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Teilw. zugl.: @Toronto, Univ., Diss., 1994.

In this thesis, an investigation into how visualisation and manipulation tools can provide better support for learners of Modal logic is described. Problems associated with learning Modal logic are also researched. Seven areas topics in Modal logic are investigated, as is the influence of domain independent factors (e. g. motivation) on learning. Studies show that students find concepts such as Modal proofs and systems difficult to learn, whilst possible worlds and Modes are fairly straightforward. Areas such as reference, belief and accessibility relations fall between these extremes. Two roles for representations in reasoning are identified: providing a concrete domain for students to reason about, and supporting the process of reasoning. Systems which make use of these complementary representations were found to be more effective for learners than either the syntactic or the diagrammatic representations traditionally used to teach Modal logic. A review of software used to support students learning logic highlights two important features: the use of examples, and automation of routine tasks. A learning environment for Modal logic was designed which incorporated these. The environment was developed using an adapted version of Smalltalk's Model-View-Controller mechanism, and incorporates complementary representations, enhance by direct manipulation. A further study investigates the added benefits of using this tool, as opposed to using the same representation but working with pen and paper. This confirms the importance of using 'concrete' content representations and minimising learners' cognitive load. Performance measures show that software users learnt more, had a deeper style of learning, and found the topics less abstract than their counterparts working with pen & paper. This research shows that complementary representations are an effective way of supporting students studying Modal logic, and that visualisation and manipulation tools which incorporate these systems will provide additional benefits for learners.

Ontologies are a formalization of a particular domain through a collection of axioms founded, usually, in Description Logic. Within its structure, the knowledge in the axioms contain semantic information of the domain and that fact has motivated the development of methods that capture such knowledge and, therefore, can perform different tasks such as prediction and similarity computation. Under the same motivation, we present a new method to capture semantic information from an ontology. We explore the logical component of the ontologies and their theoretical connections with their counterparts in Category Theory, as Category Theory develops a structural representation of mathematical systems and the structures found there have strong relationships with Logic founded in the so-called Curry-Howard-Lambek isomorphism. In this regard, we have developed a method that represents logical axioms as Categorical diagrams and uses the commutativity property of such diagrams as a constraint to generate embeddings of ontology classes in Rn. Furthermore, as a contribution in terms of software tools, we developed mOWL: Machine Learning Library With Ontologies. mOWL is a software library that incorporates methods in the state of the art, usually in Machine Learning, which utilizes ontologies as background knowledge. We rely on mOWL to implement the proposed method and compare it with the existing ones.

Au Yeung Ching Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 97-110). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Semantic Web and Ontologies --- p.3 / Chapter 1.2 --- Motivations --- p.5 / Chapter 1.2.1 --- Fuzziness of Concepts --- p.6 / Chapter 1.2.2 --- Typicality of Objects --- p.6 / Chapter 1.2.3 --- Context and Its Effect on Reasoning --- p.8 / Chapter 1.3 --- Objectives --- p.9 / Chapter 1.4 --- Contributions --- p.10 / Chapter 1.5 --- Structure of the Thesis --- p.11 / Chapter 2 --- Background Study --- p.13 / Chapter 2.1 --- The Semantic Web --- p.14 / Chapter 2.2 --- Ontologies --- p.16 / Chapter 2.3 --- Description Logics --- p.20 / Chapter 2.4 --- Fuzzy Set Theory --- p.23 / Chapter 2.5 --- Concepts and Categorization in Cognitive Psychology --- p.25 / Chapter 2.5.1 --- Theory of Concepts --- p.26 / Chapter 2.5.2 --- Goodness of Example versus Degree of Typicality --- p.28 / Chapter 2.5.3 --- Similarity between Concepts --- p.29 / Chapter 2.5.4 --- Context and Context Effects --- p.31 / Chapter 2.6 --- Handling of Uncertainty in Ontologies and Description Logics --- p.33 / Chapter 2.7 --- Typicality in Models for Knowledge Representation --- p.35 / Chapter 2.8 --- Semantic Similarity in Ontologies and the Semantic Web --- p.39 / Chapter 2.9 --- Contextual Reasoning --- p.41 / Chapter 3 --- A Formal Model of Ontology --- p.44 / Chapter 3.1 --- Rationale --- p.45 / Chapter 3.2 --- Concepts --- p.47 / Chapter 3.3 --- Characteristic Vector and Property Vector --- p.47 / Chapter 3.4 --- Subsumption of Concepts --- p.49 / Chapter 3.5 --- Likeliness of an Individual in a Concept --- p.51 / Chapter 3.6 --- Prototype Vector and Typicality --- p.54 / Chapter 3.7 --- An Example --- p.59 / Chapter 3.8 --- Similarity between Concepts --- p.61 / Chapter 3.9 --- Context and Contextualization of Ontology --- p.65 / Chapter 3.9.1 --- Formal Definitions --- p.67 / Chapter 3.9.2 --- Contextualization of an Ontology --- p.69 / Chapter 3.9.3 --- "Contextualized Subsumption Relations, Likeliness, Typicality and Similarity" --- p.71 / Chapter 4 --- Discussions and Analysis --- p.73 / Chapter 4.1 --- Properties of the Formal Model for Fuzzy Ontologies --- p.73 / Chapter 4.2 --- Likeliness and Typicality --- p.78 / Chapter 4.3 --- Comparison between the Proposed Model and Related Works --- p.81 / Chapter 4.3.1 --- Comparison with Traditional Ontology Models --- p.81 / Chapter 4.3.2 --- Comparison with Fuzzy Ontologies and DLs --- p.82 / Chapter 4.3.3 --- Comparison with Ontologies modeling Typicality of Objects --- p.83 / Chapter 4.3.4 --- Comparison with Ontologies modeling Context --- p.84 / Chapter 4.3.5 --- Limitations of the Proposed Model --- p.85 / Chapter 4.4 --- "Significance of Modeling Likeliness, Typicality and Context in Ontologies" --- p.86 / Chapter 4.5 --- Potential Application of the Model --- p.88 / Chapter 4.5.1 --- Searching in the Semantic Web --- p.88 / Chapter 4.5.2 --- Benefits of the Formal Model of Ontology --- p.90 / Chapter 5 --- Conclusions and Future Work --- p.91 / Chapter 5.1 --- Conclusions --- p.91 / Chapter 5.2 --- Future Research Directions --- p.93 / Publications --- p.96 / Bibliography --- p.97

Most computer game characters are either ``talkers,' i.e., they engage in dialogue with the player, or ``fighters,' i.e., they engage in actions against or with the player, and that may affect the virtual world. The reason for this dichotomy is a corresponding gap in the underlying development technologies used for each kind of character. Using concepts from task modeling and computational linguistics, we have developed a new kind of character-authoring technology which bridges this gap, thereby making it possible to create richer and more interesting characters for computer games.

The goal of the commodity expert project was to develop a prototype program that would act as an intelligent assistant to a commodity market analyst. Since expert analysis must deal with very large, yet incomplete, data bases of unreliable facts about a complex world, the project would stringently test the applicability of Artificial Intelligence techniques. After a significant effort however, I am forced to the conclusion that an intelligent, real-world system of the kind envisioned is currently out of reach. Some of the difficulties were due to the size and complexity of the domain. As its true scale became evident, the available resources progressively appeared less adequate. The representation and reasoning problems that arose were persistently difficult and fundamental work is needed before the tools will be sufficient to engineer truly intelligent assistants. Despite these difficulties, perhaps even because of them, much can be learned from the project. To assist future applications projects, I explain in this report some of the reasons for the negative result, and also describe some positive ideas that were gained along the way. In doing so, I hope to convey the respect I have developed for the complexity of real-world domains, and the difficulty of describing the ways experts deal them.

We develop a qualitative method for understanding and representing phase space structures of complex systems and demonstrate the method with a program, MAPS --- Modeler and Analyzer for Phase Spaces, using deep domain knowledge of dynamical system theory. Given a dynamical system, the program generates a complete, high level symbolic description of the phase space structure sensible to human beings and manipulable by other programs. Using the phase space descriptions, we are developing a novel control synthesis strategy to automatically synthesize a controller for a nonlinear system in the phase space to achieve desired properties.

TYPICAL is a package for describing and making automatic inferences about a broad class of SCHEME predicate functions. These functions, called types following popular usage, delineate classes of primitive SCHEME objects, composite data structures, and abstract descriptions. TYPICAL types are generated by an extensible combinator language from either existing types or primitive terminals. These generated types are located in a lattice of predicate subsumption which captures necessary entailment between types; if satisfaction of one type necessarily entail satisfaction of another, the first type is below the second in the lattice. The inferences make by TYPICAL computes the position of the new definition within the lattice and establishes it there. This information is then accessible to both later inferences and other programs (reasoning systems, code analyzers, etc) which may need the information for their own purposes. TYPICAL was developed as a representation language for the discovery program Cyrano; particular examples are given of TYPICAL's application in the Cyrano program.

In Corpus Based Knowledge Representation [CBKR], limited association capability, that is, no criteria in place to extract substantial associations in the corpus, and lack of support for hypothesis testing and prediction in context, restricted the application of the methodology by information specialists and data analysts. In this thesis, the researcher proposed a framework called CBKR+ to increase the expressiveness of CBKR by identifying and incorporating association criteria to allow the support of new forms of analyses related to hypothesis testing and prediction in context. <br /><br /> As contributions of the CBKR+ framework, the researcher (1) defined a new domain categorization model called Basis for Categorization model, (2) incorporated the Basis for Categorization model to (a) facilitate a first level categorization of the schema components in the corpus, and (b) define the Set of Criteria for Association to cover all types of associations and association agents, (3) defined analysis mechanisms to identify and extract further associations in the corpus in the form of the Set of Criteria for Association, and (4) improved the expressiveness of the representation, and made it suitable for hypothesis testing and prediction in context using the above. <br /><br /> The application of the framework was demonstrated, first, by using it on examples from the CBKR methodology, and second, by applying it on 12 domain representations acquired from multiple sources from the physical-world domain of Criminology. The researcher arrived at the conclusion that the proposed CBKR+ framework provided an organized approach that was more expressive, and supported deeper analyses through more diagnostic and probability-based forms of queries.