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Home » Archives for Melinda Gervasio
Melinda Gervasio

Melinda Gervasio

Associate Technical Director, Artificial Intelligence Center
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Publications

Artificial intelligence publications November 1, 2022

Global and Local Analysis of Interestingness for Competency-Aware Deep Reinforcement Learning

Melinda Gervasio, Pedro Sequeira

Our new framework provides various measures of RL agent competence stemming from interestingness analysis and is applicable to a wide range of RL algorithms.

Artificial intelligence publications August 17, 2022

A Framework for understanding and Visualizing Strategies of RL Agents

Pedro Sequeira, Jesse Hostetler, Melinda Gervasio

We present a framework for learning comprehensible models of sequential decision tasks in which agent strategies are characterized using temporal logic formulas.

Artificial intelligence publications July 1, 2022

Outcome-Guided Counterfactuals for Reinforcement Learning Agents from a Jointly Trained Generative Latent Space

Eric Yeh, Pedro Sequeira, Jesse Hostetler, Melinda Gervasio

We present a novel generative method for producing unseen and plausible counterfactual examples for reinforcement learning (RL) agents based upon outcome variables that characterize agent behavior.

Machine learning publications April 1, 2021

Confidence Calibration for Domain Generalization under Covariate Shift

Yi Yao, Ajay Divakaran, Melinda Gervasio

We present novel calibration solutions via domain generalization. Our core idea is to leverage multiple calibration domains to reduce the effective distribution disparity between the target and calibration domains for improved calibration transfer without needing any data from the target domain.

Artificial intelligence publications November 1, 2020

Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents’ Capabilities and Limitations

Melinda Gervasio, Pedro Sequeira

We propose an explainable reinforcement learning (XRL) framework that analyzes an agent’s history of interaction with the environment to extract interestingness elements that explain its behavior.

Artificial intelligence publications August 1, 2020

Learning Procedures by Augmenting Sequential Pattern Mining with Planning Knowledge

Melinda Gervasio, Karen Myers

This paper explores the use of filtering heuristics based on action models for automated planning to augment sequence mining techniques.

Artificial intelligence publications August 3, 2019

Bridging the Gap: Converting Human Advice into Imagined Examples

Karen Myers, Melinda Gervasio, Eric Yeh

We present an approach that converts human advice into synthetic or imagined training experiences, serving to scaffold the low-level representations of simple, reactive learning systems such as reinforcement learners.

Artificial intelligence publications March 1, 2019

Interestingness Elements for Explainable Reinforcement Learning through Introspection

Pedro Sequeira, Eric Yeh, Melinda Gervasio

The framework uses introspective analysis of an agent’s history of interaction with its environment to extract several interestingness elements regarding its behavior.

Artificial intelligence publications March 1, 2018

Explanation to Avert Surprise

Melinda Gervasio, Karen Myers, Eric Yeh

We present an explanation framework based on the notion of explanation drivers —i.e., the intent or purpose behind agent explanations. We focus on explanations meant to reconcile expectation violations and enumerate a set of triggers for proactive explanation.

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