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Home » Archives for Pedro Sequeira
Pedro Sequeira

Pedro Sequeira

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Publications

Artificial intelligence publications February 20, 2023

Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoning

Pedro Sequeira

Abstract We approach the problem of understanding how people interact with each other in collaborative settings, especially when individuals know little about their teammates, via Multiagent Inverse Reinforcement Learning (MIRL), where the goal is to infer the reward functions guiding the behavior of each individual given trajectories of a team’s behavior during some task. Unlike […]

Artificial intelligence publications November 3, 2022

Sensor Control for Information Gain in Dynamic, Sparse and Partially Observed Environments

Aravind Sundaresan, Pedro Sequeira, Vidyasagar Sadhu

We present an approach for autonomous sensor control for information gathering under partially observable, dynamic and sparsely sampled environments.

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.

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 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.

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