Professor Felipe Meneguzzi

Chair in Computing Science

Accepting PhDs

51cg

School/Department: School of Natural and Computing Sciences

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 51cg. I am a and of . 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 of after completing a Postdoctoral Fellowship at the same university. I obtained my PhD degree at , with a thesis on under the supervision of Professor and co-supervised by Professor . Prior to my full-time academic career, I worked in the industry as a contractor for , and worked on a variety of projects with some very interesting .

For further information, visit my .

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

in 2024;
;
;
1st place at the () in 2020;
Distinguished Visiting Fellow award from the
Best Student paper at IJCNN 2017:
Visionary workshop paper at AAMAS 2017: ;
as well as
1st place at the
2nd place at the ; 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

View Research Area

Research Specialisms

Artificial Intelligence
Machine Learning

Our research specialisms are based on the Higher Education Classification of Subjects (HECoS) which is , published under the licence.

Teaching

Programmes

BSc Computing Science
Undergraduate, 4 year, September start
MSc Artificial Intelligence
Postgraduate, 3 stage, January start
MSc Artificial Intelligence
Postgraduate, 3 semester, September start
MEng Computing Science (5 years)
Undergraduate, 5 year, September start
MEng Computing Science (Artificial Intelligence)
Undergraduate, 5 year, September start
BSc Computing Science and Mathematics
Undergraduate, 4 year, September start
BSc Computing Science and Physics
Undergraduate, 4 year, September start

Courses

Publications

Update

51cg

Biography

Qualifications

External Memberships

Prizes and Awards

Research Overview

Research Areas

Accepting PhDs

Computing Science

Research Specialisms

Programmes

Courses

Predicting Brain Age at Slice Level: Convolutional Neural Networks and Consequences for Interpretability

A Tensor-Based Markov Decision Process Representation

Object-Based Goal Recognition Using Real-World Data

Using Self-Attention LSTMs to Enhance Observations in Goal Recognition

SmartIX: A database indexing agent based on reinforcement learning

Augmented Behavioral Cloning from Observation

Web Planner: A Tool to Develop, Visualize, and Test Classical Planning Domains

Landmark-based approaches for goal recognition as planning

Using Sub-Optimal Plan Detection to Identify Commitment Abandonment in Discrete Environments

BDI agent architectures: A survey

Energy-aware path planning for autonomous mobile robot navigation

LatRec: Recognizing Goals in Latent Space

Semantic Attachments for HTN Planning

Automating news summarization with sentence vectors offset

Classification of contractual conflicts via learning of semantic representations

ConCon: A contract conflict identifier: Demonstration

GADIS: A genetic algorithm for database index selection

Landmark-enhanced heuristics for goal recognition in incomplete domain models

Online probabilistic goal recognition over nominal models

Using scene context to improve action recognition

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

Forecasting Demand with limited information using gradient tree boosting

Hybrid activity and plan recognition for video streams

Method Composition through Operator Pattern Identification

Monitoring plan optimality using landmarks and domain-independent heuristics

Planning in a Normative System

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Books and Reports

Chapters in Books, Reports and Conference Proceedings

Contributions to Conferences

Contributions to Journals

Contributions to Specialist Publications

Working Papers

Other Contributions