Extending relational model transformations to better support the verification of increasingly autonomous systems

Over the past decade the capabilities of autonomous systems have been steadily increasing. Unmanned systems are moving from systems that are predominantly remotely operated, to systems that include a basic decision making capability. This is a trend that is expected to continue with autonomous systems making decisions in increasingly complex environments, based on more abstract, higher-level missions and goals. These changes have significant implications for how these systems should be designed and engineered. Indeed, as the goals and tasks these systems are to achieve become more abstract, and the environments they operate in become more complex, are current approaches to verification and validation sufficient? Domain Specific Modelling is a key technology for the verification of autonomous systems. Verifying these systems will ultimately involve understanding a significant number of domains. This includes goals/tasks, environments, systems functions and their associated performance. Relational Model Transformations provide a means to utilise, combine and check models for consistency across these domains. In this thesis an approach that utilises relational model transformation technologies for systems verification, Systems MDD, is presented along with the results of a series of trials conducted with an existing relational model transformation language (QVT-Relations). These trials identified a number of problems with existing model transformation languages, including poorly or loosely defined semantics, differing interpretations of specifications across different tools and the lack of a guarantee that a model transformation would generate a model that was compliant with its associated meta-model. To address these problems, two related solvers were developed to assist with realising the Systems MDD approach. The first solver, MMCS, is concerned with partial model completion, where a partial model is defined as a model that does not fully conform with its associated meta-model. It identifies appropriate modifications to be made to a partial model in order to bring it into full compliance. The second solver, TMPT, is a relational model transformation engine that prioritises target models. It considers multiple interpretations of a relational transformation specification, chooses an interpretation that results in a compliant target model (if one exists) and, optionally, maximises some other attribute associated with the model. A series of experiments were conducted that applied this to common transformation problems in the published literature

Practical reasoning for defeasable description logics.

Doctor of Philosophy in Mathematics, Statistics and Computer Science. University of KwaZulu-Natal, Durban 2016.Description Logics (DLs) are a family of logic-based languages for formalising ontologies. They have useful computational properties allowing the development of automated reasoning engines to infer implicit knowledge from ontologies. However, classical DLs do not tolerate exceptions to speci ed knowledge. This led to the prominent research area of nonmonotonic or defeasible reasoning for DLs, where most techniques were adapted from seminal works for propositional and rst-order logic. Despite the topic's attention in the literature, there remains no consensus on what \sensible" defeasible reasoning means for DLs. Furthermore, there are solid foundations for several approaches and yet no serious implementations and practical tools. In this thesis we address the aforementioned issues in a broad sense. We identify the preferential approach, by Kraus, Lehmann and Magidor (KLM) in propositional logic, as a suitable abstract framework for de ning and studying the precepts of sensible defeasible reasoning. We give a generalisation of KLM's precepts, and their arguments motivating them, to the DL case. We also provide several preferential algorithms for defeasible entailment in DLs; evaluate these algorithms, and the main alternatives in the literature, against the agreed upon precepts; extensively test the performance of these algorithms; and ultimately consolidate our implementation in a software tool called Defeasible-Inference Platform (DIP). We found some useful entailment regimes within the preferential context that satisfy all the KLM properties, and some that have scalable performance in real world ontologies even without extensive optimisation

Change Management for Distributed Ontologies

Akkermans, J.M. [Promotor]Schreiber, A.T. [Promotor

Conférence Nationale d'Intelligence Artificielle Année 2020

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