that identifies the uncertainties, quantitative analysis of the effects of the uncertainties on the decision process, and communication of the uncertainty. (Funtowwicz and Ravetz 1990, Petersen 2000, Regan et a...

Modelling of complex systems is mainly based on the decomposition of these systems in autonomous elements, and the identification and definitio9n of possible interactions between these elements. For this, the agent-based approach is a modelling solution often proposed. Complexity can also be due to external events or internal to systems, whose main characteristics are uncertainty, imprecision, ...