Reason Maintenance - State of the Art

This paper describes state of the art in reason maintenance with a focus on its future usage in the KiWi project. To give a bigger picture of the field, it also mentions closely related issues such as non-monotonic logic and paraconsistency. The paper is organized as follows: first, two motivating scenarios referring to semantic wikis are presented which are then used to introduce the different reason maintenance techniques

Enriched property ontology for knowledge systems : a thesis presented in partial fulfilment of the requirements for the degree of Master of Information Systems in Information Systems, Massey University, Palmerston North, New Zealand

"It is obvious that every individual thing or event has an indefinite number of properties or attributes observable in it and might therefore be considered as belonging to an indefinite number of different classes of things" [Venn 1876]. The world in which we try to mimic in Knowledge Based (KB) Systems is essentially extremely complex especially when we attempt to develop systems that cover a domain of discourse with an almost infinite number of possible properties. Thus if we are to develop such systems how do we know what properties we wish to extract to make a decision and how do we ensure the value of our findings are the most relevant in our decision making. Equally how do we have tractable computations, considering the potential computation complexity of systems required for decision making within a very large domain. In this thesis we consider this problem in terms of medical decision making. Medical KB systems have the potential to be very useful aids for diagnosis, medical guidance and patient data monitoring. For example in a diagnostic process in certain scenarios patients may provide various potential symptoms of a disease and have defining characteristics. Although considerable information could be obtained, there may be difficulty in correlating a patient's data to known diseases in an economic and efficient manner. This would occur where a practitioner lacks a specific specialised knowledge. Considering the vastness of knowledge in the domain of medicine this could occur frequently. For example a Physician with considerable experience in a specialised domain such as breast cancer may easily be able to diagnose patients and decide on the value of appropriate symptoms given an abstraction process however an inexperienced Physician or Generalist may not have this facility.[FROM INTRODUCTION

Consistency reasoning in knowledge systems.

by Ying Kit Wong.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 144-146).AcknowledgmentsAbstractChapter 1 --- Introduction --- p.1Chapter 1.1 --- Characteristics of Human Commonsense Reasoning --- p.4Chapter 1.2 --- Neural-Logic Belief Network as the Basic Inconsistency Rea- soning System --- p.7Chapter 1.3 --- Consistency of Knowledge --- p.8Chapter 1.4 --- Update Sequence Independence in Belief States --- p.10Chapter 1.5 --- Lazy Consistency Reasoning --- p.12Chapter 1.6 --- Comparison of W-Consistency with Other Systems --- p.14Chapter 1.7 --- Integration of Different Methods in One Formalization --- p.16Chapter 2 --- Neural-Logic Belief Network (NLBN) --- p.17Chapter 2.1 --- Definitions --- p.17Chapter 2.2 --- Computation Functions --- p.20Chapter 3 --- W-Consistency Reasoning --- p.29Chapter 3.1 --- W-Consistency --- p.30Chapter 3.2 --- Logical Suppression --- p.33Chapter 3.3 --- Consistency Check --- p.35Chapter 3.4 --- Consistency Maintenance --- p.35Chapter 3.5 --- The W-Consistency Reasoning Process --- p.41Chapter 3.6 --- Proof of Consistency Reasoning Process Terminates Finitely and Consistent --- p.42Chapter 4 --- Implementation --- p.46Chapter 4.1 --- Introduction --- p.46Chapter 4.2 --- New Features in Phase Two --- p.48Chapter 4.2.1 --- Consistency Reasoning Function --- p.48Chapter 4.2.2 --- Knowledge File --- p.49Chapter 4.3 --- Inference Engine for Consistency Reasoning --- p.54Chapter 4.4 --- Examples of using XHOPES --- p.56Chapter 5 --- Comparison between NLBN with W-Consistency and AGM Logic --- p.63Chapter 5.1 --- AGM Logic with Epistemic Entrenchment --- p.64Chapter 5.1.1 --- Three Forms of Belief Change --- p.64Chapter 5.1.2 --- Epistemic Entrenchment --- p.67Chapter 5.2 --- Network Update Operators in NLBN vs. Belief Changesin AGM --- p.68Chapter 5.3 --- Epistemic Entrenchment vs. Degree-of-Belief --- p.77Chapter 5.4 --- Consistency Preservation --- p.80Chapter 5.5 --- Classical vs. Non-classical Logical Consistency --- p.82Chapter 5.6 --- Retraction vs. Suppression --- p.83Chapter 5.7 --- Foundation vs. Coherence Theories --- p.84Chapter 6 --- Comparison of W-Consistency with other Systems --- p.86Chapter 6.1 --- G-Consistency --- p.87Chapter 6.1.1 --- Overview of G-Consistency --- p.87Chapter 6.1.2 --- Comparison of W-Consistency with G-Consistency --- p.88Chapter 6.2 --- S-Consistency --- p.94Chapter 6.2.1 --- Overview of S-Consistency --- p.94Chapter 6.2.2 --- Comparison of W-Consistency with S-Consistency --- p.95Chapter 6.3 --- Truth Maintenance Systems --- p.97Chapter 6.3.1 --- Introduction of Truth Maintenance Systems --- p.97Chapter 6.3.2 --- Comparison of TMS between W-Consistency with NLBN --- p.99Chapter 7 --- Lazy Consistency Reasoning using W-Consistency --- p.102Chapter 7.1 --- Proof of Lazy Characteristic of W-Consistency --- p.104Chapter 7.2 --- Example of Lazy Consistency Reasoning --- p.112Chapter 7.3 --- Discussion and Application --- p.117Chapter 8 --- Integration of Different Consistency Reasoning Methods --- p.120Chapter 8.1 --- Mixing W-Consistency and G-Consistency into a NLBN --- p.121Chapter 8.2 --- Using a NLBN for Truth Maintenance --- p.129Chapter 8.2.1 --- TMS's Truth Maintenance Strategy --- p.129Chapter 8.2.2 --- Consistency Reasoning style of NLBN --- p.134Chapter 8.2.3 --- Using NLBN for TMS-style Truth Maintenance --- p.136Chapter 8.2.4 --- Discussion --- p.140Chapter 9 --- Conclusion --- p.143Chapter A --- Test Case for Merging Knowledge Bases Using XHOPES --- p.15

Fictional Persuasion and the Nature of Belief

Psychological studies on fictional persuasion demonstrate that being engaged with fiction systematically affects our beliefs about the real world, in ways that seem insensitive to the truth. This threatens to undermine the widely accepted view that beliefs are essentially regulated in ways that tend to ensure their truth, and may tempt various non-doxastic interpretations of the belief-seeming attitudes we form as a result of engaging with fiction. I evaluate this threat, and argue that it is benign. Even if the relevant attitudes are best seen as genuine beliefs, as I think they often are, their lack of appropriate sensitivity to the truth does not undermine the essential tie between belief and truth. To this end, I shall consider what I take to be the three most plausible models of the cognitive mechanisms underlying fictional persuasion, and argue that on none of these models does fictional persuasion undermine the essential truth-tie