Professor Felipe Meneguzzi | People | The University of Aberdeen

Professor Felipe Meneguzzi

Chair in Computing Science

Accepting PhDs

About

School/Department: School of Natural and Computing Sciences

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Personal Website

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Biography

I am a researcher on automated planning, goal and plan recognition, multiagent systems, BDI agents, and machine learning. I currently hold a Chair of Computing Science at the University of Aberdeen. I am a Senior Member of the ACM and of AAAI. I was a Professor of AI at the Pontifical Catholic University of Rio Grande do Sul in Brazil, where I remain as a Bridges Professor. Before my current position, I worked as a Project Scientist in Multiagent Systems at the Robotics Institute of Carnegie Mellon University after completing a Postdoctoral Fellowship at the same university. I obtained my PhD degree at King's College London, with a thesis on Extending agent languages for multiagent domains under the supervision of Professor Michael Luck and co-supervised by Professor Andrew Jones. Prior to my full-time academic career, I worked in the industry as a contractor for Hewlett-Packard Brazil, and worked on a variety of projects with some very interesting people.

For further information, visit my personal website.

Qualifications

PhD Artificial Intelligence
2009 - King's College London

External Memberships

Executive Council of the AAAI
Special Committee on AI for the Brazilian Computer Society
Bridges Professor at the Pontifical Catholic University of Rio Grande do Sul

Prizes and Awards

Best SPC member award from AAMAS 2021;
1st place at the International Planning Competition (IPC) in 2020;
Distinguished Visiting Fellow award from the Scottish Informatics and Computer Science Alliance (SICSA);
Best Student paper at IJCNN 2017: An Application to Support Visually-Impaired People through Deep Convolutional Neural Networks;
Visionary workshop paper at AAMAS 2017: Norm Conflict Identification Using Deep Learning;
Google Research Award for Latin America in 2016 as well as in 2019;
1st place at the 2016 Multi-Agent Programming Contest;
2nd place at the 2016 Predictive Analytics in Mental Health Competition (PAC); and
Runner up to the Microsoft Research Faculty Fellowship in 2013.

Research

Research Overview

My overall research area is Artificial Intelligence and my research spans the areas of Automated Planning, Autonomous Agents and Applications of Machine Learning. The main goal of my research is to develop practical reasoning mechanisms as a means to both refine the capabilities of autonomous agents and to understand reasoning in general. To accomplish this objective, I work on integrating data-driven and symbolic techniques for reasoning and decision-making while maintaining a low complexity for the description formalisms, helping to achieve explainability for human users.

Research Areas

Accepting PhDs

I am currently accepting PhDs in Computing Science.

Please get in touch if you would like to discuss your research ideas further.

Computing Science

Supervising
Accepting PhDs

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Research Specialisms

Artificial Intelligence
Machine Learning

Our research specialisms are based on the Higher Education Classification of Subjects (HECoS) which is HESA open data, published under the Creative Commons Attribution 4.0 International licence.

Publications

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Biography

Qualifications

External Memberships

Prizes and Awards

Research Overview

Research Areas

Accepting PhDs

Computing Science

Research Specialisms

Q-Table compression for reinforcement learning

Sensor Placement for Plan Monitoring using Genetic Programming

A Deep Learning Approach to Classify Aspect-Level Sentiment using Small Datasets

Goal Recognition in Latent Space

Measuring Semantic Similarity between Sentences Using A Siamese Neural Network

Norm Conflict Identification using Vector Space Offsets

Identification of autism spectrum disorder using deep learning and the ABIDE dataset

Optimizing UCT for settlers of catan

GoCo: planning expressive commitment protocols

An operational semantics for a fragment of PRS

Goal recognition in incomplete domain models

Norm identification in Jason using a bayesian approach

Towards online goal recognition combining goal mirroring and landmarks

Norm conflict identification in contracts

Norm Conflict Identification using Deep Learning

Bounded-Monitor Placement in Normative Environments

Landmark-Based Heuristics for Goal Recognition

Deep neural networks for kitchen activity recognition

A Deep Learning Approach for Norm Conflict Identification

Detecting commitment abandonment by monitoring sub-optimal steps during plan execution

Reinforcement Learning Applied to RTS games

Virtual guide dog: An application to support visually-impaired people through deep convolutional neural networks

A Deep Neural Architecture for Kitchen Activity Recognition

A Plan Optimality Monitoring Approach to Detect Commitment Abandonment

Applying ontologies to the development and execution of Multi-Agent Systems

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Books and Reports

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