Melinda Gervasio

AIJ Journal 2020 Journal Article

Interestingness elements for explainable reinforcement learning: Understanding agents' capabilities and limitations

• Pedro Sequeira
• Melinda Gervasio

We propose an explainable reinforcement learning (XRL) framework that analyzes an agent's history of interaction with the environment to extract interestingness elements that help explain its behavior. The framework relies on data readily available from standard RL algorithms, augmented with data that can easily be collected by the agent while learning. We describe how to create visual summaries of an agent's behavior in the form of short video-clips highlighting key interaction moments, based on the proposed elements. We also report on a user study where we evaluated the ability of humans to correctly perceive the aptitude of agents with different characteristics, including their capabilities and limitations, given visual summaries automatically generated by our framework. The results show that the diversity of aspects captured by the different interestingness elements is crucial to help humans correctly understand an agent's strengths and limitations in performing a task, and determine when it might need adjustments to improve its performance.

TIST Journal 2011 Journal Article

PTIME

• Pauline M. Berry
• Melinda Gervasio
• Bart Peintner
• Neil Yorke-Smith

In a world of electronic calendars, the prospect of intelligent, personalized time management assistance seems a plausible and desirable application of AI. PTIME ( Personalized Time Management ) is a learning cognitive assistant agent that helps users handle email meeting requests, reserve venues, and schedule events. PTIME is designed to unobtrusively learn scheduling preferences, adapting to its user over time. The agent allows its user to flexibly express requirements for new meetings, as they would to an assistant. It interfaces with commercial enterprise calendaring platforms, and it operates seamlessly with users who do not have PTIME. This article overviews the system design and describes the models and technical advances required to satisfy the competing needs of preference modeling and elicitation, constraint reasoning, and machine learning. We further report on a multifaceted evaluation of the perceived usefulness of the system.

AAMAS Conference 2006 Conference Paper

Deploying a Personalized Time Management Agent

• Pauline Berry
• Ken Conley
• Melinda Gervasio
• Bart Peintner
• Tomas Uribe
• Neil Yorke-Smith