Melinda Gervasio Wayne Iba Pat Langley Stephanie Sage
Institute for the Study of Learning and Expertise
2164 Staunton Court, Palo Alto, California 94306
{gervasio, iba, langley, sage}@isle.org
Crisis domains present the challenge of developing good responses in a
timely manner. In this paper, we present an interactive, case-based
approach to crisis response that provides users with the ability to
rapidly develop good responses while leaving ultimate decision-making
control to the users. We introduce INCA, the INteractive Crisis
Assistant we have implemented for planning and scheduling in crisis
domains. We also present HAZMAT, the artificial domain involving
hazardous material incidents that we developed for the purpose of
evaluating different responses and various assistant mechanisms. We
then discuss two preliminary studies that we conducted to evaluate
scheduling assistance in INCA. Results from the first set of
experiments indicate that INCA's case-based scheduling assistance
provides users with initial candidate solutions that enable users to
develop high quality responses more quickly. The second set of
experiments demonstrates the potential of machine learning methods to
further facilitate interactive scheduling by accurately predicting
preferred user adaptations. Based on these encouraging results, we
close with directions for future work and a brief discussion of
related research.