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.

Reinforcement learning (RL) is a particular machine learning technique enabling an agent to learn while interacting with its environment. Agents in non-stationary environments are faced with the additional problem of handling concept drift, which is a partially-observable change that modifies the environment without notification. This causes several problems: agents with a decaying exploration fail to adapt while agents capable of adapting may over fit to noise and overwrites previously learned knowledge. These issues are known as the plasticity-stability dilemma and catastrophic forgetting, respectively. Agents in such environments must take steps to mitigate both problems. This work contributes an algorithm that combines a concept drift classifier with multi-objective reinforcement learning (MORL) to produce an unsupervised technique for learning in non-stationary environments, especially in the face of partially observable changes. The algorithm manages the plasticity-stability dilemma by strategically adjusting learning rates and mitigates catastrophic forgetting by systematically storing knowledge and recalling it when it recognizes repeat situations. Results demonstrate that agents using this algorithm outperform agents using an approach that ignores non-stationarity.

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.

Highly capable multiple robot architectures often resort to micromanagement to provide enhanced cooperative abilities, sacrificing individual autonomy. Conversely, multi-robot architectures that maintain individual autonomy are often limited in their cooperative abilities.  This article presents a modified three layer architecture that solves both of these issues.  The addition of a Coordinator layer to a three-layered approach provides a platform-independent interface for coordination on tasks and takes advantage of individual autonomy to improve coordination capabilities.  This reduces communication overhead versus many multi-robot architecture designs and allows for more straightforward resizing of the robot collective and increased individual autonomy.