Fabio Cozman's Selected Publications Page!

João Figueiredo Nobre Brito Cortese, Fabio G. Cozman, Marcos Paulo Lucca-Silveira, Adriano Figueiredo Bechara. Should explainability be a fifth ethical principle in AI ethics? AI and Ethics, 2022. Preprint available.

Gustavo Padilha Polleti, Douglas Luan de Souza, Fabio G. Cozman. Why should I not follow you? Reasons for and reasons against in responsible recommender systems. 3rd FAccTRec Workshop on Responsible Recommendation at RecSys, pp. 1-6., 2020. Preprint available.

Gustavo Padilha Polleti, Hugo Neri Munhoz, Fabio Gagliardi Cozman. Explanations within Conversational Recommendation Systems: Improving Coverage through Knowledge Graph Embeddings. AAAI Workshop on Interactive and Conversational Recommendation Systems, 2020. Preprint available.

Andrey Ruschel, Arthur Colombini Gusmao, Gustavo Padilha Polleti, Fabio Gagliardi Cozman. Explaining completions produced by embeddings of knowledge graphs, European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pp. 324-335, 2019. Preprint available.

Gustavo Padilha Polleti, Fabio Gagliardi Cozman. Explaining Content-based Recommendations with Topic Models, Brazilian Conference on Intelligent Systems, pp. 794-799, 2019. Preprint available.

Gustavo Padilha Polleti, Fabio Gagliardi Cozman. Faithfully Explaining Predictions of Knowledge Embeddings, Encontro Nacional de Inteligencia Artificial, pp. 1-12, 2019. Preprint available.

Juliana Cesaro, Fabio G. Cozman Measuring Unfairness through Game-Theoretic Interpretability ECML PKDD Joint International Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence and eXplainable Knowledge Discovery in Data Mining, September 2019. Preprint available.

Rodrigo Monteiro de Aquino, Fabio Gagliardi Cozman. Natural language explanations of classifier behavior, IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering, pp. 239-242, 2019. Preprint available.

Arthur Colombini Gusmao, Alvaro Henrique Chaim Correia, Glauber de Bona, Fabio Gagliardi Cozman. Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach. ICML Workshop on Human Interpretability in Machine Learning, pp. 79-86, 2018. Preprint available.

Cordeiro, V.D., Cozman, F. (2024). Artificial Intelligence and Everyday Knowledge. In: Dunn, H.S., Ragnedda, M., Ruiu, M.L., Robinson, L., The Palgrave Handbook of Everyday Digital Life. Palgrave Macmillan, Cham., pp. 23-35. (https://doi.org/10.1007/978-3-031-30438-5_2)

Cristina Godoy B. de Oliveira, Fabio G. Cozman, João Paulo C. Veiga. This hot AI summer will impact Brazil’s democracy, Nature Human Behaviour, volume 7, pag. 1842–1844, 2023. Available here.

Fabio G. Cozman, Guilherme A. Plonsky, Hugo Neri. Inteligência Artificial: Avanços e Tendências, São Paulo, Instituto de Estudos Avançados da Universidade de São Paulo, 2021.

Bruno Moreschi, Gabriel Pereira, Fabio G. Cozman. The Brazilian Workers in Amazon Mechanical Turk: Dreams and realities of ghost workers. Revista Contracampo. 39:44-64, 2020. Preprint available.

Hugo Neri Munhoz, Fabio Gagliardi Cozman. The role of experts in the public perception of risk of artificial intelligence. AI and Society, Online November 2019. Preprint available.

Marcel Barros; Andressa Pinto; Andres Monroy; Felipe Moreno; Jefferson Coelho; Aldomar Pietro Silva; Caio Fabricio Deberaldini Netto; José Roberto Leite; Marlon Mathias; Eduardo Tannuri; Artur Jordao; Edson Gomi; Fabio Cozman; Marcelo Dottori; Anna Helena Reali Costa. Early Detection of Extreme Storm Tide Events Using Multimodal Data Processing. AAAI, 2024. Preprint available.

Felipe Marino Moreno, Luiz A. Schiaveto Neto, Fabio Gagliardi Cozman, Marcelo Dottori, Eduardo Aoun Tannuri. Enhancing forecast of ocean physical variables through physics informed machine learning in the Santos estuary, Brazil. OCEANS - Chennai Conference, pp. 1-7, 2022. Preprint available.

Gustavo Alencar Bisinotto, Lucas P. Cotrim, Fabio Gagliardi Cozman, Eduardo Aoun Tannuri. Assessment of sea state estimation with convolutional neural networks based on the motion of a moored FPSO subjected to high‑frequency wave excitation. 41st Ocean, Offshore and Arctic Engineering Conference (OMAE), pp. 1-10, 2022. Preprint available.

Marlon S. Mathias, Wesley P. Almeida, Jefferson F. Coelho, Lucas P. de Freitas, Felipe Marino Moreno, Caio Fabricio Deberaldini Netto, Fabio Gagliardi Cozman, Anna Helena Reali Costa, Eduardo Aoun Tannuri, Marcelo Dottori. Augmenting a physics-informed neural network for the 2D Burgers equation by addition of solution data points. Brazilian Conference on Intelligent Systems (BRACIS), v. 2. pp. 388-401, 2022. Available at https://doi.org/10.1115/1.4064676

Gustavo A. Bisinotto, Pedro C. de Mello, Fabio G. Cozman, Eduardo A. Tannuri. Motion-Based Wave Inference With Neural Networks: Transfer Learning From Numerical Simulation to Experimental Data. J. Offshore Mech. Arct. Eng., 146(5): 051204 - Paper No: OMAE-23-1115, 2024. Available at https://doi.org/10.1115/1.4064618

Felipe M. Moreno, Eduardo A. Tannuri, Fabio G. Cozman. Automatic Clustering of Metocean Conditions on the Brazilian Coast Journal of Offshore Mechanics and Arctic Engineering, 145(4): 041202, 2023. Available at https://doi.org/10.1115/1.4056618.

Gustavo A. Bisinotto, João V. Sparano, Alexandre N. Simos, Fabio G. Cozman, Marcos D. Ferreira, Eduardo A. Tannuri. Sea state estimation based on the motion data of a moored FPSO using neural networks: An evaluation with multiple draft conditions. Ocean Engineering, 276 (2023) 114235 Available here.

Felipe Marino Moreno, Jefferson Fialho Coelho, Marlon Sproesser Mathias, Marcel Rodrigues de Barros, Caio Fabricio Deberaldini Netto, Lucas Palmiro de Freitas, Marcelo Dottori, Fábio Gagliardi Cozman, Anna Helena Reali Costa, Edson Satoshi Gomi, Eduardo Aoun Tannuri. Echo State Networks for Surface Current Forecasting in a Port Access Channel. Proceedings of the ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering. Volume 5: Ocean Engineering. Melbourne, Australia. June 11–16, 2023. V005T06A056. ASME. Available at https://doi.org/10.1115/OMAE2023-103265

This is really ongoing research!

Victor Hugo Nascimento Rocha, Fabio Gagliardi Cozman. A credal least undefined stable semantics for probabilistic logic programs and probabilistic argumentation. 19th International Conference on Principles of Knowledge Representation and Reasoning (KR), pp. 1-10, 2022. Preprint available.

Victor Hugo Nascimento Rocha, Fabio Gagliardi Cozman. Bipolar argumentation frameworks with explicit conclusions: Connecting argumentation and logic programming. International Workshop on Non-Monotonic Reasoning (NMR), pp. 1-10, 2022. Preprint available.

Fabio G. Cozman, Hugo Neri Munhoz. Some thoughts on knowledge-enhanced machine learning. International Journal of Approximate Reasoning, 136:308-324, 2021. Preprint available.

Francisco H.O.Vieira de Faria, Arthur Colombini Gusmao, Glauber De Bona, Denis Deratani Maua, Fabio Gagliardi Cozman. Speeding up parameter and rule learning for acyclic probabilistic logic programs. International Journal of Approximate Reasoning, 106:32-50, 2019. Preprint available. (This paper is an extended version of a few papers published in SUM2017, KDMILE2017, and StarAI2017).

Jose Eduardo Ochoa Luna, Kate Revoredo, Fabio Gagliardi Cozman. Link prediction using a probabilistic description logic. Journal of the Brazilian Computer Society, 19:397-409, 2013. Preprint available. (This paper supercedes previous work that appeared in URSW2012 and ENIA2012 and URSW2011.)

Jose Eduardo Ochoa Luna, Kate C. Revoredo, Fabio Gagliardi Cozman. Learning probabilistic description logics: A framework and algorithms, Advances in Artificial Intelligence - 10th Mexican International Conference on Artificial Intelligence (MICAI2011), Lecture Notes in Artificial Intelligence 7094 Part I, pp. 28-39, Springer, 2011. Preprint available. (Selected Second Best Student Paper at the conference.)

Denis Deratani Mauá, Fabio Gagliardi Cozman. Specifying credal sets with probabilistic answer set programming. Proceedings of the Thirteenth International Symposium on Imprecise Probability: Theories and Applications, PMLR 215:321-332, 2023. Available here; preprint available.

Fabio G. Cozman, Denis D. Mauá. The joy of Probabilistic Answer Set Programming: Semantics, complexity, expressivity, inference. International Journal of Approximate Reasoning, 125:218-239, 2020. Preprint available. (This paper supercedes material published in ISIPTA 2019.)

Denis Deratani Maua, Fabio Gagliardi Cozman. Complexity results for probabilistic answer set programming. International Journal of Approximate Reasoning, 118:133-154, 2020. Preprint available. (This paper supercedes, and in a few cases corrects, material published in ECSQARU2017.)

Fabio Gagliardi Cozman and Denis Deratani Maua. On the semantics and complexity of probabilistic logic programs, Journal of Artificial Intelligence Research, 60:221-262, 2017. Article available. (This is an extended version of three papers, one published at PGM2016, another one published at WPLP2016, another one published at ENIAC2016.

Fabio Gagliardi Cozman. Languages for Probabilistic Modeling Over Structured and Relational Domains. A Guided Tour of Artificial Intelligence Research, vol. 2, chapter 9, edited by P. Marquis, O. Papini, H. Prade, Springer, 2020. Preprint available.

Fabio Gagliardi Cozman, Denis Deratani Maua. The finite model theory of Bayesian network specifications: Descriptive complexity and zero/one laws. International Journal of Approximate Reasoning, 110:107-126, 2019. Preprint available. (This paper supercedes, and in a few cases corrects, material published in ESQARU2017; also the material appeared in IJCAI2018 (selected in the Best Sister Conference track).

Fabio G. Cozman, Denis D. Maua. The complexity of Bayesian networks specified by propositional and relational languages, Artificial Intelligence, 262:96-141, 2018. Preprint available. (This paper supercedes, and in a few cases corrects, material published in AAAI2015, SUM2015, Preprint available, and ENIAC2015.

Glauber de Bona, Fabio G. Cozman. On the coherence of probabilistic relational formalisms. Entropy, 20(4):229/1-229/24, 2018. Preprint available.

Denis D. Maua, Fabio G. Cozman. The effect of combination functions on the complexity of relational Bayesian networks. International Journal of Approximate Reasoning, 85:178-195, 2017. Preprint available. (This is an extended version of a paper published at PGM2016.)

Denis D. Maua, Cassio Polpo de Campos, Fabio G. Cozman: The complexity of MAP inference in Bayesian networks specified through logical languages. Int. Joint Conference on Artificial Intelligence, pp. 889-895, 2015. Preprint available.

Fabio G. Cozman, Denis D. Maua. Specifying probabilistic relational models with description logics. Encontro Nacional de Inteligencia Artificial e Computacional, 2015. Preprint available.

Fabio G. Cozman, Rodrigo B. Polastro, Felipe I. Takiyama, and Kate C. Revoredo. Computing inferences for relational Bayesian networks based on ALC constructs. in F. Bobillo, R. N. Carvalho, P. C. G. Costa, C. d'Amato, N. Fanizzi, K. B. Laskey, K. J. Laskey, T. Lukasiewicz, M. Nickles, M. Pool (editors), Uncertainty Reasoning for the Semantic Web III: ISWC International Workshops, URSW 2011-2013, Revised Selected Papers (ISBN: 978-3-319-13412-3), pp. 21-40, 2014. Preprint available.

Paulo E. Santos, Fabio G. Cozman, Valquiria F. Pereira, B. Hummel. Probabilistic logic encoding of spatial domains. International Workshop on Uncertainty in Description Logics, 2010. Preprint available.

Valquiria F. Pereira, B. Hummel, Paulo E. Santos, Fabio G. Cozman. Encoding spatial domains with relational Bayesian networks. Workshop on Spatio-Temporal Dynamics (STeDY), pp. 49-54, 2010. Preprint available.

Cassio Polpo de Campos, Fabio G. Cozman, Jose Eduardo Ochoa Luna. Assembling a consistent set of sentences in relational probabilistic logic with stochastic independence, Journal of Applied Logic, 7:137-154, 2009.

Fabio G. Cozman, Rodrigo Polastro. Complexity analysis and variational inference for interpretation-based probabilistic description logics, Conference on Uncertainty in Artificial Intelligence, 2009. Preprint available.

Fabio G. Cozman, Rodrigo Bellizia Polastro. Loopy propagation in a probabilistic description logic, Second International Conference on Scalable Uncertainty Management, Lecture Notes in Artificial Intelligence, LNAI 5291, pp. 120-133, Springer, 2008. Preprint available.

This is really ongoing research!

Marcelo Archanjo José, Fabio Gagliardi Cozman. A multilingual translator to SQL with database schema pruning to improve self-attention. International Journal of Information Technology, Volume 15, pages 3015–3023, (2023). Available here.

Hugo Neri, Fabio G. Cozman Who Killed the Winograd Schema Challenge? Intelligent Systems: 12th Brazilian Conference, September 25–29, 2023, Proceedings - Part III, pp. 211–225, Springer 2023. Available at https://doi.org/10.1007/978-3-031-45392-2_14; preprint available.

Marcos Menon José, Marcelo Archanjo José, Denis D. Maua, Fabio G. Cozman. Integrating question answering and text-to-SQL in Portuguese. 15th International Conference on Computational Processing of the Portuguese Language (PROPOR 2022), pp. 278-287, 2022. Preprint available.

Yan Vianna Sym, João Gabriel Moura Campos, Fabio Gagliardi Cozman. An automated journalist covering the Blue Amazon. International Natural Language Generation Conference (INLG), pp. 1-3, 2022. Preprint available.

Paulo Pirozelli, Ais B. R. Castro, Ana Luiza C. de Oliveira, André Seidel Oliveira, Flávio Nakasato Cação, Igor C. Silveira, João Gabriel Moura Campos, Laura C. Motheo, Leticia F. Figueiredo, Lucas F. A. O. Pellicer, Marcelo Archanjo José, Marcos Menon José, Pedro de M. Ligabue, Ricardo S. Grava, Rodrigo M. Tavares, Vinicius B. Matos, Yan Vianna Sym, Anna Helena Reali Costa, Anarosa A. F. Brandão, Denis D. Maua, Fabio Gagliardi Cozman, Sarajane M. Peres. The BLue Amazon Brain (BLAB): A modular architecture of services about the Brazilian Maritime Territory. JCAI Workshop: AI Modeling Oceans and Climate Change (AIMOCC 2022), pp. 1-11, 2022. Preprint available.

Pedro de M. Ligabue, Anarosa A. F. Brandão, Sarajane M. Peres, Fabio Gagliardi Cozman, Paulo Pirozelli. BlabKG: a Knowledge Graph for the Blue Amazon. IEEE International Conference on Knowledge Graph (ICKG), pp. 1-8, 2022. Preprint available.

Paulo Pirozelli, Anarosa A. F. Brandão, Sarajane M. Peres, Fabio Gagliardi Cozman. To Answer or not to Answer? Filtering questions for QA systems. Brazilian Conference on Intelligent Systems (BRACIS), v. 2. p. 464-478, 2022. Preprint available.

André F. A. Paschoal, Paulo Pirozelli, Valdinei F. da Silva, Karina Valdivia Delgado, Sarajane M. Peres, Marcos Menon José, Flávio Nakasato Cação, André Seidel Oliveira, Anarosa A. F. Brandão, Anna Helena Reali Costa, Fabio Gagliardi Cozman. Pirá: A bilingual Portuguese-English dataset for question-answering about the ocean. Proceedings of CIKM, pp. 1-10, 2021. Preprint available.

Vinicius Cleves de Oliveira Carmo, Vinicius Toquetti de Melo, Flavio Pol Gonçalves, Rodrigo da Silva Cunha, Ismael Ferreira dos Santos, Rodrigo Augusto Barreira, Fabio Gagliardi Cozman, Edson Satoshi Gomi. Comparing statistical and neural question answering in Offshore Enginnering. 42nd Ibero-Latin-American Congress on Computational Methods in Engineering (XLII CILAMCE), pp. 1-7, 2021. Preprint available.

Marcelo Archanjo José, Fabio Gagliardi Cozman. mRAT-SQL+GAP: A Portuguese Text-to-SQL transformer. Brazilian Conference on Intelligent Systems, v. 2. pp. 511-525, 2021. Preprint available.

Flávio Nakasato Cação, Marcos Menon José, André Seidel Oliveira, Stefano Spindola, Anna Helena Reali Costa, Fabio Gagliardi Cozman. DEEPAGÉ: Answering questions in Portuguese about the Brazilian environment. Brazilian Conference on Intelligent Systems, v. 2, pp. 419-433, 2021. Preprint available.

Fabio G. Cozman, Hugo Neri. The Winograd schemas from hell. Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), pp. 531-542, 2020. Preprint available.

André Luiz Rosa Teixeira, João Gabriel Moura Campos, Rossana Cunha, Thiago Castro Ferreira, Adriana Silvino Pagano, Fabio G. Cozman. DaMata: A robot-journalist covering the Brazilian Amazon deforestation. International Conference on Natural Language Generation, pp. 103-106, 2020. Preprint available.

Alvaro H. C. Correia, Jorge L. M. Silva, Thiago C. Martins, Fabio G. Cozman. A Fully Attention Based Information Retriever. International Joint Conference on Neural Networks (IJCNN), pp. 2799-2806, 2018. Preprint available.

Emerson Cruz, Fabio G. Cozman, Wilson Souza, Albertina Takiuti. The impact of teenage pregnancy on school dropout in Brazil: a Bayesian network approach. BMC PUBLIC HEALTH, 21:1850, 2021. Preprint available.

Denis D. Maua, Fabio G. Cozman. Fast local search methods for solving limited memory influence diagrams. International Journal of Approximate Reasoning, 68:230-245, 2016. Preprint available. (This is an extended version of a paper that appeared at PGM2014, invited by the editors.)

F. T. Ramos, F. G. Cozman. Anytime anyspace probabilistic inference, International Journal of Approximate Reasoning, 38:53-80, 2005. Preprint available.

F. G. Cozman. Axiomatizing Noisy-OR, Technical Report from Escola Politecnica da USP, BT/PMR/0409, 2004. This report is an extended version of paper presented at the European Conference on Artificial Intelligence 2004.

J. S. Ide, F. G. Cozman, F. T. Ramos. Generating random Bayesian networks with constraints on induced width, European Conference on Artificial Intelligence (ECAI), pp. 323-327, IOS Press, Amsterdan, 2004. Preprint available.

F. G. Cozman. Generalizing Variable Elimination in Bayesian Networks, Proceedings of the IBERAMIA/SBIA 2000 Workshops (Workshop on Probabilistic Reasoning in Artificial Intelligence), pp. 27-32, Editora Tec Art, Sao Paulo, Brazil, 2000. Preprint available.

F. G. Cozman, I. Cohen, Risks of semi-supervised learning, in Olivier Chapelle, Bernhard Scholkopf, Alexander Zien (editors), Semi-Supervised Learning, pp. 55-70, 2006. Preprint available.

N. Sebe, I. Cohen, F. G. Cozman, T. Gevers, T. S. Huang. Learning probabilistic classifiers for human-computer interaction applications, Multimedia Systems, 10(6):484-498, 2005. Preprint available.

I. Cohen, F. G. Cozman, N. Sebe, M. C. Cirelo, T. S. Huang. Semisupervised learning of classifiers: Theory, algorithms, and their application to human-computer interaction, IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(12):1553-1568, 2004. Preprint available.

F. G. Cozman, I. Cohen, M. C. Cirelo. Semi-supervised learning of mixture models, International Conference on Machine Learning, pp. 99-106, 2003. Preprint available.

I. Cohen, N. Sebe, F. G. Cozman, M. C. Cirelo, T. S. Huang. Learning Bayesian network classifiers for facial expression recognition using both labeled and unlabeled data, IEEE Conference on Computer Vision and Pattern Recognition, 2003. Preprint available.

Fabio G. Cozman. Playing with Sets of Lexicographic Probabilities and Sets of Desirable Gambles. In: Thomas Augustin; Fabio G. Cozman; Gregory Wheeler, Reflections on the Foundations of Probability and Statistics: Essays in Honor of Teddy Seidenfeld, p. 143-159, Springer, 2023. (This paper supercedes, and improves, material published in ISIPTA 2015..

Fabio G. Cozman. Graphoid properties of concepts of independence for sets of probabilities. International Journal of Approximate Reasoning, 131:56-79, 2021. Preprint available. (This paper is a much extended version of a previous paper in ISIPTA 2019.

Fabio G. Cozman Evenly convex credal sets. International Journal of Approximate Reasoning, 103:124-138, 2018. Preprint available. (This paper supercedes material published in ISIPTA2017.

Fabio G. Cozman. Imprecise and indeterminate probabilities. In: Alan Hajek and Christopher Hitchcock (editors), The Oxford Handbook of Probability and Philosophy (ISBN 9780199607617), pp. 296-311, 2016.

Fabio G. Cozman. Learning imprecise probability models: Conceptual and practical challenges. International Journal of Approximate Reasoning, 55:1594-1596, 2014. Preprint available.

Fabio G. Cozman, Cassio Polpo de Campos. Kuznetsov independence for interval-valued expectations and sets of probability distributions: properties and algorithms. International Journal of Approximate Reasoning, 55:666-682, 2014. (DOI: 10.1016/j.ijar.2013.09.013). Preprint available. (This paper supercedes old (and obsolete!) papers on Kuznetsov independence, at ISIPTA2001 and ISIPTA2003; the latter paper contains problems described in the errata.)

Fabio G.Cozman. Independence for sets of full conditional measures, sets of lexicographic probabilities, and sets of desirable gambles. International Symposium on Imprecise Probability: Theories and Applications, pp. 87-98, 2013. Preprint available.

Fabio G. Cozman. Sets of probability distributions, independence, and convexity. Synthese, 186(2):577-600, 2012. Preprint available.

Fabio Gagliardi Cozman. Concentration inequalities and laws of large numbers under epistemic and regular irrelevance. International Journal of Approximate Reasoning, 51:1069-1084, 2010. Preprint available. (This is an extended version of a paper presented at ISIPTA2009.)

C. Polpo de Campos, F. G. Cozman. Computing lower and upper expectations under epistemic independence, International Journal of Approximate Reasoning, 44(3):244-260, 2007. Preprint available.

P. Vicig, M. Zaffalon, F. G.Cozman. Notes on "Notes on conditional previsions", International Journal of Approximate Reasoning, 44(3):358-365, 2007. Preprint available.

F. G. Cozman, P. Walley. Graphoid properties of epistemic irrelevance and independence, Annals of Mathematics and Artificial Intelligence, 45:173-195, 2005. Preprint available.

F. G. Cozman. Computing posterior upper expectations, International Journal of Approximate Reasoning, vol. 24, pp. 191-205, 2000. Preprint available; note the errata for this paper!

F. G. Cozman. Calculation of Posterior Bounds Given Convex Sets of Prior Probability Measures and Likelihood Functions, Journal of Computational and Graphical Statistics, vol. 8(4), pp. 824-838, 1999. Preprint available. note the errata for this paper!

F. Cozman and L. Chrisman. Learning Convex Sets of Probability from Data, Technical Report CMU-RI-TR-97-25, Robotics Institute, Carnegie Mellon University, Pittsburgh, 1997.

F. Cozman. An Informal Introduction to Quasi-Bayesian Theory (and Lower Probability, Lower Expectations, Choquet Capacities, Robust Bayesian Methods, etc...) for AI, Technical Report CMU-RI-TR-97-24, Robotics Institute, Carnegie Mellon University, Pittsburgh, 1997.

F. Cozman; E. Krotkov. Quasi-Bayesian Strategies for Efficient Plan Generation: Application to the Planning to Observe Problem, Proc. Twelfth Conference Uncertainty in Artificial Intelligence, pp. 186-193, 1996. Preprint available.

Radu Marinescu, Debarun Bhattacharjya, Junkyu Lee, Fabio Cozman, Alexander Gray. Credal Marginal MAP. Advances in Neural Information Processing Systems - NeurIPS, vol. 36, pp. 47804-47815, 2023. Available here; preprint available.

Fabio Gagliardi Cozman. Markov conditions and factorization in logical credal networks. Proceedings of the Thirteenth International Symposium on Imprecise Probability: Theories and Applications, PMLR 215:130-140, 2023. Available here; preprint available.

Denis D. Mauá, Fabio G. Cozman. Thirty years of credal networks: Specification, algorithms and complexity. International Journal of Approximate Reasoning, 126:133-157, 2020. Preprint available.

Denis D. Maua, Diarmaid Conaty, Fabio G. Cozman, Katja Poppenhaeger, Cassio Polpo de Campos. Robustifying sum-product networks. International Journal of Approximate Reasoning, 101:163-180, 2018. Preprint available. (This paper extends material published in ISIPTA2017.

Fabio G. Cozman, Denis D. Maua. On the complexity of propositional and relational credal networks. International Journal of Approximate Reasoning, 83:298-319, 2017. Preprint available. (This is an extended version of a paper published at ISIPTA2015.)

C. P. de Campos and F. G. Cozman. Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks. Conference on Advances in Artificial Intelligence (AAAI), pp. 217-223, 2013. Preprint available.

Jaime Shinsuke Ide, Fabio G. Cozman. Approximate algorithms for credal networks with binary variables, International Journal of Approximate Reasoning, v. 48, p. 275-296, 2008. Preprint available.

Cassio Polpo de Campos, Fabio Gagliardi Cozman. Inference in credal networks through integer programming, Fifth International Symposium on Imprecise Probability: Theories and Applications, pp. 145-154, Prague, Czech Republic, 2007. Preprint available.

A. Antonucci, M. Zaffalon, J. Ide, F. G. Cozman. Binarization algorithms for approximate updating in credal nets. In L. Penserini, P. Peppas, A. Perini, eds., Proceedings of the Third European Starting AI Researcher Symposium, pp. 120-131, Amsterdam, The Netherlands, IOS Press, 2006.

F. G.Cozman. Graphical models for imprecise probabilities, Journal of International Journal of Approximate Reasoning, 39(2-3):167-184, 2005. Preprint available.

J. C. F. da Rocha, F. G. Cozman. Inference in credal networks: branch-and-bound methods and the A/R+ algorithm, International Journal of Approximate Reasoning, 39(2-3):279-296, 2005. Preprint available.

C. P. de Campos, F. G. Cozman. Belief updating and learning in semi-qualitative probabilistic networks, Conference on Uncertainty in Artificial Intelligence (UAI), pp. 153-160, Edinburgh, United Kingdom, 2005. Preprint available.

C. P. de Campos, F. G. Cozman. The inferential complexity of Bayesian and credal networks, International Joint Conference on Artificial Intelligence, pp. 1313-1318, Edinburgh, United Kingdom, 2005. Preprint available.

F. G. Cozman, C. P. de Campos, J. S. Ide, J. C. F. da Rocha. Propositional and relational Bayesian networks associated with imprecise and qualitative probabilistic assessments, Conference on Uncertainty in Artificial Intelligence, pp. 104-111, AUAI Press, 2004. Preprint available.

C. P. de Campos, F. G. Cozman. Inference in credal networs using multilinear programming, Second Starting AI Researcher Symposium (STAIRS), pp. 50-61, IOS Press, 2004. Preprint available.

J. C. F. da Rocha, F. G. Cozman, C. P. de Campos. Inference in polytrees with sets of probabilities, Conference on Uncertainty in Artificial Intelligence, pp. 217-224, Morgan Kaufmann, 2003. Preprint available.

F. G. Cozman. Credal networks, Artificial Intelligence Journal, vol. 120, pp. 199-233, 2000. Preprint available.

Gregory Wheeler, Fabio G. Cozman. On the imprecision of full conditional probabilities. Synthese, 199;3761-3782, 2021. Preprint available.

Fabio G. Cozman. Independence for full conditional probabilities: Structure, factorization, non-uniqueness, and Bayesian networks. International Journal of Approximate Reasoning, 54:1261-1278, 2013. Preprint available.

Fabio G. Cozman, Teddy Seidenfeld. Independence for full conditional measures and their graphoid properties. Foundations of the Formal Sciences VI, Reasoning about Probabilities and Probabilistic Reasoning pp. 1-29, College Publications, London, 2009. Preprint available.

Fabio G. Cozman, Lucas F. di Ianni. Probabilistic satisfiability and coherence checking through integer programming. International Journal of Approximate Reasoning, 58:57--70, 2015. Preprint available. (This is an extended version of a paper that appeared at ECSQARU2013, selected as one of the four best papers in the conference.)

Glauber De Bona, Fabio G. Cozman, Marcelo Finger. Generalized probabilistic satisfiability through integer programming. Journal of the Brazilian Computer Society, 21:11, 2015. Preprint available. (This is an extended version of a paper that appeared at BRACIS2013, selected as Second Best Paper in the conference.)

Glauber de Bona, Fabio G, Cozman, Marcelo Finger. Towards classifying propositional probabilistic logics. Journal of Applied Logic, 12(3):349-368, 2014. Preprint available.

Fabio G. Cozman, Cassio Polpo de Campos, Jose Carlos Ferreira da Rocha. Probabilistic logic with independence, International Journal of Approximate Reasoning, v. 49, p. 3-17, 2008. Preprint available.

Thiago P. Bueno, Denis D. Maua, Leliane N. de Barros, Fabio G. Cozman. Modeling Markov Decision Processes with imprecise probabilities using probabilistic logic programming. Int. Conf. on Imprecise Probabilities: Theories and Applications (Proc. of Machine Learning Research vol. 62), pp. 49-60, 2017. Paper available.

Karina Valdivia Delgado, Leliane Nunes de Barros, Fabio Gagliardi Cozman, Scott Sanner. Using mathematical programming to solve Factored Markov Decision Processes with Imprecise Probabilities. International Journal of Approximate Reasoning, p. 200, 2011. Preprint available.

Daniel Kikuti, Fabio G. Cozman, Ricardo Shirota Filho. Sequential decision making with partially ordered preferences. Artificial Intelligence, 175(7-8):1346-1365, 2011. Preprint available.

Karina Valdivia Delgado, Scott Sanner, Leliane Nunes de Barros, Fabio G. Cozman. Efficient solutions to factored MDPs with imprecise transition probabilities, 19th International Conference on Automated Planning and Scheduling, pp. 98-105, Thessaloniki, Greece, 2009. Preprint available.

Felipe W. Trevizan, Fabio G. Cozman, Leliane N. de Barros. Mixed Probabilistic and Nondeterministic Factored Planning through Markov Decision Processes with Set-Valued Transitions, Workshop on A Reality Check for Planning and Scheduling Under Uncertainty at the Eighteenth International Conference on Automated Planning and Scheduling (ICAPS), 2008. Preprint available.

Felipe W. Trevizan, Fabio G. Cozman, Leliane N. de Barros. Planning under Risk and Knightian Uncertainty, International Joint Conference on Artificial Intelligence, pp. 2023-2028, 2007. Preprint available.

Ricardo Shirota Filho, Fabio Gagliardi Cozman, Felipe Werndl Trevizan, Cassio Polpo de Campos, Leliane Nunes de Barros. Multilinear and integer programming for Markov decision processes with imprecise probabilities, Fifth International Symposium on Imprecise Probability: Theories and Applications, pp. 395-404, Prague, Czech Republic, 2007. Preprint available.

D. Kikuti, F. G. Cozman, C. P. de Campos. Partially ordered preferences in decision trees: computing strategies with imprecision in probabilities, IJCAI Workshop on Advances in Preference Handling, Edinburgh, United Kingdom, 2005. Preprint available. PLEASE also note that an errata has been produced, correcting a few mistakes in the paper!

Sarah Pires Perez, Fabio Gagliardi Cozman. How to generate synthetic paintings to improve art style classification. Brazilian Conference on Intelligent Systems, v. 2. pp. 238-253, 2021. Preprint available.

José Amêndola, Lucas S. Miura, Anna H. Reali Costa, Fabio G. Cozman, Eduardo A. Tannuri. Navigation in restricted channels under environmental conditions: Fast-time simulation by asynchronous deep reinforcement learning. IEEE Access, 8:149199-149213, 2020. Preprint available.

Didiana Prata, Fabio G. Cozman, Gustavo Padilha Polleti. Using AI to classify Instagram's dissident images Conference of the International Committee of Design History and Design Studies, pp. 1-14, 2020. Preprint available.

Paulo E. Santos, Murilo F. Martins, Valquiria Fenelon, Fabio G. Cozman, Hannah M. Dee. Probabilistic self-localisation on a qualitative map based on occlusions. Journal of Experimental and Theoretical Artificial Intelligence, 2016. Preprint available.

Valdinei Freire, Flavio S. Truzzi, Anna H. Reali Costa, Fabio G. Cozman. Evaluation of linear relaxations in Ad Network optimization for online marketing. Journal of the Brazilian Computer Society, 21:13, 2015. Preprint available. (This is an extended version of a paper that appeared at BRACIS2013, selected as Third Best Paper in the conference.)

Valquiria Fenelon, Paulo E. Santos, Hannah M. Dee and Fabio G. Cozman. Reasoning about shadows in a mobile robot environment. Applied Intelligence, 38(4):553-565, 2013. (DOI: 10.1007/s10489-012-0385-5) Preprint available.

Rafael A. M. Goncalves, Diego R. Cueva, Marcos R. Pereira-Barretto, Fabio G. Cozman. A model for inference of emotional state based on facial expressions. Journal of the Brazilian Computer Society, vol. 19, pp. 3-13, 2013. (DOI: 10.1007/s13173-012-0081-7) Preprint available. (This is an extended version of a paper presented at ENIAC2011, selected as one of the three best papers in that meeting.)

Marcelo Li Koga, Valdinei Freire da Silva, Fabio Gagliardi Cozman, Anna Helena Reali Costa. Speeding-up reinforcement learning through abstraction and transfer learning. International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2013. Preprint available.

Diego R. Cueva, Rafael A. M. Goncalves, Fabio Gagliardi Cozman, Marcos R. Pereira-Barretto. Crawling to improve multimodal emotion detection, Advances in Artificial Intelligence - 10th Mexican International Conference on Artificial Intelligence (MICAI2011), Lecture Notes in Artificial Intelligence 7095, Part II, pp. 343-350, Springer, 2011. Preprint available.

Rodrigo B. Polastro, Fabiano E. Correa, Fabio G. Cozman, J. Okamoto Jr. Semantic mapping with a probabilistic description logic. Lecture Notes in Artificial Intelligence, volume 6404, Advances in Artificial Intelligence - SBIA 2010, pp. 62-71, 2010. Preprint available.

Fabiano Correa, Rodrigo Bellizia Polastro, Fabio Gagliardi Cozman, Jun Okamoto Junior. Dealing with semantic knowledge in robotics with a probabilistic description logic. ASAI 2010 - XI Argentine Symposium on Artificial Intelligence, p. 1-12, 2010. Preprint available.

Marko Ackermann, Fabio Gagliardi Cozman. Automatic knee flexion in lower limb orthoses. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 31(4):305-311, 2009. Preprint available.

F. G. Cozman, E. Krotkov, C. E. Guestrin. Outdoor Visual Position Estimation for Planetary Rovers, Autonomous Robots, vol. 9, pp. 135-150, 2000. Preprint available.

C. Guestrin; F. G. Cozman; E. Krotkov. Fast Software Image Stabilization with Color Registration, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 19-24, Victoria, Canada, October, 1998.

F. Cozman; E. Krotkov. Depth from Scattering, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico, June, 1997. Preprint available.

R. Simmons; E. Krotkov; L. Chrisman; F. Cozman; R. Goodwin; M. Hebert; L. Katragadda; S. Koenig; G. Krishnaswamy; Y. Shinoda; W. Whittaker; and P. Klarer. Experience with Rover Navigation for Lunar-Like Terrains, Proceedings of the Conference on Intelligent Robots and Systems (IROS), pages 441-446, 1995.

F. Cozman; E. Krotkov. Robot Localization using a Computer Vision Sextant, International Conference on Robotics and Automation, pages 106-111, Nagoya, Japan, May 1995. Preprint available.

F. Cozman; E. Krotkov. Truncated Gaussians as Tolerance Sets, Fifth Workshop on Artificial Intelligence and Statistics, Fort Lauderdale Florida, 1995. Preprint available.

F. G. Cozman; P. E. Miyagi. Trajectory Controller for a Mobile Robot using Optimal Control, XI Congresso Brasileiro de Engenharia Mecânica, 3:537-540, Sao Paulo, SP Brazil, 1991.