Defeasible inheritance systems and reactive diagrams

We give an analysis of defeasible inheritance diagrams, also from the perspective of reactive diagrams

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

Logical tools for handling change in agent-based systems

We give a unified approach to various results and problems of nonclassical logic

Conditionals and modularity in general logics

In this work in progress, we discuss independence and interpolation and related topics for classical, modal, and non-monotonic logics

Evaluating the Impact of Defeasible Argumentation as a Modelling Technique for Reasoning under Uncertainty

Limited work exists for the comparison across distinct knowledge-based approaches in Artificial Intelligence (AI) for non-monotonic reasoning, and in particular for the examination of their inferential and explanatory capacity. Non-monotonicity, or defeasibility, allows the retraction of a conclusion in the light of new information. It is a similar pattern to human reasoning, which draws conclusions in the absence of information, but allows them to be corrected once new pieces of evidence arise. Thus, this thesis focuses on a comparison of three approaches in AI for implementation of non-monotonic reasoning models of inference, namely: expert systems, fuzzy reasoning and defeasible argumentation. Three applications from the fields of decision-making in healthcare and knowledge representation and reasoning were selected from real-world contexts for evaluation: human mental workload modelling, computational trust modelling, and mortality occurrence modelling with biomarkers. The link between these applications comes from their presumptively non-monotonic nature. They present incomplete, ambiguous and retractable pieces of evidence. Hence, reasoning applied to them is likely suitable for being modelled by non-monotonic reasoning systems. An experiment was performed by exploiting six deductive knowledge bases produced with the aid of domain experts. These were coded into models built upon the selected reasoning approaches and were subsequently elicited with real-world data. The numerical inferences produced by these models were analysed according to common metrics of evaluation for each field of application. For the examination of explanatory capacity, properties such as understandability, extensibility, and post-hoc interpretability were meticulously described and qualitatively compared. Findings suggest that the variance of the inferences produced by expert systems and fuzzy reasoning models was higher, highlighting poor stability. In contrast, the variance of argument-based models was lower, showing a superior stability of its inferences across different system configurations. In addition, when compared in a context with large amounts of conflicting information, defeasible argumentation exhibited a stronger potential for conflict resolution, while presenting robust inferences. An in-depth discussion of the explanatory capacity showed how defeasible argumentation can lead to the construction of non-monotonic models with appealing properties of explainability, compared to those built with expert systems and fuzzy reasoning. The originality of this research lies in the quantification of the impact of defeasible argumentation. It illustrates the construction of an extensive number of non-monotonic reasoning models through a modular design. In addition, it exemplifies how these models can be exploited for performing non-monotonic reasoning and producing quantitative inferences in real-world applications. It contributes to the field of non-monotonic reasoning by situating defeasible argumentation among similar approaches through a novel empirical comparison

A Data-Driven Methodology for Motivating a Set of Coherence Relations

Institute for Communicating and Collaborative SystemsThe notion that a text is coherent in virtue of the `relations' that hold between its component spans currently forms the basis for an active research programme in discourse linguistics.Coherence relations feature prominently in many theories of discourse structure, and have recently been used with considerable success in text generation systems. However, while the concept of coherence relations is now common currency for discourse theorists there remains much confusion about them, and no standard set of relations has yet emerged. The aim of this thesis is to contribute towards the development of a standard set of relations. We begin from an explicitly empirical conception of relations: they are taken to model a collection of psychological mechanisms operative during the tasks of reading and writing.This conception is fleshed out with reference to psychological theories of skilled task performance, and to Rosch's notion of the basic level of categorisation. A methodology for investigating these mechanisms is then presented, which takes as its starting point a study of cue phrases- the sentence/clause connectives by which they are signaled. Although it is conventional to investigate psychological mechanisms by studying human behaviour, it is argued here that evidence for the constructs modelled by relations can be sought in ananalysis of the linguistic resources available for marking them explicitly intext. The methodology is based on two simple linguistic tests: the test for cue phrases and the test for substitutability. Both tests are functional in inspiration: the former test identifies a heterogenous class of phrases used for linking one portion of text to another; and the later test is used to discover when a writer is willing to substitute one of these phrases for another. The tests are designed to capture the judgements of ordinary readers and writers, rather than the theoretical intuitions of specialised discourse analysts. The test for cue phrases is used to analyse around 20 pages of naturally occuring text, from which a corpus of over 20 cue phrases is assembled. The substitutability test is then used to organise this corpus into a hierarchical taxonomy, representing the substitutability relationship between every pair of phrases. The taxonomy of cue phrases lends itself neatly to a model of relations as feature-based constructs. Many cue phrases can be interpreted as signalling just some features of relations, rather than whole relations. Small extracts from the taxonomy can be used systematically to determine the alternative values of single features; complex relation definitions can then be formed by combining the values of many features. The thesis delivers results on two levels. Firstly,it sets out a methodology for motivating a set of relation definitions, which rests on a systematic analysis of oncrete linguistic data, and demands a minimum of theoretical assumptions. Also provided are the relation definitions which result from applying the methodology. The new definitions give an interesting picture of the variation that exists amongst cuephrases, and offers a number of innovative insights into text coherence