Reasoning consistently about inconsistency

Patching et al. and Hinde et al. in their work on truth-space mass assignments, presented a semantic unification function and a semantic separation function for mass assignment logic that dealt with inconsistency. This paper takes these two functions and while preserving the outside inconsistencies shows how inconsistency can be reasoned about in a consistent manner. This means that inconsistency that arises outside the system need not enter the system, but needs to be represented within the system, and can therefore be extracted appropriately as output from the system to emerge as inconsistency on the outside. The internal reasoning system need therefore only concern itself with belief in truth, falsity and uncertainty

Fuzzy expert systems in civil engineering

Imperial Users onl

Reasoning Consistently about Inconsistency

Patching et al. and Hinde et al. in their work on truth-space mass assignments, presented a semantic unification function and a semantic separation function for mass assignment logic that dealt with inconsistency. This paper takes these two functions and while preserving the outside inconsistencies shows how inconsistency can be reasoned about in a consistent manner. This means that inconsistency that arises outside the system need not enter the system, but needs to be represented within the system, and can therefore be extracted appropriately as output from the system to emerge as inconsistency on the outside. The internal reasoning system need therefore only concern itself with belief in truth, falsity and uncertainty

From fuzzy to annotated semantic web languages

The aim of this chapter is to present a detailed, selfcontained and comprehensive account of the state of the art in representing and reasoning with fuzzy knowledge in Semantic Web Languages such as triple languages RDF/RDFS, conceptual languages of the OWL 2 family and rule languages. We further show how one may generalise them to so-called annotation domains, that cover also e.g. temporal and provenance extensions

Analyzing Fuzzy Logic Computations with Fuzzy XPath

Implemented with a fuzzy logic language by using the FLOPER tool developed in our research group, we have recently designed a fuzzy dialect of the popular XPath language for the flexible manipulation of XML documents. In this paper we focus on the ability of Fuzzy XPath for exploring derivation trees generated by FLOPER once they are exported in XML format, which somehow serves as a debugging/analizing tool for discovering the set of fuzzy computed answers for a given goal, performing depth/breadth-first traversals of its associated derivation tree, finding non fully evaluated branches, etc., thus reinforcing the bi-lateral synergies between Fuzzy XPath and FLOPER

A blackboard-based system for learning to identify images from feature data

A blackboard-based system which learns recognition rules for objects from a set of training examples, and then identifies and locates these objects in test images, is presented. The system is designed to use data from a feature matcher developed at R.S.R.E. Malvern which finds the best matches for a set of feature patterns in an image. The feature patterns are selected to correspond to typical object parts which occur with relatively consistent spatial relationships and are sufficient to distinguish the objects to be identified from one another. The learning element of the system develops two separate sets of rules, one to identify possible object instances and the other to attach probabilities to them. The search for possible object instances is exhaustive; its scale is not great enough for pruning to be necessary. Separate probabilities are established empirically for all combinations of features which could represent object instances. As accurate probabilities cannot be obtained from a set of preselected training examples, they are updated by feedback from the recognition process. The incorporation of rule induction and feedback into the blackboard system is achieved by treating the induced rules as data to be held on a secondary blackboard. The single recognition knowledge source effectively contains empty rules which this data can be slotted into, allowing it to be used to recognise any number of objects - there is no need to develop a separate knowledge source for each object. Additional object-specific background information to aid identification can be added by the user in the form of background checks to be carried out on candidate objects. The system has been tested using synthetic data, and successfully identified combinations of geometric shapes (squares, triangles etc.). Limited tests on photographs of vehicles travelling along a main road were also performed successfully