Clark Glymour

The ability to identify the mineral composition of rocks and soils is an important tool for the exploration of geological sites. Even though expert knowledge is commonly used for this task, it is desirable to create automated systems with similar or better performance. For instance, NASA intends to design robots that are sufficiently autonomous to perform this task on planetary missions. Spectrometer readings provide one important source of data for identifying sites with minerals of interest. Reflectance spectrometers measure intensities of light reflected from surfaces over a range of wavelengths. Spectral intensity patterns may in some cases be sufficiently distinctive for proper identification of minerals or classes of minerals. For some mineral classes, carbonates for example, specific short spectral intervals are known to carry a distinctive signature. Finding similar distinctive spectral ranges for other mineral classes is not an easy problem. We propose and evaluate data-driven techniques in two stages: first, evaluating algorithms to identify which components are probably present in a given rock; second, trying to improve this classification by automatically searching for spectral ranges optimized for specific classes of minerals. In one set of studies, we partition the whole interval of wavelengths available in our data into sub-intervals, or bins, and use a genetic algorithm to evaluate a candidate selection of subintervals. As an alternative to these computationally expensive search techniques, we present an entropy-based heuristic that gives higher scores for wavelengths more likely to distinguish between classes. Results are presented for four different classes, showing reasonable improvements in identifying some, but not all, of the mineral classes tested.

Contemporary cognitive neuropsychology attempts to infer unobserved features of normal human cognition, or ‘cognitive architecture’, from experiments with normals and with brain-damaged subjects in whom certain normal cognitive capacities are altered, diminished, or absent. Fundamental methodological issues about the enterprise of cognitive neuropsychology concern the characterization of methods by which features of normal cognitive architecture can be identified from such data, the assumptions upon which the reliability of such methods are premised, and the limits of such methods—even granting their assumptions—in resolving uncertainties about that architecture. With some idealization, the question of the capacities of various experimental designs in cognitive neuropsychology to uncover cognitive architecture can be reduced to comparatively simple questions about the prior assumptions investigators are willing to make. This paper presents some of simplest of those reductions.

Words, Thoughts and Theories arguesthat infants and children discover the physical and psychological featuresof the world by a process akin to scientific inquiry, more or less asconceived by philosophers of science in the 1960s (the theory theory).This essay discusses some of the philosophical background to analternative, more popular, ``modular'''' or ``maturational'''' account ofdevelopment, dismisses an array of philosophical objections to the theorytheory, suggests that the theory theory offers an undeveloped project forartificial intelligence, and, relying on recent psychological work oncausation, offers suggestions about how principles of causal inference mayprovide a developmental solution to the ``frame problem''''.

Convergent realists desire scientific methods that converge reliably to informative, true theories over a wide range of theoretical possibilities. Much attention has been paid to the problem of induction from quantifier-free data. In this paper, we employ the techniques of formal learning theory and model theory to explore the reliable inference of theories from data containing alternating quantifiers. We obtain a hierarchy of inductive problems depending on the quantifier prefix complexity of the formulas that constitute the data, and we provide bounds relating the quantifier prefix complexity of the data to the quantifier prefix complexity of the theories that can be reliably inferred from such data without background knowledge. We also examine the question whether there are theories with mixed quantifiers that can be reliably inferred with closed, universal formulas in the data, but not without