Abstracts of the invited talks presented at the AAAI Fall Symposium on Discovery Informatics: AI Takes a Science-Centered View on Big Data. Talks include  A Data Lifecycle Approach to Discovery Informatics,  Generating Biomedical Hypotheses Using Semantic Web Technologies,  Socially Intelligent Science, Representing and Reasoning with Experimental and Quasi-Experimental Designs, Bioinformatics Computation of Metabolic Models from Sequenced Genomes, Climate Informatics: Recent Advances and Challenge Problems for Machine Learning in Climate Science,  Predictive Modeling of Patient State and Therapy Optimization, Case Studies in Data-Driven Systems: Building Carbon Maps to Finding Neutrinos,  Computational Analysis of Complex Human Disorders, and Look at This Gem: Automated Data Prioritization for Scientific Discovery of Exoplanets, Mineral Deposits, and More.

It seemed miraculous at the time; a situated automaton that knew things without needing any models. However, I thought of it as (sensor-driven) feedback control, versus (plan driven, eyes shut) feed-forward control. I then used Mike Georgeff's procedural reasoning system (PRS) to make Flakey not only drive but navigate an office building. In some respects this project succeeded: the robot's "domain knowledge" was nothing more than a static connection graph--no distances to drive, no widths of halls or doorways, no a priori obstacles--such information was acquired en route from sensory input. In other respects, however, progress was unsatisfying.