A foundation for machine learning in design

This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, "What types of knowledge can be learnt?", "How does learning occur?", and "When does learning occur?". Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD

Participatory design of a continuous care ontology : towards a user-driven ontology engineering methodology

The patient room of the future would be able to sense the needs and preferences of the patients and nurses and adapt itself accordingly by combining all the heterogeneous data offered by the different technologies. This goal can be achieved by developing a context-aware framework, which exploits and integrates the heterogeneous data by utilizing a continuous care ontology. The existing ontology engineering methodologies are rather extreme in their choices to include domain experts. On the one hand, there are methodologies that only discuss the scope, use and requirements of the ontology with the domain experts. On the other hand, there are approaches in which the ontology is completely constructed by the domain experts by providing them with user-friendly and collaborative tools. In this paper, a participatory ontology engineering methodology is presented that finds a middle ground between these two extremes. The methodology actively involves social scientists, ontology engineers and stakeholders. The stakeholders participate in each step of the ontology life cycle without having to construct the ontology themselves or attribute a large amount of their time. The applicability of the methodology is illustrated by presenting the co-created continuous care ontology

Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information

Space exploration: The interstellar goal and Titan demonstration

Automated interstellar space exploration is reviewed. The Titan demonstration mission is discussed. Remote sensing and automated modeling are considered. Nuclear electric propulsion, main orbiting spacecraft, lander/rover, subsatellites, atmospheric probes, powered air vehicles, and a surface science network comprise mission component concepts. Machine, intelligence in space exploration is discussed

The Crop-Group and the inconsistent use of Linnean names in the taxonomy of domesticated plants

There have been several proposals for classification categories for systematic groups of domesticated plants. In the 6th edition of the International Code for Nomenclature of Cultivated Plants (ICNCP) only two main categories were included, the cultivar and the cultivar-group. The 7th edition of ICNCP saw the introduction of the Group to encompass the cultivar-group together with other kinds of groupings, also of unnamed material. Despite the existence of the ICNCP, many names for systematic groups of domesticated plants are still in purely Linnean form, following the rules of the International Code of Botanical Nomenclature (ICBN). This practice illustrates a lack of insight in the workings and logic of systematic thinking with respect to domesticated plants and muddles the borderline between the contexts of domestication and evolution. The inclusion of the Crop category in the ICNCP would accommodate the nomenclature and classification of all systematic groups of domesticated plants in one logically consistent system, setting it apart from the realm of the classical botanical classification in use for wild plants

A cognitive architecture for learning in reactive environments

Previous research in machine learning has viewed the process of empirical discovery as search through a space of 'theoretical' terms. In this paper, we propose a problem space for empirical discovery, specifying six complementary operators for defining new terms that ease the statement of empirical laws. The six types of terms include: numeric attributes (such as PV/T); intrinsic properties (such as mass); composite objects (such as pairs of colliding balls); classes of objects (such as acids and alkalis); composite relations (such as chemical reactions); and classes of relations (such as combustion/oxidation). We review existing machine discovery systems in light of this framework, examining which parts of the problem space were, covered by these systems. Finally, we outline an integrated discovery system (IDS) we are constructing that includes all six of the operators and which should be able to discover a broad range of empirical laws

A framework for empirical discovery

Previous research in machine learning has viewed the process of empirical discovery as search through a space of 'theoretical' terms. In this paper, we propose a problem space for empirical discovery, specifying six complementary operators for defining new terms that ease the statement of empirical laws. The six types of terms include: numeric attributes (such as PV/T); intrinsic properties (such as mass); composite objects (such as pairs of colliding balls); classes of objects (such as acids and alkalis); composite relations (such as chemical reactions); and classes of relations (such as combustion/oxidation). We review existing machine discovery systems in light of this framework, examining which parts of the problem space were, covered by these systems. Finally, we outline an integrated discovery system (IDS) we are constructing that includes all six of the operators and which should be able to discover a broad range of empirical laws