Automated Planning

Plan, Intent, and Goal Recognition

Goal recognition is the task of recognizing agents’ goals from possibly incomplete or noisy observed action sequences. Most goal and plan recognition approaches employ plan libraries to represent agent behavior (i.e., a library that describes all plans for achieving goals), and plan recognition techniques which use such libraries are analogous to parsing. However, work that use classical planning domain definitions to represent potential agent behavior bring goal and plan recognition closer to automated planning. I have worked on a number approaches that perform goal recognition using classical planning domains that are order of magnitude faster than the older state-of-the art, as well as published a book chapter on the applications of plan recognition, of which there are many. Our work is also featured as the state of the art in plan recognition as planning in PAIR's tutorial on plan recognition. Human users generally operate in complex dynamic environments where they face challenges due to cognitive overload in planning and replanning; that is, the users must perform multiple concurrent tasks including: collecting coherent information about the current situation, reasoning about constraints and policies, and dealing with uncertainty to achieve timely decision making. In order to help the users to cope with cognitive overload in such an environment, proactive agents can offer context-sensitive assistance by anticipating the users needs, autonomously planning assistive actions, and offering assistance in an appropriate format at a right time. While at CMU, I have worked on various architectures for proactive assistance in the context of information gathering for coalition operations. I also co-chaired (with Jean Oh) the AAAI Fall Symposium on Proactive Assistant Agents This work has yielded the following (selected) publications:

GUSMÃO, Kin Max P.; PEREIRA, Ramon F; and MENEGUZZI, Felipe. The More the Merrier?! Evaluating the Effect of Landmark Extraction Algorithms on Landmark-Based Goal Recognition. In the AAAI 2020 Workshop on Plan, Activity, and Intent Recognition (PAIR), New York, USA, 2020.
[PDF] [BibTeX] [Slides]
GUSMÃO, Kin Max P.; PEREIRA, Ramon F; and MENEGUZZI, Felipe. The More the Merrier?! Evaluating the Effect of Landmark Extraction Algorithms on Landmark-Based Goal Recognition. In the AAAI 2020 Workshop on Plan, Activity, and Intent Recognition (PAIR), New York, USA, 2020.
[PDF] [BibTeX] [Slides]
AMADO, Leonardo R.; and MENEGUZZI, Felipe. LatRec: Recognizing Goals in Latent Space (Student Poster). In 34th AAAI Conference on Artificial Intelligence (AAAI), New York, USA, 2020.
[PDF] [BibTeX] [Slides]
AMADO, Leonardo R.; LICKS, Gabriel P.; MARCON, Matheus; PEREIRA, Ramon F.; and MENEGUZZI, Felipe. Using Self-Attention LSTMs to Enhance Observations in Goal Recognition. In Proceedings of the 33rd International Joint Conference on Neural Networks (IJCNN), Glasgow, Scotland, 2020.
[PDF] [BibTeX] [Slides]
PEREIRA, Ramon. F.; PEREIRA, André G.; MENEGUZZI, Felipe. Landmark-Enhanced Heuristics for Goal Recognition in Incomplete Domain Models. In Proceedings of the 29th International Conference on Automated Planning and Scheduling (ICAPS), Berkeley, USA, 2019.
[PDF] [BibTeX] [Slides]
PEREIRA, Ramon. F.; VERED, Mor; MENEGUZZI, Felipe; RAMIREZ, Miquel. Online Probabilistic Goal Recognition over Nominal Models. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), Macau, China, 2019.
[PDF] [BibTeX]
AMADO, Leonardo R.; PEREIRA, Ramon F.; AIRES, João Paulo; MAGNAGUAGNO, Maurício C.; GRANADA, Roger Leitzke; and MENEGUZZI, Felipe. Goal Recognition in Latent Space. In Proceedings of the 31st International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 2018.
[PDF] [BibTeX]
PEREIRA, Ramon; and MENEGUZZI, Felipe. Goal Recognition in Incomplete Domain Models. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), New Orleans, USA, 2018.
[PDF] [BibTeX]
VERED, Mor; PEREIRA, Ramon F.; KAMINKA, Gal; and MENEGUZZI, Felipe. Online Goal Recognition as Reasoning over Landmarks. In AAAI 2018 Workshop on Plan, Activity, and Intent Recognition (PAIR), New Orleans, USA, 2018.
[PDF] [BibTeX]
PEREIRA, Ramon F.; OREN, Nir; and MENEGUZZI, Felipe. Landmark-Based Heuristics for Goal Recognition. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), San Francisco, CA, USA, 2017.
[PDF] [BibTeX] [Slides]
PEREIRA, Ramon F.; OREN, Nir; and MENEGUZZI, Felipe. Detecting Commitment Abandonment by Monitoring Plan Execution. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), São Paulo, Brazil, 2017.
[PDF] [BibTeX]
MONTEIRO, Juarez; AIRES, João Paulo; GRANADA, Roger; BARROS, Rodrigo and MENEGUZZI, Felipe. Virtual Guide Dog: An Application to Support Visually-Impaired People through Deep Convolutional Neural Networks. In Proceedings of the 30th International Joint Conference on Neural Networks, Anchorage, AK, USA.
[PDF] [BibTeX]
MONTEIRO, Juarez; GRANADA, Roger; BARROS, Rodrigo and MENEGUZZI, Felipe. Deep Neural Networks for Kitchen Activity Recognition. In Proceedings of the 30th International Joint Conference on Neural Networks, Anchorage, AK, USA.
[PDF] [BibTeX]
FRAGA PEREIRA, Ramon; and MENEGUZZI, Felipe. Landmark-based Plan Recognition, in arXiv, 2016.
[arXiv] [BibTeX]
FRAGA PEREIRA, Ramon; Meneguzzi, Felipe. Landmark-based Plan Recognition. In 22nd European Conference on Artificial Intelligence (ECAI), The Hague, Netherlands, 2016.
[PDF (IOS Press)] [PDF] [BibTeX]
FAGUNDES, Moser; MENEGUZZI, Felipe; BORDINI, Rafael and VIEIRA, Renata. Dealing with ambiguity in plan recognition under time constraints, In Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Paris, France, 2014.
[PDF] [BibTeX] [Slides]
FAGUNDES, Moser; OSSOWSKI, Sascha; and MENEGUZZI, Felipe. Analyzing the tradeoff between efficiency and cost of norm enforcement in stochastic environments populated with self-interested agents, In Proceedings of the 21st European Conference on Artificial Intelligence (ECAI), Prague, Czech Republic, 2014.
[PDF] [BibTeX]
OH, Jean; MENEGUZZI, Felipe; SYCARA, Katia and NORMAN, Timothy J. Prognostic normative reasoning, Engineering Applications of Artificial Intelligence, 2013.
[DOI] [BibTeX]
MENEGUZZI, Felipe; OH, Jean; CHAKRABORTY, Nilanjan; SYCARA, Katia; MEHROTRA, Siddharth; TITTLE, James and LEWIS, Michael. A cognitive architecture for emergency response, in Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Valencia, Spain, 2012.
[PDF] [BibTeX]
OH, Jean; MENEGUZZI, Felipe; SYCARA, Katia and NORMAN, Timothy J. An agent architecture for prognostic reasoning assistance, in Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI), Barcelona, Spain, 2011.
[PDF] [BibTeX]
OH, Jean; MENEGUZZI, Felipe; SYCARA, Katia and NORMAN, Timothy J. Prognostic normative reasoning in coalition planning, in Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Taipei, Taiwan, 2011.
[PDF] [BibTeX]
MENEGUZZI, Felipe; OH, Jean; SYCARA, Katia; PARSONS, Simon and NORMAN, Timothy J. Challenges in anticipatory information management under network constraints , in The Fourth Annual Conference of the International Technology Alliance, London, UK, 2010.
[PDF] [BibTeX]
OH, Jean; MENEGUZZI, Felipe; SYCARA, Katia. ANTIPA: an agent architecture for intelligent information assistance, in Proceedings of the Nineteenth European Conference on Artificial Intelligence (ECAI), Lisbon, Portugal, 2010.
[PDF] [BibTeX]

AI Planning Formalisms

Planning is perhaps the first thing that comes to mind when one thinks about Artificial Intelligence. Understandably, algorithms for deciding what to do have been one of the most prolific areas of research in the field. One of the main contributions in my thesis was the integration of a planning algorithm to a BDI agent interpreter to strike a balance between human-optimized plans in a plan library and the creation of new plans at runtime to allow an agent to deal with situations not foreseen at design time. My interests lie in the relations that planning formalisms and algorithms have to agent reasoning, as well as the convertibility of real world domains into planning formalisms (which are more straightforward for human planners). Such work has yielded the following (selected) publications:

MAGNAGUAGNO, Maurício C.; MENEGUZZI, Felipe. Semantic Attachments for HTN Planning. In 34th AAAI Conference on Artificial Intelligence (AAAI), New York, USA, 2020.
[PDF] [BibTeX] [Slides]
MAGNAGUAGNO, Maurício C.; PEREIRA, Ramon F.; MÓRE, Martin D.; and MENEGUZZI, Felipe. Chapter: Web Planner: A Tool to Develop, Visualize and Test Classical Planning Domains. In "Knowledge Engineering Tools and Techniques for AI Planning", 2020.
[DOI] [BibTeX]
MAGNAGUAGNO, Maurício C.; PEREIRA, Ramon F.; MÓRE, Martin D.; and MENEGUZZI, Felipe.. Develop, Visualize and Test Classical Planning Descriptions in your Browser (Demo). In Proceedings of the 29th International Conference on Automated Planning and Scheduling (ICAPS), Berkeley, USA, 2019.
[PDF] [BibTeX].
MENEGUZZI, F.; MAGNAGUAGNO, M. C.; SINGH, Munindar; TELANG, Pankaj; and YORKE-SMITH, Neil. GoCo: Planning Expressive Commitment Protocols. Autonomous Agents and Multi-agent Systems, vol 32:4, pp 459–502, 2018.
[DOI] [PDF] [BibTeX]
MAGNAGUAGNO, Maurício C.; PEREIRA, Ramon F.; and MENEGUZZI, Felipe. DOVETAIL - An abstraction for Classical Planning using a Visual Metaphor, In 29th International Florida Artificial Intelligence Research Society Conference (FLAIRS), Key Largo, USA (FLAIRS), Key Largo, USA, 2016.
[PDF] [BibTeX]
DE SILVA, Lavindra; MENEGUZZI, Felipe; SANDERSON, David; CHAPLIN, Jack; BAKKER, Otto; ANTZOULATOS, Nikolas and RATCHEV, Svetan. Interfacing BDI Agent Systems with Geometric Reasoning for Robotics and Manufacturing, In International Workshop on Service Orientation in Holonic and Multi-Agent Manufacturing (SOHOMA’15), Cambridge, UK.
[PDF] [BibTeX]
DE SILVA, Lavindra and MENEGUZZI, Felipe. On the Design of Symbolic-Geometric Online Planning Systems, In Workshop on Hybrid Reasoning (HR 2015) @ IJCAI 2015, Buenos Aires, Argentina, 2015.
[PDF] [BibTeX] [Slides]
MENEGUZZI, Felipe; TANG, Yuqing; SYCARA, Katia and PARSONS, Simon. An approach to generate MDPs using HTN representations, in Decision Making in Partially Observable, Uncertain Worlds: Exploring Insights from Multiple Communities (DMPOUW), Barcelona, Spain, 2011.
[PDF] [BibTeX] [Slides]
TANG, Yuqing; MENEGUZZI, Felipe; PARSONS, Simon and SYCARA, Katia. Probabilistic Hierarchical Planning over MDPs, in Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Taipei, Taiwan, 2011.
[PDF] [BibTeX]
MENEGUZZI, Felipe; TANG, Yuqing; SYCARA, Katia and PARSONS, Simon. On representing planning domains under uncertainty, in The Fourth Annual Conference of the International Technology Alliance, London, UK, 2010.
[PDF] [BibTeX]

Reinforcement Learning

Reinforcement Learning is a class of machine learning algorithms aimed at learning behavior from interacting with an environment. The current stage of hype about Machine Learning has finally arrived at this type of learning algorithm, but this is not why I work on this approach, but rather, because this type of learning in intimately related to planning. My work on this area, while relatively limited, is very fun, and have led to the following publications:

MONTEIRO, Juarez; GAVENSKI, Nathan; GRANADA, Roger; MENEGUZZI, Felipe; and BARROS, Rodrigo. Augmented Behavioral Cloning from Observation . In Proceedings of the 33rd International Joint Conference on Neural Networks (IJCNN), Glasgow, Scotland, 2020.
[PDF] [BibTeX] [Slides]
AMADO, Leonardo; and MENEGUZZI, Felipe. Q-Table compression for reinforcement learning. In Proceedings of the 30th Florida Artificial Intelligence Research Society Conference (FLAIRS), Marco Island, FL, USA, 2017.
[PDF] [BibTeX]
AMADO, Leonardo Rosa; and MENEGUZZI, Felipe Reinforcement learning applied to RTS games. In 2017 Workshop on Adaptive Learning Agents (ALA@AAMAS), São Paulo, Brazil, 2017.
[PDF] [BibTeX] [Slides]

Machine Learning for fMRI

Functional Magnetic Resonance Imaging (or fMRI) is neuroimaging technique that focuses on capturing cerebral activity by monitoring blood flow in the brain as a consequence of activation. In simple terms, whenever regions of the brain are more or less active its flow of oxygenated blood varies generating a blood-oxygen-level dependent (BOLD) signal. This technique has been fundamental for the study of brain function in-vivo, allowing multiple insights into the neuroscience behind brain function, as well as of a number of pathologies and conditions. In this context, have worked with the Brain Institute of Rio Grande do Sul in applying Machine Learning to get insights into neuroscience via machine learning with a focus on interpretability. Our findings have been published in a number of papers:

DA SILVA, Laura; ESPER, Nathalia; RUIZ, Duncan; MENEGUZZI, Felipe; and BUCHWEITZ, Augusto. Visual Explanation for Identification of the Brain Bases for Dyslexia on fMRI Data, in arXiv, 2020.
[arXiv] [BibTeX]
HEINSFELD, Aníbal Sólon; FRANCO, Alexandre Rosa; CRADDOCK, R. Cameron; BUCHWEITZ, Augusto; and MENEGUZZI, Felipe. Identification of autism spectrum disorder using deep learning and the ABIDE dataset. Neuroimage: Clinical, vol 17, pp 16-23, 2018.
[DOI] [PDF] [BibTeX]
CRADDOCK, R. Cameron; MARGULIES, Daniel S.; BELLEC, Pierre; NOLAN NICHOLS, B.; ALCAUTER, Sarael; A. BARRIOS, Fernando; BURNOD, Yves; CANNISTRACI, Christopher J.; COHEN-ADAD, Julien; DE LEENER, Benjamin; DERY, Sebastien; DOWNAR, Jonathan; DUNLOP, Katharine; R. FRANCO, Alexandre; SELIGMAN FROEHLICH, Caroline; GERBER, Andrew J.; S. GHOSH, Satrajit; GRABOWSKI, Thomas J.; HILL, Sean; HEINSFELD, Anibal Sólon; HUTCHISON, R. Matthew; KUNDU, Prantik; LAIRD, Angela R.; LIEW, Sook-Lei; LURIE, Daniel J.; MCLAREN, Donald G.; MENEGUZZI, Felipe; MENNES, Maarten; MESMOUDI, Salma; O'CONNOR, David; PASAYE, Erick H.; PELTIER, Scott; POLINE, Jean-Baptiste; PRASAD, Gautam; FRAGA PEREIRA, Ramon; QUIRION, Pierre-Olivier; ROKEM, Ariel; SAAD, Ziad S.; SHI, Yonggang; STROTHER, Stephen C.; TORO, Roberto; UDDIN, Lucina Q.; VAN HORN, John D.; VAN METER, John W. ; WELSH, Robert C.; and XU, Ting Brainhack: a collaborative workshop for the open neuroscience community, in GigaScience, Vol. 5(1):1--8, 2016.
[DOI] [BibTeX]
FRAGA PEREIRA, Ramon; HEINSFELD, Anibal Sólon; FRANCO, Alexandre; BUCHWEITZ, Augusto; and MENEGUZZI, Felipe. Detecting task-based fMRI compliance using plan abandonment techniques in GigaScience, Vol. 5(1):21--22, 2016.
[DOI] [BibTeX]
HEINSFELD, Anibal Sólon; FRANCO, Alexandre; BUCHWEITZ, Augusto; and MENEGUZZI, Felipe. NeuroView: a customizable browser-base utility in GigaScience, Vol. 5(1):25--25, 2016.
[DOI] [BibTeX]
FROEHLICH, Caroline; MENEGUZZI, Felipe; FRANCO, Alexandre R.; DRESCH, Luiz and BUCHWEITZ, Augusto. Identifying the neural representation of word reading in children diagnosed with dyslexia, In 2015 Annual Meeting of the Organization for Human Brain Mapping (OHBM), Honolulu, Hawaii, USA, 2015.
[PDF] [BibTeX]
FROEHLICH, Caroline; MENEGUZZI, Felipe; FRANCO, Alexandre R. and BUCHWEITZ, Augusto. Classifying Brain States for Cognitive Tasks: a Functional MRI Study in Children with Reading Impairments, In Proceedings of the 24th Brazilian Congress on Biomedical Engineering (CBEB), Uberlándia, MG, Brazil, 2014.
[PDF] [BibTeX]

Multiagent Systems

Agent-based software is an established method for modelling an increasingly important number of network-centred systems. Unlike traditional object-oriented approaches to system modelling, agents are able to control their own internal state and behaviour, and have dynamic relationships among themselves and the environment in which they operate. Until recently, these agent properties have been largely explored on the theoretical level and many practical questions need to be addressed before agent-oriented programming becomes an everyday reality. Thus, practical agent programming languages make up a subfield within agent systems research, yet there are still many unresolved problems.

Normative Reasoning

Systems of autonomous and self-interested agents interacting to achieve individual and collective goals may exhibit undesirable or unexpected properties if left unconstrained. This has led to efforts in defining systems of norms, effectively formalised laws, in terms of obligations, permissions and prohibitions. However, only relatively recently has there been efforts in modifying agent reasoning mechanisms to process norms so that an agent can deliberately, as opposed to by design (or lack of mechanisms), comply with or violate norms. In this context, I contributed to the CONTRACT project, designing and implementing a number of agent reasoning mechanisms to process norms in the context of the aerospace engine care market. My work in this area focuses on designing algorithms for norm processing and integrating them into agent reasoning mechanisms. These efforts have yielded the following publications:

AIRES, João Paulo; and MENEGUZZI, Felipe. A Deep Learning Approach for Norm Conflict Identification. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), São Paulo, Brazil, 2017.
[PDF] [BibTeX]
AIRES, João Paulo; and MENEGUZZI, Felipe. Norm Conflict Identification using Deep Learning. In 2017 International Workshop on Coordination, Organisations, Institutions and Norms (COIN@AAMAS), São Paulo, Brazil, 2017.
[PDF] [BibTeX] [Slides]
KRZISCH, Guilherme; and MENEGUZZI, Felipe Planning in a Normative System. In 2017 International Workshop on Coordination, Organisations, Institutions and Norms (COIN@AAMAS), São Paulo, Brazil, 2017.
[PDF] [BibTeX]
KRZISCH, Guilherme; and MENEGUZZI, Felipe Norm Identification in Jason using a Bayesian Approach. In 18th Workshop on Multi-agent-based Simulation (MABS@AAMAS), São Paulo, Brazil, 2017.
[PDF] [BibTeX] [Slides]
CRANEFIELD, Stephen; Meneguzzi, Felipe; OREN, Nir; SAVARIMUTHU, Bastin T. R.. A Bayesian approach to norm identification. In 22nd European Conference on Artificial Intelligence (ECAI), The Hague, Netherlands, 2016.
[PDF (IOS Press)] [PDF] [BibTeX]
MENEGUZZI, Felipe; TELANG, Pankaj R. and YORKE-SMITH, Neil. Towards Planning Uncertain Commitment Protocols, In Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Istanbul, Turkey, 2015.
[PDF] [BibTeX]
CRANEFIELD, Stephen; MENEGUZZI, Felipe; OREN, Nir and SAVARIMUTHU, Tony. A Bayesian approach to norm identification, In Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Istanbul, Turkey, 2015.
[PDF] [BibTeX]
CRANEFIELD, Stephen; MENEGUZZI, Felipe; OREN, Nir and SAVARIMUTHU, Tony. A Bayesian approach to norm identification, University of Otago Technical Report, Otago, New Zealand, 2015.
[DOI] [PDF] [BibTeX]
CHANG, Stephan and MENEGUZZI, Felipe. Simulating Normative Behaviour in Multi-Agent Environments using Monitoring Artefacts, In 17th International Workshop on Coordination, Organisations, Institutions and Norms (COIN 2015) @AAMAS, Istanbul, Turkey, 2015.
[PDF] [BibTeX]
AIRES, João Paulo; STRUBE DE LIMA, Vera Lúcia and MENEGUZZI, Felipe. Identifying Potential Conflicts between Norms in Contracts, In 18th International Workshop on Coordination, Organisations, Institutions and Norms (COIN 2015) @IJCAI, Buenos Aires, Argentina, 2015.
[PDF] [BibTeX]
LI, Jiaqi; MENEGUZZI, Felipe; FAGUNDES, Moser and LOGAN, Brian. Reinforcement Learning of Normative Monitoring Intensities, In 18th International Workshop on Coordination, Organisations, Institutions and Norms (COIN 2015) @IJCAI, Buenos Aires, Argentina, 2015.
[PDF] [BibTeX] [Slides]
MENEGUZZI, Felipe; LOGAN, Brian and FAGUNDES, Moser. Norm monitoring with asymmetric information, In Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Paris, France, 2014.
[PDF] [BibTeX]
FAGUNDES, Moser; OSSOWSKI, Sascha; and MENEGUZZI, Felipe. Analyzing the tradeoff between efficiency and cost of norm enforcement in stochastic environments populated with self-interested agents, In Proceedings of the 21st European Conference on Artificial Intelligence (ECAI), Prague, Czech Republic, 2014.
[PDF] [BibTeX]
ALRAWAGFEH, Wagdi and MENEGUZZI, Felipe. Utilizing Permission Norms in BDI Practical Normative Reasoning, In 16th International Workshop on Coordination, Organisations, Institutions and Norms (COIN 2014) @AAMAS, Paris, France, 2014.
[Springer][PDF] [BibTeX] [Slides]
FAGUNDES, Moser; OSSOWSKI, Sascha; and MENEGUZZI, Felipe. Imperfect norm enforcement in stochastic environments: an analysis of efficiency and cost tradeoffs, In Proceedings of the 14th Ibero-American Conference on Artificial Intelligence (IBERAMIA), Santiago, Chile, 2014.
[PDF] [BibTeX]
MENEGUZZI, Felipe; TELANG, Pankaj and SINGH, Munindar P. A First-Order Formalization of Commitments and Goals, In Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI), Bellevue, WA, USA, 2013.
[PDF] [BibTeX] [Slides]
TELANG, Pankaj; MENEGUZZI, Felipe and SINGH, Munindar P. Hierarchical Planning about Goals and Commitments, In Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Saint Paul, MN, USA, 2013.
[PDF] [BibTeX]
OREN, Nir and MENEGUZZI, Felipe. Norm Identification through Plan Recognition, In 15th International Workshop on Coordination, Organisations, Institutions and Norms (COIN 2013) @AAMAS, Saint Paul, MN, USA, 2013.
[PDF] [BibTeX] [Slides]
MENEGUZZI, Felipe; MODGIL, Sanjay; OREN, Nir; MILES, Simon; LUCK, Michael; FACI, Noura; Applying electronic contracting to the aerospace aftercare domain, Engineering Applications of Artificial Intelligence, Available online 8 July 2012.
[DOI] [BibTeX]
OH, Jean; MENEGUZZI, Felipe; SYCARA, Katia and NORMAN, Timothy J. Introduction to Prognostic Normative Reasoning, in Lecture Notes in Computer Science 7068, 2012.
[PDF] [BibTeX]
OREN, Nir; VASCONCELOS, Wamberto; MENEGUZZI, Felipe and LUCK, Michael. Acting on Norm Constrained Plans, in Computational Logic in Multi-Agent Systems (CLIMA XII), Barcelona, Spain, 2011.
[PDF] [BibTeX] [Slides]
MILES, Simon; OREN, Nir; LUCK, Michael; MODGIL, Sanjay; MENEGUZZI, Felipe; FACI, Nora; HOLT, Camden; VICKERS, Gary. Electronic Business Contracts between Services, in Handbook of Research on P2P and Grid Systems for Service-Oriented Computing: Models, Methodologies and Applications, published by IGI Global, 2010.
[PDF] [BibTeX]
MENEGUZZI, Felipe; OREN, Nir and VASCONCELOS, Wamberto. Using constraints for Norm-aware BDI Agents, in The Fourth Annual Conference of the International Technology Alliance, London, UK, 2010.
[PDF] [BibTeX]
MENEGUZZI, Felipe; MODGIL, Sanjay; OREN, Nir; MILES, Simon; LUCK Michael; HOLT, Camden; SMITH, Malcolm. A contract-based system for aerospace aftercare, in Workshop on Technological trends in Enterprise Systems for SMEs and large enterprises: Heading towards the Future Internet, London, UK, 2009.
[PDF] [BibTeX] [Slides]
MENEGUZZI, Felipe and LUCK, Michael Norm-based behaviour modification in BDI agents. Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Budapest, Hungary, 2009.
[PDF] [BibTeX] [Slides]
MODGIL, Sanjay; FACI, Noura; MENEGUZZI, Felipe; OREN, Nir; MILES, Simon; LUCK, Michael A Framework for Monitoring Agent-Based Normative Systems. Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Budapest, Hungary, 2009.
[PDF] [BibTeX] [Slides]
MENEGUZZI, Felipe; MODGIL, Sanjay; OREN, Nir; MILES, Simon; LUCK Michael; FACI, Noura; HOLT, Camden; SMITH, Malcolm. Monitoring and Explanation of Contract Execution: A Case Study in the Aerospace Domain . Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems (AAMAS) - Industry and Applications Track, Budapest, Hungary, 2009.
[PDF] [BibTeX] [Slides]

Agent Reasoning and the BDI Model

One of the most widely studied agent reasoning models is based on the notions of beliefs, desires and intentions (or BDI) as mental attitudes that guide the selection of courses of action. In this model, beliefs describe knowledge about the world, while desires are states of affairs to achieve, and intentions are commitments to achieving a particular subset of desires. The BDI model has its origins in the philosophical work of Bratman to explain the way in which humans select a series of actions directed at the achievement of a larger goal while avoiding spending time considering less important ones. Most of my work on agent reasoning draws heavily on this particular model either directly, in my work on agent languages, or indirectly, in the architectures for proactive assistance I worked on. My research in this area has yielded the following publications:

DE SILVA, Lavindra; MENEGUZZI, Felipe; and LOGAN, Brian. BDI Agent Architectures: A Survey. In 29th International Joint Conference on Artificial Intelligence (IJCAI), Yokohama, Japan, 2020.
[PDF] [BibTeX] [Slides]
DE SILVA, Lavindra; MENEGUZZI, Felipe; and LOGAN, Brian. An Operational Semantics for a Fragment of PRS. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden, 2018.
[PDF (IJCAI)] [PDF] [BibTeX] [Slides]
CARDOSO, Rafael C.; FRAGA PEREIRA, Ramon; KRZISCH, Guilherme; MAGNAGUAGNO, MaurÍcio C.; BASÉGIO, Túlio; and MENEGUZZI, Felipe. Team PUCRS: a Decentralised Multi-Agent Solution for the Agents in the City Scenario. Int. J. of Agent-Oriented Software Engineering, to appear, 2017.
[PDF] [BibTeX]
MENEGUZZI, Felipe and DE SILVA, Lavindra. Planning in BDI Agents: A survey of the integration of planning algorithms and agent reasoning, In The Knowledge Engineering Review (KER), Vol. 30:1, 2015.
[DOI] [BibTeX]
LUZ, Bernardo; MENEGUZZI, Felipe and VICCARI, Rosa. Alternatives to Threshold-Based Desire Selection in Bayesian BDI Agents, In Workshop on Engineering Multiagent Systems (EMAS'13) @AAMAS, Saint Paul, MN, USA, 2013.
[PDF] [BibTeX] [Slides]
MENEGUZZI, Felipe. Motivations and Goal-Directed Autonomy, in AAAI-10 Workshop on Goal-Directed Autonomy, 2010 (invited paper).
[PDF] [BibTeX] [Slides]
MENEGUZZI, Felipe Extending agent languages for autonomy. AAMAS 2008 Doctoral Mentoring Programme. Estoril, Portugal, 2008.
[PDF] [BibTeX] [Slides]
MENEGUZZI, F.; LUCK, Michael; Interaction among agents that plan. From Agent Theory to Agent Implementation. Estoril, Portugal, 2008.
[PDF] [BibTeX] [Slides]
MENEGUZZI, F. and LUCK, M.. Motivations as an abstraction of meta-level reasoning. Proceedings of the 5th International Central and Eastern European Conference on Multi-Agent Systems. Leipzig, 2007.
[PDF] [BibTeX] [Slides]

Agent Programming Languages

One major push of my research during my PhD was focused on creating agent languages and architectures that overcome these limitations by the integration of AI techniques within the underlying agent interpreters. In my thesis I created AgentSpeak(PL), an extension of a traditional logic-based BDI-style agent language, which includes a mechanism for processing goals in a manner that decouples goal achievement from plan execution, as well as generating new plans to cope with unforeseen situations at design time. AgentSpeak(PL) bridges the gap between agent languages and multiagent systems by introducing a simple cooperation mechanism together with a norm processing mechanism aimed at providing some degree of societal control. Publications in this area include:

MENEGUZZI, F. R.; ZORZO, Avelino Francisco; MÓRA, Michael da Costa; LUCK, Michael M.. Incorporating Planning into BDI Agents. Scalable Computing: Practice and Experience, v. 8, 2007.
[PDF] [BibTeX]
MENEGUZZI, F.; LUCK, Michael; Leveraging new plans in AgentSpeak(PL). Declarative Agent Languages and Technologies. Estoril, Portugal, 2008.
[PDF] [BibTeX] [Slides]
MENEGUZZI, F.; LUCK, Michael. Composing high-level plans for declarative agent programming. Declarative Agent Languages and Technologies. Honolulu, Hawai'i, 2007.
[PDF] [BibTeX] [Slides]
MENEGUZZI, F.R., ZORZO, Avelino Francisco, MÓRA, Michael da Costa. Mapping mental states into propositional planning. In: Proceedings of the 3rd International Joint Conference on Autonomous Agents and Multiagent Systems, New York, 2004.
[PDF] [BibTeX]
MENEGUZZI, F. R.; ZORZO, Avelino Francisco; MÓRA, Michael da Costa. Propositional Planning in BDI Agents. In: 19th Annual ACM Symposium on Applied Computing, 2004, Nicosia. Proceedings of the 19th Annual ACM Symposium on Applied Computing, 2004.
[PDF] [BibTeX]

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