Modelling causal reasoning

PhDAlthough human causal reasoning is widely acknowledged as an object of scientific enquiry, there is little consensus on an appropriate measure of progress. Up-to-date evidence of the standard method of research in the field shows that this method has been rejected at the birth of modern science. We describe an instance of the standard scientific method for modelling causal reasoning (causal calculators). The method allows for uniform proofs of three relevant computational properties: correctness of the model with respect to the intended model, full abstraction of the model (function) with respect to the equivalence of reasoning scenarios (input), and formal relations of equivalence and subsumption between models. The method extends and exploits the systematic paradigm [Handbook of Logic in Artificial Intelligence and Logic Programming, volume IV, p. 439-498, Oxford 1995] to fit with our interpretation of it. Using the described method, we present results for some major models, with an updated summary spanning seventy-two years of research in the field

Semantic Service Description Framework for Efficient Service Discovery and Composition

Web services have been widely adopted as a new distributed system technology by industries in the areas of, enterprise application integration, business process management, and virtual organisation. However, lack of semantics in current Web services standards has been a major barrier in the further improvement of service discovery and composition. For the last decade, Semantic Web Services have become an important research topic to enrich the semantics of Web services. The key objective of Semantic Web Services is to achieve automatic/semi-automatic Web service discovery, invocation, and composition. There are several existing semantic Web service description frameworks, such as, OWL-S, WSDL-S, and WSMF. However, existing frameworks have several issues, such as insufficient service usage context information, precisely specified requirements needed to locate services, lacking information about inter-service relationships, and insufficient/incomplete information handling, make the process of service discovery and composition not as efficient as it should be. To address these problems, a context-based semantic service description framework is proposed in this thesis. This framework focuses on not only capabilities of Web services, but also the usage context information of Web services, which we consider as an important factor in efficient service discovery and composition. Based on this framework, an enhanced service discovery mechanism is proposed. It gives service users more flexibility to search for services in more natural ways rather than only by technical specifications of required services. The service discovery mechanism also demonstrates how the features provided by the framework can facilitate the service discovery and composition processes. Together with the framework, a transformation method is provided to transform exiting service descriptions into the new framework based descriptions. The framework is evaluated through a scenario based analysis in comparison with OWL-S and a prototype based performance evaluation in terms of query response time, the precision and recall ratio, and system scalability

Logic, Reasoning, Argumentation: Insights from the Wild

This article provides a brief selective overview and discussion of recent research into natural language argumentation that may inform the study of human reasoning on the assumption that an episode of argumentation issues an invitation to accept a corresponding inference. As this research shows, arguers typically seek to establish new consequences based on prior information. And they typically do so vis-à-vis a real or an imagined opponent, or an opponent-position, in ways that remain sensitive to considerations of context, audiences, and goals. Deductively valid inferences remain a limiting case of such reasoning. In view of these insights, it may appear less surprising that allegedly “irrational” behavior can regularly be produced in experimental settings that expose subjects to standardized reasoning tasks

Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives

Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future