Extended incidence calculus and its comparison with related theories

This thesis presents a comprehensive study o f incidence calculus, a probabilistic logic for reasoning under uncertainty which extends two-value propositional logic to a multiple-value logic. There are three main contributions in this thesis.First of all, the original incidence calculus is extended considerably in three aspects: (a) the original incidence calculus is generalized; (b) an efficient algorithm for incidence assignment based on generalized incidence calculus is developed; (c) a combination rule is proposed for the combination of both independent and some dependent pieces of evidence. Extended incidence calculus has the advantages of representing information flexibly and combining multiple sources o f evidence.Secondly, a comprehensive comparison between extended incidence calculus and the Dempster-Shafer (DS) theory of evidence is provided. It is proved that extended incidence calculus is equivalent to DS theory in representing evidence and combining independent evidence but superior to DS theory in combining de­pendent evidence.Thirdly, the relations between extended incidence calculus and the assumption- based truth maintenance systems are discussed. It is proved that extended inci­dence calculus is equivalent to the ATM S in calculating labels for nodes. Extended incidence calculus can also be used as a basis for constructing probabilistic ATMSs.The study in this thesis reveals that extended incidence calculus can be re­garded as a bridge between numerical and symbolic reasoning mechanisms

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

Artificial intelligence methods in process plant layout

The thesis describes "Plant Layout System" or PLS, an Expert System which automates all aspects of conceptual layout of chemical process plant, from sizing equipment using process data to deriving the equipment items' elevation and plan positions. PLS has been applied to a test process of typical size and complexity and which encompasses a wide range of layout issues and problems. The thesis presents the results of the tests to show that PLS generates layouts that are entirely satisfactory and conventional from an engineering viewpoint. The major advance made during this work is the approach to layout by Expert System of any kind of process plant. The thesis describes the approach in full, together with the engineering principles which it acknowledges. Plant layout problems are computationally complex. PLS decomposes layout into a sequence of formalised steps and uses a powerful and sophisticated technique to reduce plant complexity. PLS uses constraint propagation for spatial synthesis and includes propagation algorithms developed specifically for this domain. PLS includes a novel qualitative technique to select constraints to be relaxed. A conventional frame based representation was found to be appropriate, but with procedural knowledge recorded in complex forward chaining rules with novel features. Numerous examples of the layout engineer's knowledge are included to elucidate the epistemology of the domain

Artificial intelligence methods in process plant layout

The thesis describes "Plant Layout System" or PLS, an Expert System which automates all aspects of conceptual layout of chemical process plant, from sizing equipment using process data to deriving the equipment items' elevation and plan positions. PLS has been applied to a test process of typical size and complexity and which encompasses a wide range of layout issues and problems. The thesis presents the results of the tests to show that PLS generates layouts that are entirely satisfactory and conventional from an engineering viewpoint. The major advance made during this work is the approach to layout by Expert System of any kind of process plant. The thesis describes the approach in full, together with the engineering principles which it acknowledges. Plant layout problems are computationally complex. PLS decomposes layout into a sequence of formalised steps and uses a powerful and sophisticated technique to reduce plant complexity. PLS uses constraint propagation for spatial synthesis and includes propagation algorithms developed specifically for this domain. PLS includes a novel qualitative technique to select constraints to be relaxed. A conventional frame based representation was found to be appropriate, but with procedural knowledge recorded in complex forward chaining rules with novel features. Numerous examples of the layout engineer's knowledge are included to elucidate the epistemology of the domain