@article{kocher_ayroles_stone_grozinger_2010, title={Individual variation in pheromone response correlates with reproductive traits and brain gene expression in worker honey bees}, volume={5}, number={2}, journal={PLoS One}, author={Kocher, S. D. and Ayroles, J. F. and Stone, E. A. and Grozinger, C. M.}, year={2010} }
 @article{bahler_stone_2000, title={Symbolic, neural, and Bayesian machine learning models for predicting carcinogenicity of chemical compounds}, volume={40}, ISSN={["0095-2338"]}, DOI={10.1021/ci990116i}, abstractNote={Experimental programs have been underway for several years to determine the environmental effects of chemical compounds, mixtures, and the like. Among these programs is the National Toxicology Program (NTP) on rodent carcinogenicity. Because these experiments are costly and time-consuming, the rate at which test articles (i.e., chemicals) can be tested is limited. The ability to predict the outcome of the analysis at various points in the process would facilitate informed decisions about the allocation of testing resources. To assist human experts in organizing an empirical testing regime, and to try to shed light on mechanisms of toxicity, we constructed toxicity models using various machine learning and data mining methods, both existing and those of our own devising. These models took the form of decision trees, rule sets, neural networks, rules extracted from trained neural networks, and Bayesian classifiers. As a training set, we used recent results from rodent carcinogenicity bioassays conducted by the NTP on 226 test articles. We performed 10-way cross-validation on each of our models to approximate their expected error rates on unseen data. The data set consists of physical-chemical parameters of test articles, alerting chemical substructures, salmonella mutagenicity assay results, subchronic histopathology data, and information on route, strain, and sex/species for 744 individual experiments. These results contribute to the ongoing process of evaluating and interpreting the data collected from chemical toxicity studies.}, number={4}, journal={JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES}, author={Bahler, D and Stone, B}, year={2000}, pages={906–914} }
 @article{lester_stone_stelling_1999, title={Lifelike pedagogical agents for mixed-initiative problem solving in constructivist learning environments}, volume={9}, ISSN={["1573-1391"]}, DOI={10.1023/A:1008374607830}, number={1-2}, journal={USER MODELING AND USER-ADAPTED INTERACTION}, author={Lester, JC and Stone, BA and Stelling, GD}, year={1999}, pages={1–44} }
 @inproceedings{lester_fitzgerald_stone_1997, title={The pedagogical design studio: Exploiting artifact-based task models for constructivist learning}, DOI={10.1145/238218.238317}, abstractNote={Intelligent learning environments that support constructivism should provide active learning experiences that are customized for individual learners. To do so, they must determine learner intent and detect misconceptions, and this diagnosis must be performed as non-invasively as possible. To this end, we propose the pedagogical design studio, a design-centered framework for learning environment interfaces. Pedagogical design studios provide learners with a rich, direct manipulation design experience. By exploiting an artifact-based task model that preserves a tight mapping between the interface state and design sub-tasks, they non-invasively infer learners’ intent and detect misconceptions. The task model is then used to tailor problem presentation, produce a customized musical score, and modulate problem-solving intervention. To explore these notions, we have implemented a pedagogical design studio for a constructivist learning environment that provides instruction to middle school students about botanical anatomy and physiology. Evaluations suggest that the design studio framework constitutes an effective approach to interfaces that support constructivist learning.}, booktitle={IUI97: 1997 International Conference on Intelligent User Interfaces, January 6-9, 1997, Orlando, Florida, USA}, publisher={New York: Association for Computing Machinery}, author={Lester, J. C. and Fitzgerald, P. J. and Stone, B. A.}, editor={J. Moore, E. Ernest and Puerta, A.Editors}, year={1997}, pages={155–162} }