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

Non-real zeros of linear differential polynomials in real meromorphic functions

It is shown that if $f$ or $1/f$ is a real entire function of infinite order of growth, with only real zeros, then $f''+\omega f$ has infinitely many non-real zeros for any $\omega > 0$.Comment: updated 27/09/1

The Apollo 14 docking anomaly

Six docking attempts were required to achieve initial latch engagement during the Apollo 14 translunar docking event. Although subsequent performance of the docking hardware was normal, the docking probe was retained for a thorough postflight investigation. Pertinent design details of the docking system, the mission events related to the anomaly, and a discussion of the postflight investigation of the cause of the anomaly are presented

Approaches to conceptual clustering

Methods for Conceptual Clustering may be explicated in two lights. Conceptual Clustering methods may be viewed as extensions to techniques of numerical taxonomy, a collection of methods developed by social and natural scientists for creating classification schemes over object sets. Alternatively, conceptual clustering may be viewed as a form of learning by observation or concept formation, as opposed to methods of learning from examples or concept identification. In this paper we survey and compare a number of conceptual clustering methods along dimensions suggested by each of these views. The point we most wish to clarify is that conceptual clustering processes can be explicated as being composed of three distinct but inter-dependent subprocesses: the process of deriving a hierarchical classification scheme; the process of aggregating objects into individual classes; and the process of assigning conceptual descriptions to object classes. Each subprocess may be characterized along a number of dimensions related to search, thus facilitating a better understanding of the conceptual clustering process as a whole

Hispanic Victims of Lethal Firearms Violence in the United States (2015)

During the period 2000 to 2013, the overall U.S. Hispanic population grew 53.3 percent. This study is intended to report the latest national information available at the time of writing on Hispanic homicide victimization and suicide in the United States, the role of firearms in homicide and suicide, and overall gun death figures. Recognizing this demographic landscape, the importance of documenting such victimization is clear