Improving performance through concept formation and conceptual clustering

Research from June 1989 through October 1992 focussed on concept formation, clustering, and supervised learning for purposes of improving the efficiency of problem-solving, planning, and diagnosis. These projects resulted in two dissertations on clustering, explanation-based learning, and means-ends planning, and publications in conferences and workshops, several book chapters, and journals; a complete Bibliography of NASA Ames supported publications is included. The following topics are studied: clustering of explanations and problem-solving experiences; clustering and means-end planning; and diagnosis of space shuttle and space station operating modes

Models of incremental concept formation

Given a set of observations, humans acquire concepts that organize those observations and use them in classifying future experiences. This type of concept formation can occur in the absence of a tutor and it can take place despite irrelevant and incomplete information. A reasonable model of such human concept learning should be both incremental and capable of handling this type of complex experiences that people encounter in the real world. In this paper, we review three previous models of incremental concept formation and then present CLASSIT, a model that extends these earlier systems. All of the models integrate the process of recognition and learning, and all can be viewed as carrying out search through the space of possible concept hierarchies. In an attempt to show that CLASSIT is a robust concept formation system, we also present some empirical studies of its behavior under a variety of conditions

Machine learning techniques for fault isolation and sensor placement

Fault isolation and sensor placement are vital for monitoring and diagnosis. A sensor conveys information about a system's state that guides troubleshooting if problems arise. We are using machine learning methods to uncover behavioral patterns over snapshots of system simulations that will aid fault isolation and sensor placement, with an eye towards minimality, fault coverage, and noise tolerance

Discovering qualitative empirical laws

In this paper we describe GLAUBER, an AI system that models the scientific discovery of qualitative empirical laws. We have tested the system on data from the history of early chemistry, and it has rediscovered such concepts as acids, alkalis, and salts, as well as laws relating these concepts. After discussing GLAUBER we examine the program's relation to other discovery systems, particularly methods for conceptual clustering and language acquisition

Casimir interactions in Ising strips with boundary fields: exact results

An exact statistical mechanical derivation is given of the critical Casimir forces for Ising strips with arbitrary surface fields applied to edges. Our results show that the strength as well as the sign of the force can be controled by varying the temperature or the fields. An interpretation of the results is given in terms of a linked cluster expansion. This suggests a systematic approach for deriving the critical Casimir force which can be used in more general models.Comment: 10 pages, 4 figure

Self-management support for chronic disease in primary care: frequency of patient self-management problems and patient reported priorities, and alignment with ultimate behavior goal selection.

BackgroundTo enable delivery of high quality patient-centered care, as well as to allow primary care health systems to allocate appropriate resources that align with patients' identified self-management problems (SM-Problems) and priorities (SM-Priorities), a practical, systematic method for assessing self-management needs and priorities is needed. In the current report, we present patient reported data generated from Connection to Health (CTH), to identify the frequency of patients' reported SM-Problems and SM-Priorities; and examine the degree of alignment between patient SM-Priorities and the ultimate Patient-Healthcare team member selected Behavioral Goal.MethodsCTH, an electronic self-management support system, was embedded into the flow of existing primary care visits in 25 primary care clinics and was used to assess patient-reported SM-Problems across 12 areas, patient identified SM-Priorities, and guide the selection of a Patient-Healthcare team member selected Behavioral Goal. SM-Problems included: BMI, diet (fruits and vegetables, salt, fat, sugar sweetened beverages), physical activity, missed medications, tobacco and alcohol use, health-related distress, general life stress, and depression symptoms. Descriptive analyses documented SM-Problems and SM-Priorities, and alignment between SM-Priorities and Goal Selection, followed by mixed models adjusting for clinic.Results446 participants with ≥ one chronic diseases (mean age 55.4 ± 12.6; 58.5% female) participated. On average, participants reported experiencing challenges in 7 out of the 12 SM-Problems areas; with the most frequent problems including: BMI, aspects of diet, and physical activity. Patient SM-Priorities were variable across the self-management areas. Patient- Healthcare team member Goal selection aligned well with patient SM-Priorities when patients prioritized weight loss or physical activity, but not in other self-management areas.ConclusionParticipants reported experiencing multiple SM-Problems. While patients show great variability in their SM-Priorities, the resulting action plan goals that patients create with their healthcare team member show a lack of diversity, with a disproportionate focus on weight loss and physical activity with missed opportunities for using goal setting to create targeted patient-centered plans focused in other SM-Priority areas. Aggregated results can assist with the identification of high frequency patient SM-Problems and SM-Priority areas, and in turn inform resource allocation to meet patient needs.Trial registrationClinicalTrials.gov ID: NCT01945918

Invisible water, visible impact: How unsustainable groundwater use challenges sustainability of Indian agriculture under climate change

India is one of the world’s largest food producers, making the sustainability of its agricultural system of global significance. Groundwater irrigation underpins India’s agriculture, currently boosting crop production by enough to feed 170 million people. Groundwater overexploitation has led to drastic declines in groundwater levels, threatening to push this vital resource out of reach for millions of small-scale farmers who are the backbone of India’s food security. Historically, losing access to groundwater has decreased agricultural production and increased poverty. We take a multidisciplinary approach to assess climate change challenges facing India’s agricultural system, and to assess the effectiveness of large-scale water infrastructure projects designed to meet these challenges. We find that even in areas that experience climate change induced precipitation increases, expansion of irrigated agriculture will require increasing amounts of unsustainable groundwater. The large proposed national river linking project has limited capacity to alleviate groundwater stress. Thus, without intervention, poverty and food insecurity in rural India is likely to worsen

Data Mining for Gene Networks Relevant to Poor Prognosis in Lung Cancer Via Backward-Chaining Rule Induction

We use Backward Chaining Rule Induction (BCRI), a novel data mining method for hypothesizing causative mechanisms, to mine lung cancer gene expression array data for mechanisms that could impact survival. Initially, a supervised learning system is used to generate a prediction model in the form of “IF <conditions> THEN <outcome>” style rules. Next, each antecedent (i.e. an IF condition) of a previously discovered rule becomes the outcome class for subsequent application of supervised rule induction. This step is repeated until a termination condition is satisfied. “Chains” of rules are created by working backward from an initial condition (e.g. survival status). Through this iterative process of “backward chaining,” BCRI searches for rules that describe plausible gene interactions for subsequent validation. Thus, BCRI is a semi-supervised approach that constrains the search through the vast space of plausible causal mechanisms by using a top-level outcome to kick-start the process. We demonstrate the general BCRI task sequence, how to implement it, the validation process, and how BCRI-rules discovered from lung cancer microarray data can be combined with prior knowledge to generate hypotheses about functional genomics

Effect of eicosapentaenoic and docosahexaenoic acid on resting and exercise-induced inflammatory and oxidative stress biomarkers: a randomized, placebo controlled, cross-over study

<p>Abstract</p> <p>Background</p> <p>The purpose of the present investigation was to determine the effects of EPA/DHA supplementation on resting and exercise-induced inflammation and oxidative stress in exercise-trained men. Fourteen men supplemented with 2224 mg EPA+2208 mg DHA and a placebo for 6 weeks in a random order, double blind cross-over design (with an 8 week washout) prior to performing a 60 minute treadmill climb using a weighted pack. Blood was collected pre and post exercise and analyzed for a variety of oxidative stress and inflammatory biomarkers. Blood lactate, muscle soreness, and creatine kinase activity were also measured.</p> <p>Results</p> <p>Treatment with EPA/DHA resulted in a significant increase in blood levels of both EPA (18 ± 2 μmol·L^-1vs. 143 ± 23 μmol·L^-1; p < 0.0001) and DHA (67 ± 4 μmol·L^-1vs. 157 ± 13 μmol·L^-1; p < 0.0001), while no differences were noted for placebo. Resting levels of CRP and TNF-α were lower with EPA/DHA compared to placebo (p < 0.05). Resting oxidative stress markers were not different (p > 0.05). There was a mild increase in oxidative stress in response to exercise (XO and H₂O₂) (p < 0.05). No interaction effects were noted. However, a condition effect was noted for CRP and TNF-α, with lower values with the EPA/DHA condition.</p> <p>Conclusion</p> <p>EPA/DHA supplementation increases blood levels of these fatty acids and results in decreased resting levels of inflammatory biomarkers in exercise-trained men, but does not appear necessary for exercise-induced attenuation in either inflammation or oxidative stress. This may be due to the finding that trained men exhibit a minimal increase in both inflammation and oxidative stress in response to moderate duration (60 minute) aerobic exercise.</p

Fluxes and fate of dissolved methane released at the seafloor at the landward limit of the gas hydrate stability zone offshore western Svalbard

Widespread seepage of methane from seafloor sediments offshore Svalbard close to the landward limit of the gas hydrate stability zone (GHSZ) may, in part, be driven by hydrate destabilization due to bottom water warming. To assess whether this methane reaches the atmosphere where it may contribute to further warming, we have undertaken comprehensive surveys of methane in seawater and air on the upper slope and shelf region. Near the GHSZ limit at ?400 m water depth, methane concentrations are highest close to the seabed, reaching 825 nM. A simple box model of dissolved methane removal from bottom waters by horizontal and vertical mixing and microbially mediated oxidation indicates that ?60% of methane released at the seafloor is oxidized at depth before it mixes with overlying surface waters. Deep waters are therefore not a significant source of methane to intermediate and surface waters; rather, relatively high methane concentrations in these waters (up to 50 nM) are attributed to isopycnal turbulent mixing with shelf waters. On the shelf, extensive seafloor seepage at &lt;100 m water depth produces methane concentrations of up to 615 nM. The diffusive flux of methane from sea to air in the vicinity of the landward limit of the GHSZ is ?4–20 ?mol m?2 d?1, which is small relative to other Arctic sources. In support of this, analyses of mole fractions and the carbon isotope signature of atmospheric methane above the seeps do not indicate a significant local contribution from the seafloor source