Pedro Sequeira

Pedro Sequeira
SRI International | SRI · Center for Artificial Intelligence

PhD | Artificial Intelligence

About

38
Publications
8,582
Reads
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519
Citations
Citations since 2016
17 Research Items
377 Citations
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2016201720182019202020212022020406080
Introduction
I am an Associate Research Scientist at Northeastern University, working on modeling human decision-making. I created a technique for automated cognitive behavior analysis (ACBA) aimed at discovering the underlying structures of human decision-making by using genetic programming. My research interests are mainly in the area of artificial intelligence (AI), involving the creation of autonomous and adaptive systems acting under uncertainty. This has involved the development of novel machine learning (ML) techniques and evolutionary mechanisms. My approach involves creating ML systems inspired by how humans learn and make decisions, and use ML techniques to discover how humans learn and make decisions in complex tasks.
Additional affiliations
October 2016 - present
Northeastern University
Position
  • Lecturer
October 2016 - present
Northeastern University
Position
  • Research Associate
March 2016 - July 2016
University of Lisbon
Position
  • Professor (Assistant)
Education
October 2008 - October 2013
University of Lisbon
Field of study
  • Information Systems and Computer Engineering
October 2001 - July 2006
University of Lisbon
Field of study
  • Information Systems and Computer Engineering

Publications

Publications (38)
Preprint
Full-text available
In recent years, advances in deep learning have resulted in a plethora of successes in the use of reinforcement learning (RL) to solve complex sequential decision tasks with high-dimensional inputs. However, existing systems lack the necessary mechanisms to provide humans with a holistic view of their competence, presenting an impediment to their a...
Preprint
Full-text available
Recent years have seen significant advances in explainable AI as the need to understand deep learning models has gained importance with the increased emphasis on trust and ethics in AI. Comprehensible models for sequential decision tasks are a particular challenge as they require understanding not only individual predictions but a series of predict...
Preprint
Full-text available
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. Our approach uses a variational autoencoder to train a latent space that jointly encodes information about the observations and outcome variables pertai...
Article
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 lear...
Article
Full-text available
The United States has experienced prolonged severe shortages of vital medications over the past two decades. The causes underlying the severity and prolongation of these shortages are complex, in part due to the complexity of the underlying supply chain networks, which involve supplier-buyer interactions across multiple entities with competitive an...
Preprint
Full-text available
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 lear...
Article
This paper describes the INSIDE system, a networked robot system designed to allow the use of mobile robots as active players in the therapy of children with autism spectrum disorders (ASD). While a significant volume of work has explored the impact of robots in ASD therapy, most such work comprises remotely operated robots and/or well-structured i...
Article
Full-text available
This work explores a group learning scenario with an autonomous empathic robot. We address two research questions: (1) Can an autonomous robot designed with empathic competencies foster collaborative learning in a group context? (2) Can an empathic robot sustain positive educational outcomes in long-term collaborative learning interactions with gro...
Preprint
Full-text available
This work explores a group learning scenario with an autonomous empathic robot. We address two research questions: (1) Can an autonomous robot designed with empathic competencies foster collaborative learning in a group context? (2) Can an empathic robot sustain positive educational outcomes in long-term collaborative learning interactions with gro...
Conference Paper
Full-text available
The growing epidemic of drug shortages in the United States causes challenges for providers all across thecritical health care infrastructure and demonstrates the lack of resiliency within drug delivery supply chains.With many drugs having no acceptable substitute, drug shortages directly translate to a public health andsafety risk. One of the unde...
Article
Full-text available
In this paper we present the interactive consoles deployed on an assistive scenario with children diagnosed with Autism Spectrum Disorders (ASD). These interfaces were developed in order to provide support to the therapists during the therapy sessions in a hospital allowing the operators to better analyze the procedure from outside and intervene wh...
Conference Paper
Full-text available
The positive impact of emotions in decision-making has long been established in both natural and artificial agents. In the perspective of appraisal theories, emotions complement perceptual information, coloring our sensations and guiding our decision-making. However, when designing autonomous agents, is emotional appraisal the best complement to th...
Conference Paper
Full-text available
Human beings naturally assign roles to one another while interacting. Role assignment is a way to organize interpersonal encounters and can result in uncertainty decrease when facing a novel interaction with someone we just met, or even to rediscover new roles within previous relationships. When people interact with synthetic characters – such as r...
Conference Paper
Full-text available
Research in education has long established how children mutually influence and support each other's learning trajectories, eventually leading to the development and widespread use of learning methods based on peer activities. In order to explore children's learning behavior in the presence of a robotic facilitator during a collaborative writing act...
Technical Report
Full-text available
Emotions are a key element in all human interactions. It is well documented that individual-and group-level interactions have different emotional expressions and humans are by nature extremely competent in perceiving, adapting and reacting to them. However, when developing social robots, emotions are not so easy to cope with. In this paper we intro...
Conference Paper
Full-text available
In order to explore the impact of integrating a robot as a facilitator in a collaborative activity, we examined interpersonal distancing of children both with a human adult and a robot facilitator. Our scenario involves two children performing a collaborative learning activity, which included the writing of a word/letter on a tactile tablet. Based...
Conference Paper
Full-text available
Several agent-based frameworks have been proposed to investigate the possible reasons that lead humans to act in the interest of others while giving up individual gains. In this paper we propose a novel framework for analyzing this phenomenon based on the notions of social importance (SI) and local discrimination. We analyze such mechanism in the c...
Conference Paper
Full-text available
We present an autonomous empathic robotic tutor to be used in classrooms as a peer in a virtual learning environment. The system merges a virtual agent design with HRI features, consisting of a robotic embodiment, a multimedia interactive learning application and perception sensors that are controlled by an artificial intelligence agent.
Article
Full-text available
Several agent-based frameworks have been proposed to investigate the possible reasons that lead humans to act in the interest of others while giving up individual gains. In this paper we propose a novel framework for analyzing this phenomenon based on the notions of social importance and local discrimination. We propose a "favors game", where a rec...
Article
Full-text available
In this paper, we investigate the use of emotional information in the learning process of autonomous agents. Inspired by four dimensions that are commonly postulated by appraisal theories of emotions, we construct a set of reward features to guide the learning process and behaviour of a reinforcement learning (RL) agent that inhabits an environment...
Article
Full-text available
The positive impact of emotions in decision-making has long been established in both natural and artificial agents. In the perspective of appraisal theories, emotions complement perceptual information, coloring our sensations and guiding our decision-making. However, when designing autonomous agents, is emotional appraisal the best complement to th...
Conference Paper
Full-text available
In this paper we propose a novel associative metric based on the classical conditioning paradigm that, much like what happens in nature, identifies associations between stimuli perceived by a learning agent while interacting with the environment. We use an associative tree structure to identify associations between the perceived stimuli and use thi...
Conference Paper
Full-text available
In this paper, we propose an adaptation of four common appraisal dimensions that evaluate the relation of an agent with its environment into reward features within an intrinsically motivated reinforcement learning framework. We show that, by optimizing the relative weights of such features for a given environment, the agents attain a greater degree...
Conference Paper
Full-text available
Recently, a novel framework has been proposed for intrinsically motivated reinforcement learning (IMRL) in which a learning agent is driven by rewards that include not only information about what the agent must accomplish in order to “survive”, but also additional reward signals that drive the agent to engage in other activities, such as playing or...
Conference Paper
Full-text available
Autonomous agents are systems situated in dynamic environments. They pursue goals and satisfy their needs by responding to external events from the environment. In these unpredictable conditions, the agents' adaptive skills are a key factor for their success. Based on previous interactions with its environment, an agent must learn new knowledge abo...
Conference Paper
Full-text available
Agents cannot be decoupled from their environment. An agent perceives and acts in a world and the model of the world influences how the agent makes decisions. Most systems with virtual embodied agents simulate the environment within a specific realization engine such as the graphics engine. As a consequence, these agents are bound to a particular k...
Conference Paper
Full-text available
Modeling synthetic characters which interact with objects in dynamic virtual worlds is important when we want the agents to act in an autonomous and non-preplanned way. Such interactions with objects would allow the synthetic characters to behave in a more believable way. Once objects offer innumerous uses, it is essential that the agent is able to...
Conference Paper
Full-text available
Overview of the FearNot! system for demonstration. Extended Abstract FearNot! is a story-telling application originally created in the EU FP5 project VICTEC and now extended in the FP6 project eCIRCUS [eCIRCUS 07]. It has applied ideas from Forum Theatre [Boal 79] to the domain of education against bullying. In Forum Theatre, sections of an audie...
Conference Paper
Full-text available
This paper describes our work integrating automatic speech generation into a virtual environment where autonomous agents are enabled to interact by natural spoken language. The application intents to address bullying problems for children aged 9-12 in the UK and Germany by presenting improvised dramas and by asking the user to act as an "invisible...
Conference Paper
Full-text available
Virtual environments are often populated by autonomous synthetic agents capable of acting and interacting with other agents as well as with humans. These virtual worlds also include objects that may have different uses and types of interactions. As such, these agents need to identify possible interactions with the objects in the environment and mea...
Conference Paper
Full-text available
This demo features FearNot!, a school-based Virtual Learning Environment (VLE) populated by synthetic characters representing the various actors in a bullying scenario. FearNot! uses emergent narrative to create improvised dramas with those characters. The goal is to enable children to explore bullying issues, and coping strategies, interacting wit...

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Projects

Projects (2)
Project
Modeling and implementation of AI decision-makers in the context of pharmaceutical supply-chains.
Project
Uncovering the Structure of Human Behavior by using Genetic Programming