Virtual Ecological Research Assistant (VERA)

Ashok Goel, Robert Bates, Spencer Rugaber
Akshay Agarwal, Christopher Cassion, Taylor Hartman, Animesh Mehta, Abbinayaa Subrahmanian

Protecting the environment is among the biggest challenges facing our society. Big data is an essential element of addressing this big challenge. Encyclopedia of Life (EOL) is the world's largest database of biological species and other biodiversity information. EOL also works closely with scores of other biodiversity datasets such as BISON, GBIF, and OBIS. We seek to make EOL and related biodiversity data sources accessible, usable, and useful, by integrating extant AI tools for information extraction, modeling and simulation, and question answering; we call the resulting system EOL+. The focus of this project is on the data engineering required for constructing EOL+ and on building a user community around EOL+. Professional and citizen scientists, and teachers and students alike, will be able to access EOL+ through the South BD Hub's web portal, and use it for modeling and analysis, explanation and prediction, as well as education and workforce development in biological diversity, ecological modeling, and environmental sustainability.
MILA-S is an interactive tool developed at Georgia Tech that enables interactive construction of conceptual models of ecological phenomena and automatically spawns simulation models from the conceptual models. We will integrate MILA-S with EOL, enabling professional and citizen scientists to generate and test explanatory hypotheses as well as make predictions about ecosystems. Watson is IBM's cognitive system for question answering. At Georgia Tech, we have added semantic processing to Watson so that the resulting Watson+ system acts as a virtual research assistant. We will train Watson+ for answering questions about biological species using EOL as the data source.

Ashok Goel
Ashok Goel, Keith McGreggor, Spencer Rugaber
Tesca Fitzgerald, David Joyner, Rochelle Lobo, Bryan Wiltgen, Gongbo Zhang

The Design & Intelligence Laboratory conducts research into human-centered artificial intelligence and computational cognitive science, with a focus on computational creativity. Current projects explore analogical reasoning in biologically inspired design, visual reasoning on intelligence tests, meta-reasoning in game-playing software agents, and learning about ecological and biological systems in science education.