A techniques-based framework for domain-specific synthesis of simulation models

The formal specification community has produced many languages but few structured design methods. Those which exist tend to be abstract, providing little guidance in tackling problems in particular domains. One way of devising domain -specific design methods is by reconstructing an example in the domain using the target method; then generalising the design structures to cover a class of designs in the domain and finally building an environment in which these structures may more easily be re- applied to new problems. We demonstrate this approach using animal population dynamics models as the domain and Prolog techniques as the target method.We have identified domain -specific techniques which use a parameterisation method from techniques editing but which contain information specific to the population dynamics domain; we define a problem description language which uses concepts from population dynamics; an interface which allows these concepts to be supplied; and provide an automated system which bridges between population dynamics problem description and the domain -specific techniques needed for model generation.TeMS - Techniques -based Model Synthesiser, is the system constructed as the main instrument of our research. Because it is an embodiment of our views on the issues addressed, we submitted TeMS to user evaluation by ecological modelling experts, which produced material for a broad discussion of the system itself, its approach to modelling and its potential uses on the ecological modelling scenario

Knowledge Based Systems: A Critical Survey of Major Concepts, Issues, and Techniques

This Working Paper Series entry presents a detailed survey of knowledge based systems. After being in a relatively dormant state for many years, only recently is Artificial Intelligence (AI) - that branch of computer science that attempts to have machines emulate intelligent behavior - accomplishing practical results. Most of these results can be attributed to the design and use of Knowledge-Based Systems, KBSs (or ecpert systems) - problem solving computer programs that can reach a level of performance comparable to that of a human expert in some specialized problem domain. These systems can act as a consultant for various requirements like medical diagnosis, military threat analysis, project risk assessment, etc. These systems possess knowledge to enable them to make intelligent desisions. They are, however, not meant to replace the human specialists in any particular domain. A critical survey of recent work in interactive KBSs is reported. A case study (MYCIN) of a KBS, a list of existing KBSs, and an introduction to the Japanese Fifth Generation Computer Project are provided as appendices. Finally, an extensive set of KBS-related references is provided at the end of the report

Approximate model composition for explanation generation

This thesis presents a framework for the formulation of knowledge models to sup¬ port the generation of explanations for engineering systems that are represented by the resulting models. Such models are automatically assembled from instantiated generic component descriptions, known as modelfragments. The model fragments are of suffi¬ cient detail that generally satisfies the requirements of information content as identified by the user asking for explanations. Through a combination of fuzzy logic based evidence preparation, which exploits the history of prior user preferences, and an approximate reasoning inference engine, with a Bayesian evidence propagation mechanism, different uncertainty sources can be han¬ dled. Model fragments, each representing structural or behavioural aspects of a com¬ ponent of the domain system of interest, are organised in a library. Those fragments that represent the same domain system component, albeit with different representation detail, form parts of the same assumption class in the library. Selected fragments are assembled to form an overall system model, prior to extraction of any textual infor¬ mation upon which to base the explanations. The thesis proposes and examines the techniques that support the fragment selection mechanism and the assembly of these fragments into models. In particular, a Bayesian network-based model fragment selection mechanism is de¬ scribed that forms the core of the work. The network structure is manually determined prior to any inference, based on schematic information regarding the connectivity of the components present in the domain system under consideration. The elicitation of network probabilities, on the other hand is completely automated using probability elicitation heuristics. These heuristics aim to provide the information required to select fragments which are maximally compatible with the given evidence of the fragments preferred by the user. Given such initial evidence, an existing evidence propagation algorithm is employed. The preparation of the evidence for the selection of certain fragments, based on user preference, is performed by a fuzzy reasoning evidence fab¬ rication engine. This engine uses a set of fuzzy rules and standard fuzzy reasoning mechanisms, attempting to guess the information needs of the user and suggesting the selection of fragments of sufficient detail to satisfy such needs. Once the evidence is propagated, a single fragment is selected for each of the domain system compo¬ nents and hence, the final model of the entire system is constructed. Finally, a highly configurable XML-based mechanism is employed to extract explanation content from the newly formulated model and to structure the explanatory sentences for the final explanation that will be communicated to the user. The framework is illustratively applied to a number of domain systems and is compared qualitatively to existing compositional modelling methodologies. A further empirical assessment of the performance of the evidence propagation algorithm is carried out to determine its performance limits. Performance is measured against the number of frag¬ ments that represent each of the components of a large domain system, and the amount of connectivity permitted in the Bayesian network between the nodes that stand for the selection or rejection of these fragments. Based on this assessment recommenda¬ tions are made as to how the framework may be optimised to cope with real world applications

The aural skills acquisition process of undergraduate electroacoustic (EA) music majors in the context of a new aural learning method

Thesis (D.M.A.)--Boston UniversityElectroacoustic (EA) musicians require aural skills that exist beyond tonality and meter; however, specialized ear training courses for EA music are rare in university and college music programs that offer EA studies (EaSt) in their curricula. Since 2005, this researcher has been developing and teaching EA aural training at a Canadian university in that was inspired by concepts from Auditory Scene Analysis (ASA) studies, primarily integration and segregation. In the 2009/10 academic year, the researcher conducted an action study with his intact EA aural training class of 25 first year undergraduate students majoring in EaSt for the purposes of better understanding and improving the students' aural skill acquisition process. and of refining the teaching and learning sequence. The action study was organized into four cycles of observation, critical reflection, and action, and focused on optimizing and autonomizing the skill acquisition process within the large, varied group. Actions were designed in response to critical reflection on emerging problems, evaluations of students' views about the process, their moods and attitudes, and measurements of students' achievements-with specific attention to eight EA-oriented skills and seven tonal and metric skills. Qualitative and quantitative data gathered from questionnaires, in-class surveys and tests, homework, and competence tests provided evidence of skill acquisition, primarily in loudness discrimination, timbral discrimination, tonal awareness, interval discrimination, meter discrimination, and descriptive ability. The most notable emerging problems in the skill acquisition process were related to the group's variety of ability levels, including imbalances in difficulty levels, in students' level of interest in the activities, and in the all-inclusive effectiveness of the training. The main transformational aspects of the action study were autonomization of the skill acquisition process at home through weekly reflective practice reports and developing a cooperative learning environment in the classroom through regular in-class discussion

Generation and Applications of Knowledge Graphs in Systems and Networks Biology

The acceleration in the generation of data in the biomedical domain has necessitated the use of computational approaches to assist in its interpretation. However, these approaches rely on the availability of high quality, structured, formalized biomedical knowledge. This thesis has the two goals to improve methods for curation and semantic data integration to generate high granularity biological knowledge graphs and to develop novel methods for using prior biological knowledge to propose new biological hypotheses. The first two publications describe an ecosystem for handling biological knowledge graphs encoded in the Biological Expression Language throughout the stages of curation, visualization, and analysis. Further, the second two publications describe the reproducible acquisition and integration of high-granularity knowledge with low contextual specificity from structured biological data sources on a massive scale and support the semi-automated curation of new content at high speed and precision. After building the ecosystem and acquiring content, the last three publications in this thesis demonstrate three different applications of biological knowledge graphs in modeling and simulation. The first demonstrates the use of agent-based modeling for simulation of neurodegenerative disease biomarker trajectories using biological knowledge graphs as priors. The second applies network representation learning to prioritize nodes in biological knowledge graphs based on corresponding experimental measurements to identify novel targets. Finally, the third uses biological knowledge graphs and develops algorithmics to deconvolute the mechanism of action of drugs, that could also serve to identify drug repositioning candidates. Ultimately, the this thesis lays the groundwork for production-level applications of drug repositioning algorithms and other knowledge-driven approaches to analyzing biomedical experiments

Semantic-guided predictive modeling and relational learning within industrial knowledge graphs

The ubiquitous availability of data in today’s manufacturing environments, mainly driven by the extended usage of software and built-in sensing capabilities in automation systems, enables companies to embrace more advanced predictive modeling and analysis in order to optimize processes and usage of equipment. While the potential insight gained from such analysis is high, it often remains untapped, since integration and analysis of data silos from different production domains requires high manual effort and is therefore not economic. Addressing these challenges, digital representations of production equipment, so-called digital twins, have emerged leading the way to semantic interoperability across systems in different domains. From a data modeling point of view, digital twins can be seen as industrial knowledge graphs, which are used as semantic backbone of manufacturing software systems and data analytics. Due to the prevalent historically grown and scattered manufacturing software system landscape that is comprising of numerous proprietary information models, data sources are highly heterogeneous. Therefore, there is an increasing need for semi-automatic support in data modeling, enabling end-user engineers to model their domain and maintain a unified semantic knowledge graph across the company. Once the data modeling and integration is done, further challenges arise, since there has been little research on how knowledge graphs can contribute to the simplification and abstraction of statistical analysis and predictive modeling, especially in manufacturing. In this thesis, new approaches for modeling and maintaining industrial knowledge graphs with focus on the application of statistical models are presented. First, concerning data modeling, we discuss requirements from several existing standard information models and analytic use cases in the manufacturing and automation system domains and derive a fragment of the OWL 2 language that is expressive enough to cover the required semantics for a broad range of use cases. The prototypical implementation enables domain end-users, i.e. engineers, to extend the basis ontology model with intuitive semantics. Furthermore it supports efficient reasoning and constraint checking via translation to rule-based representations. Based on these models, we propose an architecture for the end-user facilitated application of statistical models using ontological concepts and ontology-based data access paradigms. In addition to that we present an approach for domain knowledge-driven preparation of predictive models in terms of feature selection and show how schema-level reasoning in the OWL 2 language can be employed for this task within knowledge graphs of industrial automation systems. A production cycle time prediction model in an example application scenario serves as a proof of concept and demonstrates that axiomatized domain knowledge about features can give competitive performance compared to purely data-driven ones. In the case of high-dimensional data with small sample size, we show that graph kernels of domain ontologies can provide additional information on the degree of variable dependence. Furthermore, a special application of feature selection in graph-structured data is presented and we develop a method that allows to incorporate domain constraints derived from meta-paths in knowledge graphs in a branch-and-bound pattern enumeration algorithm. Lastly, we discuss maintenance of facts in large-scale industrial knowledge graphs focused on latent variable models for the automated population and completion of missing facts. State-of-the art approaches can not deal with time-series data in form of events that naturally occur in industrial applications. Therefore we present an extension of learning knowledge graph embeddings in conjunction with data in form of event logs. Finally, we design several use case scenarios of missing information and evaluate our embedding approach on data coming from a real-world factory environment. We draw the conclusion that industrial knowledge graphs are a powerful tool that can be used by end-users in the manufacturing domain for data modeling and model validation. They are especially suitable in terms of the facilitated application of statistical models in conjunction with background domain knowledge by providing information about features upfront. Furthermore, relational learning approaches showed great potential to semi-automatically infer missing facts and provide recommendations to production operators on how to keep stored facts in synch with the real world

The weight of experience: an investigation of probability weighting under decisions from experience

In decisions from experience tasks objective information regarding payoffs and probabilities must be inferred from samples of possible outcomes. A series of recent experiments has revealed that people show deviating choice behaviour in such tasks, indicating underweighting of small probabilities instead of overweighting of small probabilities as in decisions from description. In a range of experiments, the research presented in this thesis provides a new direction by showing that such reversals from overweighting to underweighting in decisions from experience are very robust and can be replicated even if all the existing explanations - sampling error, recency weighting and judgement error - are experimentally controlled for. Furthermore, reversals were replicated within common decision making biases like the common ratio effect. An important, but unexpected, new finding has been the observation of a reversed reflection effect under decisions from experience. This suggests that the difference between choice behaviour may not be restricted to underlying transformations of probabilities, as suggested in the literature. Drawing from an extensive range of model tests and parameter estimations, it is also demonstrated that the differences are reflected in the best fitting parameter values for prospect theory under decisions from experience. However, it is also shown that simple reinforcement models, which provide a more intuitive rationale for experiential choice behaviour, can account for the data just as well, without any assumptions regarding the weighting of probabilities

Doctor of Philosophy

dissertationThe explosion of structured Web data (e.g., online databases, Wikipedia infoboxes) creates many opportunities for integrating and querying these data that go far beyond the simple search capabilities provided by search engines. Although much work has been devoted to data integration in the database community, the Web brings new challenges: the Web-scale (e.g., the large and growing volume of data) and the heterogeneity in Web data. Because there are so much data, scalable techniques that require little or no manual intervention and that are robust to noisy data are needed. In this dissertation, we propose a new and effective approach for matching Web-form interfaces and for matching multilingual Wikipedia infoboxes. As a further step toward these problems, we propose a general prudent schema-matching framework that matches a large number of schemas effectively. Our comprehensive experiments for Web-form interfaces and Wikipedia infoboxes show that it can enable on-the-fly, automatic integration of large collections of structured Web data. Another problem we address in this dissertation is schema discovery. While existing integration approaches assume that the relevant data sources and their schemas have been identified in advance, schemas are not always available for structured Web data. Approaches exist that exploit information in Wikipedia to discover the entity types and their associate schemas. However, due to inconsistencies, sparseness, and noise from the community contribution, these approaches are error prone and require substantial human intervention. Given the schema heterogeneity in Wikipedia infoboxes, we developed a new approach that uses the structured information available in infoboxes to cluster similar infoboxes and infer the schemata for entity types. Our approach is unsupervised and resilient to the unpredictable skew in the entity class distribution. Our experiments, using over one hundred thousand infoboxes extracted from Wikipedia, indicate that our approach is effective and produces accurate schemata for Wikipedia entities

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl