Research Directions in Interactive AI

Contents

The MAPLE (Multi-Agent Planning and LEarning) research group at UMBC has several ongoing projects that emphasize user interaction with AI methods. This position paper briefly summarizes these projects: (1) representing and incorporating a user’s qualitative domain knowledge into machine learning algorithms; (2) creating understandable visual representations of high-dimensional probabilistic domain models (Bayesian networks); (3) developing cost-sensitive constraint satisfaction methods that allow the system to reason about the cognitive costs of involving a user in checking constraints; and (4) integrating automated planning and constraint satisfaction methods with interactive plan editing techniques to permit mixed-initiative plan generation in complex, real-world domains.