An increasingly important job for the autonomous agents is determining what goal they should be accomplishing. In dynamic environments the goal of the autonomous agents does not always remain constant. This research examines how to detect and adapt to goal changes within a dynamic game environment. An adaptive learner capable of detecting concept drift is used to detect when a goal change has occurred within the game environment and exploration techniques are used to adapt to the change. Initial results show that the agent has an 84% detection rate.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of six AI assignments from the 2017 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs.