REPRESENTING AND LEARNING PREFERENCES OVER COMBINATORIAL DOMAINS

Agents make decisions based on their preferences. Thus, to predict their decisions one has to learn the agent\u27s preferences. A key step in the learning process is selecting a model to represent those preferences. We studied this problem by borrowing techniques from the algorithm selection problem to analyze preference example sets and select the most appropriate preference representation for learning. We approached this problem in multiple steps. First, we determined which representations to consider. For this problem we developed the notion of preference representation language subsumption, which compares representations based on their expressive power. Subsumption creates a hierarchy of preference representations based solely on which preference orders they can express. By applying this analysis to preference representation languages over combinatorial domains we found that some languages are better for learning preference orders than others. Subsumption, however, does not tell the whole story. In the case of languages which approximate each other (another piece of useful information for learning) the subsumption relation cannot tell us which languages might serve as good approximations of others. How well one language approximates another often requires customized techniques. We developed such techniques for two important preference representation languages, conditional lexicographic preference models (CLPMs) and conditional preference networks (CP-nets). Second, we developed learning algorithms for highly expressive preference representations. To this end, we investigated using simulated annealing techniques to learn both ranking preference formulas (RPFs) and preference theories (PTs) preference programs. We demonstrated that simulated annealing is an effective approach to learn preferences under many different conditions. This suggested that more general learning strategies might lead to equally good or even better results. We studied this possibility by considering artificial neural networks (ANNs). Our research showed that ANNs can outperform classical models at deciding dominance, but have several significant drawbacks as preference reasoning models. Third, we developed a method for determining which representations match which example sets. For this classification task we considered two methods. In the first method we selected a series of features and used those features as input to a linear feed-forward ANN. The second method converts the example set into a graph and uses a graph convolutional neural network (GCNN). Between these two methods we found that the feature set approach works better. By completing these steps we have built the foundations of a portfolio based approach for learning preferences. We assembled a simple version of such a system as a proof of concept and tested its usefulness

Phenol Gasification in Supercritical Water: Chemistry, Byproducts, and Toxic Impacts.

In order to better understand the chemistry underlying supercritical water gasification (SCWG) of biomass, phenol was processed with supercritical water in quartz reactors while systematically varying the temperature, water density, reactant concentration, and reaction time. Both the gas and liquid phases were analyzed post-reaction to identify and quantify the reaction intermediates and products, including H2, CO, CH4, and CO2 in the gas phase and many different compounds—mainly polycyclic aromatic hydrocarbons (PAHs)—in the liquid phase. Higher temperatures promoted gasification and resulted in a product gas rich in H2 and CH4 (33% and 29%, respectively, at 700 °C), but char yields increased as well. Dibenzofuran and other identified phenolic dimers were implicated as precursor molecules for char formation pathways, which can be driven by free radical polymerization at high temperatures. Two different reaction pathways emerged from the kinetic modeling of phenol conversion: a water-inhibited thermal pathway in which rate ~ [phenol]^1.73 [water]^-16.60 and a water-accelerated hydrothermal pathway in which rate ~ [phenol]^0.92 [water]^1.39. Benzene and dibenzofuran form directly from phenol and account for nearly all phenol consumption during SCWG at 500–700 °C. Experiments with dibenzofuran as the starting reactant generated the same array of products—typically in comparable quantities—as that observed with phenol as the reactant. When benzene was the reactant, biphenyl was the main product and some H2 formed. Information about the reaction pathways obtained from these experiments served as the basis for constructing and optimizing a kinetic model that describes the reaction rates of phenol and its primary and gaseous products in supercritical water. Arrhenius parameters are reported, and the formation and consumption rates for each species as calculated by the model are analyzed. Since many of the identified PAHs are EPA priority pollutants and have known human health and environmental effects, the UNEP/SETAC toxicity model, “USEtox,” was employed to characterize the human toxic and ecotoxic impacts due to a hypothetical emission of this byproduct stream into freshwater. Total toxic impact increased with gasification temperature up to a maximum at 650 °C but then decreased at 700 °C.PHDChemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107077/1/huelsman_1.pd

Interventions for Child and Adolescent Depression: Do Professional Therapists Produce Better Results?

We reviewed and analyzed child and adolescent depression treatment studies (1980–2001) through a comprehensive literature search. The outcome data from 19 studies (31 treatments) were extracted and weighted standard mean effect sizes were computed. Outcomes were compared across two levels of therapist training: professional and graduate student. Moreover, age was examined to test for differential effects on treatment outcome. Overall, professionals and graduate student therapists produced impressive yet commensurate outcomes when treating depressed youth. There were no significant differences found when treating children versus adolescents. The implications and limitations are reviewed, as are the suggestions for future research