An important goal for machine learning is to transfer knowledge between tasks. For example, learning to play RoboCup Keepaway should contribute to learning the full game of RoboCup soccer. Often approaches to task transfer focus on transforming the original representation to fit the new task. Such representational transformations are necessary because the target task often requires new state information that was not included in the original representation. In RoboCup Keepaway, changing from the 3 vs. 2 variant of the task to 4 vs. 3 adds state information for each of the new players. In contrast, this dissertation explores the idea that transfer is most effective if the representation is designed to be the same even across different tasks. To this end, (1) the bird’s eye view (BEV) representation is introduced, which can represent different tasks on the same two-dimensional map. Because the BEV represents state information associated with positions instead of objects, it can be scaled to more objects without manipulation. In this way, both the 3 vs. 2 and 4 vs. 3 Keepaway tasks can be represented on the same BEV, which is (2) demonstrated in this dissertation. Yet a challenge for such representation is that a raw two-dimensional map is highdimensional and unstructured. This dissertation demonstrates how this problem is addressed naturally by the Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) approach. HyperNEAT evolves an indirect encoding, which compresses the representation by exploiting its geometry. The dissertation then explores further exploiting the power of such encoding, beginning by (3) enhancing the configuration of the BEV with a focus on iii modularity. The need for further nonlinearity is then (4) investigated through the addition of hidden nodes. Furthermore, (5) the size of the BEV can be manipulated because it is indirectly encoded. Thus the resolution of the BEV, which is dictated by its size, is increased in precision and culminates in a HyperNEAT extension that is expressed at effectively infinite resolution. Additionally, scaling to higher resolutions through gradually increasing the size of the BEV is explored. Finally, (6) the ambitious problem of scaling from the Keepaway task to the Half-field Offense task is investigated with the BEV. Overall, this dissertation demonstrates that advanced representations in conjunction with indirect encoding can contribute to scaling learning techniques to more challenging tasks, such as the Half-field Offense RoboCup soccer domain.

Permission from the author to release this work as open access is pending. Please contact the ICS library if you would like to view this work.

Semantic interoperability is essential on the Semantic Web to enable different information systems to exchange data. Ontology matching has been recognised as a means to achieve semantic interoperability on the Web by identifying similar information in heterogeneous ontologies. Existing ontology matching approaches have two major limitations. The first limitation relates to similarity metrics, which provide a pessimistic value when considering complex objects such as strings and conceptual entities. The second limitation relates to the role of description logic reasoning. In particular, most approaches disregard implicit information about entities as a source of background knowledge. In this thesis, we first present a new similarity function, called the degree of commonality coefficient, to compute the overlap between two sets based on the similarity between their elements. The results of our evaluations show that the degree of commonality performs better than traditional set similarity metrics in the ontology matching task. Secondly, we have developed the Knowledge Organisation System Implicit Mapping (KOSIMap) framework, which differs from existing approaches by using description logic reasoning (i) to extract implicit information as background knowledge for every entity, and (ii) to remove inappropriate correspondences from an alignment. The results of our evaluation show that the use of Description Logic in the ontology matching task can increase coverage. We identify people interested in ontology matching and reasoning techniques as the target audience of this work

by Wong Ka-sing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references. / Abstracts in English and Chinese. / Chapter Chapter One --- Introduction --- p.1 / Chapter Chapter Two --- Research in Expertise --- p.4 / Chapter A --- Inherited Traits and Acquisition of Expertise --- p.4 / Chapter B --- Expert-novice Differences : Knowledge --- p.5 / Chapter C --- Practice and Development of Expertise --- p.12 / Chapter Chapter Three --- Expertise in teaching --- p.14 / Importance of Subject Matter Expertise in Relationship to Pedagogical Reasoning and Pedagogical Content Knowledge --- p.18 / Chapter Chapter Four --- Domain knowledge and its representation --- p.24 / Knowledge Representations and Networks --- p.26 / Techniques of Eliciting and Representing Knowledge --- p.29 / Chapter Chapter Five --- The Pathfinder algorithm and research resultsin relationship to education and learning --- p.34 / Chapter A --- Validity of the Pathfinder Network --- p.36 / Chapter B --- The Interpretation of Links in Pathfinder Network --- p.38 / Chapter Chapter Six --- Research findings relevant to the present study --- p.39 / Chapter A --- "Gomez, R. L, Hadfield O. D. and Housner, L D (1996): "" Concept Maps and Simulated Teaching Episodes as Indicators of Competence in Teaching Elementary Mathematics""" --- p.39 / Chapter B --- "Goldsmith, T. E. , Johnson, P.j., and Acton, W. H (1991): "" Assessing structural knowledge""" --- p.43 / Chapter C --- "Acton, W. H., Johnson, P. j. and Goldsmith, T. E (1994): ""Structural knowledge assessment: Comparison of referent structures""" --- p.45 / Chapter D --- "Gonzalvo, P., Canas, J. J., and Bajo. M. T. (1994): "" Structural representations in knowledge acquisition""" --- p.48 / Chapter E --- "Johnson, P. J. Goldsmith, T. E, and Teague, K. W (1994).: "" Locus of predictive advantage in Pathfinder- based representations of classroom knowledge""" --- p.50 / Chapter Chapter Seven --- The research question --- p.52 / Research Hypothesis --- p.53 / The Subjects of this Study --- p.54 / Method of Data Collection --- p.57 / Concepts Used for Rating --- p.58 / Chapter Chapter Eight --- Results and analysis --- p.60 / Qualitative Analysis of Pathfinder Networks and MDS Solutions --- p.60 / Testing of Hypotheses --- p.70 / Discussion --- p.75 / Chapter Chapter Nine --- Limitations of the present study --- p.78

The study addresses the automated translation of architectural drawings from 2D Computer Aided Drafting (CAD) data into a Building Information Model (BIM), with emphasis on the nature, possible role, and limitations of a drafting language Knowledge Representation (KR) on the problem and process. The central idea is that CAD to BIM translation is a complex diagrammatic interpretation problem requiring a domain (drafting language) KR to render it tractable and that such a KR can take the form of an information model. Formal notions of drawing-as-language have been advanced and studied quite extensively for close to 25 years. The analogy implicitly encourages comparison between problem structures in both domains, revealing important similarities and offering guidance from the more mature field of Natural Language Understanding (NLU). The primary insight we derive from NLU involves the central role that a formal language description plays in guiding the process of interpretation (inferential reasoning), and the notable absence of a comparable specification for architectural drafting. We adopt a modified version of Engelhard's approach which expresses drawing structure in terms of a symbol set, a set of relationships, and a set of compositional frameworks in which they are composed. We further define an approach for establishing the features of this KR, drawing upon related work on conceptual frameworks for diagrammatic reasoning systems. We augment this with observation of human subjects performing a number of drafting interpretation exercises and derive some understanding of its inferential nature therefrom. We consider this indicative of the potential range of inferential processes a computational drafting model should ideally support. The KR is implemented as an information model using the EXPRESS language because it is in the public domain and is the implementation language of the target Industry Foundation Classes (IFC) model. We draw extensively from the IFC library to demonstrate that it can be applied in this manner, and apply the MVD methodology in defining the scope and interface of the DOM and IFC. This simplifies the IFC translation process significantly and minimizes the need for mapping. We conclude on the basis of selective implementations that a model reflecting the principles and features we define can indeed provide needed and otherwise unavailable support in drafting interpretation and other problems involving reasoning with this class of diagrammatic representations.

Thesis (M.Sc. (Hons.)) -- University of Western Sydney, 2001. / "A thesis submitted for the degree of Master of Science (Honours) - Computing and Information Technology at University of Western Sydney" Bibliography : leaves 93-98.

The domain of molecular biology is complex and vast. Bio-ontologies and information visualisation have arisen in recent years as means to assist biologists in making sense of this information. Ontologies can enable the construction of conceptual queries, but existing systems to do this are too technical for most biologists. OntoDas, the software developed as part of this thesis work, demonstrates how the application of techniques from information visualisation and human computer interaction can result in software which enables biologists to construct conceptual queries. / Thesis (M.Comp.Sc.)-Universty of KwaZulu-Natal, Pietermaritzburg, 2007.

There is almost a universal agreement among scholars in information retrieval (IR) research that knowledge representation needs improvement. As core component of an IR system, improvement of the knowledge representation system has so far involved manipulation of this component based on principles such as vector space, probabilistic approach, inference network, and language modeling, yet the required improvement is still far from fruition. One promising approach that is highly touted to offer a potential solution exists in the cognitive paradigm, where knowledge representation practice should involve, or start from, modeling the human conceptual system. This study based on two related cognitive theories: the theory-based approach to concept representation and the psychological theory of semantic relations, ventured to explore the connection between the human conceptual model and the knowledge representation model (represented by samples of concepts and relations from the unified medical language system, UMLS). Guided by these cognitive theories and based on related and appropriate data-analytic tools, such as nonmetric multidimensional scaling, hierarchical clustering, and content analysis, this study aimed to conduct an exploratory investigation to answer four related questions. Divided into two groups, a total of 89 research participants took part in two sets of cognitive tasks. The first group (49 participants) sorted 60 food names into categories followed by simultaneous description of the derived categories to explain the rationale for category judgment. The second group (40 participants) performed sorting 47 semantic relations (the nonhierarchical associative types) into 5 categories known a priori. Three datasets resulted as a result of the cognitive tasks: food-sorting data, relation-sorting data, and free and unstructured text of category descriptions. Using the data analytic tools mentioned, data analysis was carried out and important results and findings were obtained that offer plausible explanations to the 4 research questions. Major results include the following: (a) through discriminant analysis category members were predicted consistently in 70% of the time; (b) the categorization bases are largely simplified rules, naïve explanations, and feature-based; (c) individuals theoretical explanation remains valid and stays stable across category members; (d) the human conceptual model can be fairly reconstructed in a low-dimensional space where 93% of the variance in the dimensional space is accounted for by the subjects performance; (e) participants consistently classify 29 of the 47 semantic relations; and, (f) individuals perform better in the functional and spatial dimensions of the semantic relations classification task and perform poorly in the conceptual dimension.

Design is hard and needs to be supported by software. One of the ways software can support designers is by providing analogical reasoning. To make analogical reasoning work well, the software makers need to know how to create a knowledge representation that will facilitate the kind of analogies that the designers want. This thesis will inform software makers by experimenting with two kinds of knowledge representations, called device-centric (DC) and environment-centric (EC), and to try to determine the relative benefits of using either one of them for analogical matching. We performed computational experiments, using Structure Mapping Engine for matching, to determine the quantity and quality of analogical matches that are produced when the representation is varied. We conducted a limited human experiment, using questionnaires and repertory grids, to determine if any of the computational results were novel, and to determine if the human similarity ratings between devices correlated with the computer results. We show that design software should use DC representations to produce a few focused matches which have high average weight. It should use EC representations to produce many matches some of high weight and some of low weight. Based on our human experiment, design software can use either DC or EC representations to produce novel matches. Our experiments also show that human matches correlate most strongly with a combined DC and EC representation and that their similarity reasons are more EC than DC. This suggests that designers tend to think more in EC terms than in DC terms.

This study investigated whether age differences in deceit detection are related to impairments in emotion recognition. Key cues to deceit are facial expressions of emotion (Frank and Ekman, 1997). The aging literature has shown an age-related decline in decoding emotions (e.g., Malatesta, Izard, Culver, and Nicolich, 1987). In the present study, 354 participants were presented with 20 interviews and asked to decide whether each man was lying or telling the truth. Ten interviews involved a crime and ten a social opinion. Each participant was in one of three presentation conditions: 1) visual only, 2) audio only, or 3) audio-visual. For crime interviews, age-related impairments in emotion recognition hindered older adults in the visual only condition. In the opinion topic interviews, older adults exhibited a truth bias which rendered them worse at detecting deceit than young adults. Cognitive and dispositional variables did not help to explain the age differences in the ability to detect deceit.