Language acquisition and machine learning

In this paper, we review recent progress in the field of machine learning and examine its implications for computational models of language acquisition. As a framework for understanding this research, we propose four component tasks involved in learning from experience - aggregation, clustering, characterization, and storage. We then consider four common problems studied by machine learning researchers - learning from examples, heuristics learning, conceptual clustering, and learning macro-operators - describing each in terms of our framework. After this, we turn to the problem of grammar acquisition, relating this problem to other learning tasks and reviewing four AI systems that have addressed the problem. Finally, we note some limitations of the earlier work and propose an alternative approach to modeling the mechanisms underlying language acquisition

Toward A Formal Definition of Task Representation

This paper addresses the issue of how tasks within an organizational context should be represented from the perspective of a single decision maker. Based on a previous paper (Hackathorn, 1981), this paper presents a formal ism for task representation based on recent work in the Knowledge Representation area. The formalism is called Simple Associative Network (SAN). The implications of this formalism result in the discussion of several issues, such as: (a) the nature of task occurrence, (b) handling multiple task types of a task occurrence, (c) means and goals as a specialization of task types, and (d) control structures among task types

Dmodel and Dalgebra : a data model and algebra for office documents

This dissertation presents a data model (called D_model) and an algebra (called D_ algebra) for office documents. The data model adopts a very natural view of modeling office documents. Documents are grouped into classes; each class is characterized by a frame template , which describes the properties (or attributes) for the class of documents. A frame template is instantiated by providing it with values to form a frame instance which becomes the synopsis of the document of the class associated with the frame template. Different frame instances can be grouped into a folder. Therefore, a folder is a set of frame instances which need not be over the same frame template. The D_model is a dual model which describes documents using two hierarchies: a document type hierarchy which depicts the structural organization of the documents and a folder organization, which represents the user\u27s real-world document filing system. The document type hierarchy exploits structural commonalities between frame templates. Such a hierarchy helps classify various documents. The folder organization mimics the user\u27s real-world document filing system and provides the user with an intuitively clear view of the filing system. This facilitates document retrieval activities. The D_algebra includes a family of operators which together comprise the fundamental query language for the D_model. The algebra provides operators that can be applied to folders which contain frame instances of different types. It has more expressive power than the relational algebra. It extends the classical relational algebra by associating attributes with types, and supporting attribute inheritance. Aggregate operators which can be applied to different frame instances in a folder are also provided. The proposed algebra is used as a sound basis to express the semantics of a high level query language for a document processing system, called TEXPROS. In the model, frame instances can represent incomplete information. Null values of the form value at present unknown are used to denote missing information in some fields of the incomplete frame instances. This dissertation provides a proof-theoretic characterization of the data model and defines the semantics of the null values within the proof-theoretic paradigm

Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure.However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock.This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning:How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies?This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances.First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential buildings. %, which were open-sourced in the interest of developing benchmarks. Third, a pioneering hierarchical forecasting method was designed, bridging individual and aggregated building load predictions in a coherent, data-efficient fashion. The approach was evaluated over hierarchies of 37, 140, and 383 nodal elements and showcased improved accuracy and coherency performances against disjointed prediction systems.Finally, building occupants and urban energy planning strategies are investigated under the prism of uncertainty. In a neighborhood of 41 Dutch residential buildings, occupants were determined to significantly impact optimal energy community designs in the context of weather and economic uncertainties.Overall, the thesis demonstrated the added value of multi-scale approaches in all analytical classes while fostering best data-science practices in the sector from benchmarks and open-source implementations

Generalization Per Category: Theory And Application

The concept of Generalization Per Category (GPO is formalized. It is shown that GPC imposes lattice structures on entity types and their subtypes. A high level application oriented data definition language based on the GPC is outlined which allows the system to derive general entity types and organize their instances. Users are freed from undue efforts in the design of databases which are about entity types with rich varieties and high populations. Effective browsing of these databases and efficient execution of frequent queries against them are achieved by using the lattice structures among the entity types and their subtypes

A conceptual model for megaprogramming

Megaprogramming is component-based software engineering and life-cycle management. Magaprogramming and its relationship to other research initiatives (common prototyping system/common prototyping language, domain specific software architectures, and software understanding) are analyzed. The desirable attributes of megaprogramming software components are identified and a software development model and resulting prototype megaprogramming system (library interconnection language extended by annotated Ada) are described

Digital Government: Knowledge Management Over Time-Varying Geospatial Datasets

Spatially-related data is collected by many government agencies in various formats and for various uses. This project seeks to facilitate the integration of these data, thus providing new uses. This will require the development of a knowledge management framework to provide syntax, context, and semantics, as well as exploring the introduction of time-varying data into the framework. Education and outreach will be part of the project through the development of an on-line short courses related to data integration in the area of geographical information systems. The grantees will be working with government partners (National Imagery and Mapping Agency, the National Agricultural Statistics Service, and the US Army Topographic Engineering Center), as well as an industrial organization, Base Systems, and the non-profit OpenGIS Consortium, which works closely with vendors of GIS products