An Incompleteness Handling Methodology for Validation of Bayesian Knowledge Bases

The PESKI (Probabilities, Expert Systems, Knowledge, and Inference) system attempts to address some of the problems in expert system design through the use of the Bayesian Knowledge Base (BKB) representation. Knowledge gathered from a domain expert is placed into this framework and inferencing is performed over it. However, by the nature of BKBs, not all knowledge is incorporated, i.e. the representation need not be a complete representation of all combinations and possibilities of the knowledge, as this would be impractical in many real-world systems. Therefore, inherent in such a system is the problem of incomplete knowledge, or spaces within the knowledge base where areas of lacking knowledge hinder arrival at a solution. Some of this knowledge is intentionally omitted but necessary for valid results. Intentional omission, a strength of the BKB representation, allows for capturing only the relevant portions of knowledge critical to modeling an expert\u27s knowledge within a domain. This research proposes a method for handling the latter form of incompleteness administered through a graphical interface. The incompleteness is then able to be detected and corrected by the knowledge engineer in an intuitive fashion

Development and characterisation of error functions in design

As simulation is increasingly used in product development, there is a need to better characterise the errors inherent in simulation techniques by comparing such techniques with evidence from experiment, test and inservice. This is necessary to allow judgement of the adequacy of simulations in place of physical tests and to identify situations where further data collection and experimentation need to be expended. This paper discusses a framework for uncertainty characterisation based on the management of design knowledge leading to the development and characterisation of error functions. A classification is devised in the framework to identify the most appropriate method for the representation of error, including probability theory, interval analysis and Fuzzy set theory. The development is demonstrated with two case studies to justify rationale of the framework. Such formal knowledge management of design simulation processes can facilitate utilisation of cumulated design knowledge as companies migrate from testing to simulation-based design

Modelling fixed plant and algal dynamics in rivers: an application to the River Frome

The development of eutrophication in river systems is poorly understood given the complex relationship between fixed plants, algae, hydrodynamics, water chemistry and solar radiation. However there is a pressing need to understand the relationship between the ecological status of rivers and the controlling environmental factors to help the reasoned implementation of the Water Framework Directive and Catchment Sensitive Farming in the UK. This research aims to create a dynamic, process-based, mathematical in-stream model to simulate the growth and competition of different vegetation types (macrophytes, phytoplankton and benthic algae) in rivers. The model, applied to the River Frome (Dorset, UK), captured well the seasonality of simulated vegetation types (suspended algae, macrophytes, epiphytes, sediment biofilm). Macrophyte results showed that local knowledge is important for explaining unusual changes in biomass. Fixed algae simulations indicated the need for the more detailed representation of various herbivorous grazer groups, however this would increase the model complexity, the number of model parameters and the required observation data to better define the model. The model results also highlighted that simulating only phytoplankton is insufficient in river systems, because the majority of the suspended algae have benthic origin in short retention time rivers. Therefore, there is a need for modelling tools that link the benthic and free-floating habitats