Many health problems are generally caused by unhealthy behaviours that occur whilst conducting everyday Activities of Daily Living (ADL), such as poor use of sanitation and hygiene. This paper describes the development of an ADL inference engine, which is able to recognise natural hygiene behaviour patterns. As opposed to traditional ADL classification approaches, the developed inference engine...

HAZOP (Hazard and Operability) is a preliminary and systematic approach for identifying hazards and suggesting hazard mitigation measures in a process facility. A knowledge-based expert system, ExpHAZOP, has been developed to automate the manual HAZOP analysis and accelerate the process. ExpHAZOP is comprised of a graphical user interface (GUI), a knowledge-base and an inference engine. One of ...

Fuzzy inference engines based on the existing fuzzy theory are inadequate to perform reliable decision making. Besides requiring the fuzzy sets and data to be normalized, the inference engine is also sensitive to noise in observational data. Inaccurate conclusions are produced if noise is present and also when the fuzzy sets are not normalized. In this paper, a new term "similarity' (o) and the...

Configurable products are an important way to achieve mass-customisation to satisfy individual customer requirements. We describe a novel configurator prototype that supports tailoring, i.e. configuring, such a product. It contains a semi-visual modelling tool based on a high-level objectand product structure-oriented modelling language with a clear formal semantics. The main functionality is d...

The system we have developed, SYCOJET, automatically builds test cases making use of specific expert knowledge. In our first version, it uses the internal structure of the knowledge base to be tested, and implicitly assumes that the inference engine derives all that can be derived from the knowledge base and the problem data. Here we are concerned with how to take into account, in a second vers...

Programmers employing inference in Bayesian networks typically rely on the inclusion of the model as well as an inference engine into their application. Sophisticated inference engines require non-trivial amounts of space and are also difficult to implement. This limits their use in some applications that would otherwise benefit from probabilistic inference. This paper presents a system that mi...

We revisit the classical problem of exact inference on probabilistic graphical models (PGMs). Our algorithm is based on recent worst-case optimal database join algorithms, which can be asymptotically faster than traditional data processing methods. We present the first empirical evaluation of these new algorithms via JoinInfer, a new exact inference engine. We empirically explore the properties...

One long-term goal for research on probabilistic programming languages (PPLs) is efficient inference using a single, generic inference engine. Many current inference engines are, however, interpreters for the given PP, leading to substantial overhead and poor performance. This paper describes a PPL compiler, Swift, that generates model-specific and inference-algorithm-specific target code from ...