Luís C. Lamb DIC, PhD



Professor, Foundations of Computer Science and AI, Institute of Informatics - UFRGS  


This is Mostly an Academic Web Page
LinkedIn Profile Twitter
Research Interests
- Integrated Machine Learning & Reasoning. Neurosymbolic AI. 
- AI Ethics and Social Computing. 
- Innovation, Strategy, and Technology Management.

Short Bio My photograph

New: Neurosymbolic AI: The 3rd Wave: Artur Garcez and Luis Lamb. Artificial Intelligence Review 2023.

Information about my research, including a brief CV and recent publications.

Correspondence from Professor D.E. Knuth


Recent Talks on AI and Neurosymbolic AI:

- IBM Neuro-Symbolic AI Workshop 2022 - Unifying Statistical and Symbolic AI. 18 & 19 January, 2022. Invited talk and panel participation.

- Invited talk: A Short on the History and Evolution of Neurosymbolic AI, 18 Jan. 2022.

- The Perspectives in AI seminar of the Center for AI – USP-IBM-FAPESP: "Neurosymbolic AI: Building Robust AI Models", Feb. 15, 2022.

- The Neuro-Symbolic AI Panel @AAAI2021: Marta Kwiatkowska, Leslie Pack Kaelbling, Luis C. Lamb, Matthew Botvinick, Guy Van den Broeck, Kristian Kersting, Feb 7, 2021

- AI Debate #2: Moving AI Forward. Montréal AI, 23 December 2020. Vincent Boucher (org), Gary Marcus, Daniel Kahneman (Nobel Laureate), Celeste Kidd, Luis C. Lamb, Francesa Rossi, Fei-Fei Li, Richard Sutton, Judea Pearl, Kenneth Stanley, Yejin Choi, Barbara Tversky, Doris Tsao, Adam Marblestone, Robert Ness, Christof Koch, Margaret Mitchell, Ryan Calo. My remarks at the panel focused on Neurosymbolic AI are available here.

- Machine Learning Street Talk: #54 Gary Marcus and Luis Lamb - Neurosymbolic models – June 3, 2021.

Media:

AI Debate 2: Night of a thousand AI scholars

The future of A.I.: 4 big things to watch for in the next few years


Some online papers:

Artur Garcez, Luis Lamb: Neurosymbolic AI: The 3rd Wave. Artificial Intelligence Review, 2023.

Luís C. Lamb, Artur d'Avila Garcez, Marco Gori, Marcelo Prates, Pedro Avelar, Moshe Y. Vardi (2020) Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective. IJCAI 2020 - 29th International Joint Conference on Artificial Intelligence, July 2020.

Learning to Solve NP-Complete Problems: A Graph Neural Network for Decision TSP. Marcelo Prates, Pedro Avelar, Henrique Lemos, Luis C. Lamb and Moshe Y. Vardi. Proc. of AAAI-19 - 33rd AAAI Conference on Artificial Intelligence, Honolulu, Jan 27 – Feb 1, 2019.

Collaboration in Social Problem-Solving: When Diversity Trumps Network Efficiency. D.V. Noble; M.R. Prates, D.S. Bossle and Luis Lamb. Proc. 29th AAAI Conference on Artificial Intelligence AAAI-15, Austin, TX, Jan. 2015.

Lucas M. Tabajara, Marcelo Prates, Diego Noble and Luis C. Lamb. Leveraging Collaboration: A Methodology for the Design of Social Problem-Solving Systems. HCOMP 2013: AAAI Conference on Human Computation and Crowdsourcing. Palm Springs, CA, Nov. 6-9, 2013. (Social problem-solving; collaboration)

A Neural-Symbolic Cognitive Agent for Online Learning and Reasoning. L. de Penning; A.S. d'Avila Garcez, Luis C. Lamb and J.J. Ch. Meyer, Proc. IJCAI-11, Barcelona, July 16-22, 2011.(Neural-symbolic Computation; cognitive computation)

Memetic Networks: analyzing the effects of network properties in multi-agent performance. R.M. Araújo & L.C. Lamb, Proc. AAAI-08, pp. 3-8, Chicago, AAAI Press 2008).

A connectionist cognitive model for temporal synchronisation and learning. Luís C. Lamb; R.V Borges; A. d’Avila Garcez; Proc. AAAI-07, pp. 827-832, Vancouver CA, AAAI Press 2007. (Cognitive computation; temporal synchronization and learning in cognitive models)

An information-theoretic analysis of memory bounds in a distributed resource allocation mechanism. Ricardo M. Araújo & Luis C. Lamb; Proc. IJCAI-07, pp. 212-217, Hyderabad, AAAI Press 2007. (Memory/resource use in Minority Games)

A Connectionist Model for Constructive Modal Reasoning. Artur S. d'Avila Garcez, Luís C. Lamb, Dov M. Gabbay: NIPS 2005: 403-410

Reasoning about time and knowledge in neural-symbolic learning systems. A.d’Avila Garcez & Luis C. Lamb, Proc. NIPS 2003, pp. 921-928; Vancouver CA, MIT Press 2004. (Neural computation; cognitive reasoning; includes a full solution to the Muddy Children Puzzle)


Selected Papers on Neurosymbolic AI, Trustworthy AI, Integrated Learning & Reasoning:

Neurosymbolic AI: The 3rd Wave: Artur Garcez and Luis Lamb. ArtificiaI Intelligence Review, 2023.

Luis Lamb, A. d'Avila Garcez, Marco Gori, Marcelo Prates, Pedro Avelar, Moshe Y. Vardi (2020) Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective. IJCAI 2020 - current version arXiv preprint arXiv:2003.00330

Artur S. d'Avila Garcez, Marco Gori, Luís C. Lamb, Luciano Serafini, Michael Spranger, Son N. Tran: Neural-symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning. FLAP 6(4): 611-632 (2019)

Felipe Grando, Lisandro Granville and Luis C. Lamb. Machine Learning in Network Centrality Measures: Tutorial and Outlook. ACM Computing Surveys, 51(5), article 102, Oct 2018 (online) Jan 2019.

Marcelo Prates, Pedro Avelar and Luis C. Lamb: Assessing Gender Bias in Machine Translation -- A Case Study with Google Translate, Neural Computing and Applications, 2019. https://arxiv.org/abs/1809.02208 See media coverage in “The Register” here.

Marcelo Prates, Pedro Avelar and Luis Lamb. On Quantifying and Understanding the Role of Ethics in AI Research: A Historical Account of Flagship Conferences and Journals. 4th Global Conference on Artificial Intelligence, GCAI 2018. These results were used in the 2019 AI Index report - from the Human-Centered AI Institute at Stanford.

Rafael de Santiago, Luis Lamb. Efficient Modularity Density Heuristics for Large Graphs. European Journal of Operational Research, 258(3):844-865, 2017.

Rafael de Santiago and Luis C. Lamb. Exact Computational Solution of Modularity Density Maximization by Effective Column Generation. Computers and Operations Research, 2017.

T.R. Besold, A. d'Avila Garcez and Luis C. Lamb. Human-Like Neural-Symbolic Computing. Dagstuhl Reports 7(5):56-83, 2017.

R. da Silva, P. Valverde, Luis Lamb: Randomness and Arbitrary Coordination in the Reactive Ultimatum Game. Communications in Nonlinear Science and Numerical Simulation, 2016.

D.S. Farenzena, Luis Lamb, Ricardo Araujo: The cost of search and evaluation in online problem-solving social networks with financial and non-financial incentives. First Monday 21(8), 2016.

R. da Silva, Luis Lamb, M.C. Barbosa. Universality, correlations, and rankings in the Brazilian universities national admission examinations. Physica A, 2016.

A. S. d'Avila Garcez, Marco Gori, Pascal Hitzler, Luís C. Lamb: Neural-Symbolic Learning and Reasoning (Dagstuhl Seminar 14381). Dagstuhl Reports 4(9): 50-84, 2015.

A. d'Avila Garcez, D.M. Gabbay, Luis C. Lamb: A neural cognitive model of argumentation with application to legal inference and decision making. Journal of Applied Logic, 12(2):109-127, 2014.

Marcio Dorn, M. Barbachan e Silva, Luciana Buriol and Luis Lamb. Three-dimensional protein structure prediction: Methods and computational strategies. Computational Biology and Chemistry, Vol. 53, Part B:251–276,2014.

R. da Silva; M. Vainstein; L.C. Lamb; S. Prado. A simple non-Markovian computational model of the statistics of soccer leagues: Emergence and scaling effects. Computer Physics Communications, Vol.184(3):661-670, 2013.

Borges, d'Avila Garcez, Lamb. Learning and Representing Temporal Knowledge in Recurrent Networks. IEEE Transactions on Neural Networks and Learning Systems, vol 22(12):2409-2421, 2011.

da Silva, Zembrzurski, Correa, and Lamb: Stock Markets and Criticality in the Current Economic Crisis. Physica A: Statistical Mechanics and its Applications, v. 389, p. 5460-5467, 2010.

Araújo and Lamb: On the Use of Memory and Resources in Minority Games. ACM Transactions on Autonomous and Adaptive Systems 4(2):1-23, May 2009.

Da Silva; Kellermann;  Lamb: Statistical fluctuations in population bargaining in the ultimatum game: static and evolutionary aspects. Jnl. of Theoretical Biology,258(2):208-218,2009.

Borges;  d’Avila Garcez;  Lamb: A neural-symbolic perspective on analogy. Behavioral and Brain Sciences 2008, 371(4):379-380,(Cambridge Univ. Press link)

d’Avila Garcez; Lamb;  Gabbay: Connectionist Modal Logic: Representing Modalities in Neural Networks, Theor. Comput. Sci. 2007, 371(1-2):34-53, 2007(Elsevier link)

d’Avila Garcez; Lamb;  Gabbay: Connectionist computations of intuitionistic reasoning, Theoretical Computer Science 2006, 358(1):34-55.

d’Avila Garcez and Lamb: A Connectionist Computational Model for Epistemic and Temporal Reasoning, Neural Computation 2006, 18(7):1711-1738, please click here (MIT Press link)

d’Avila Garcez; Gabbay;  Lamb: Value-based argumentation frameworks as Neural-Symbolic Learning Systems, Jnl. Log. Comput.2005, 15(6):1041-1058 please click here (Oxford U.Press link)

Broda; Gabbay; Lamb; Russo: Labelled Natural deduction for conditional logics of normality. Logic Jnl. IGPL 2002 10(2):123-163 (Oxford Univ. Press link)


Book Neural-Symbolic Cognitive Reasoning by A.d’Avila Garcez, Luis Lamb and Dov Gabbay. Cognitive Technologies, Springer 2009. Click here for details.

Contatos (Contact details):

Dr Luís C. Lamb
Instituto de Informatica, UFRGS (Direct dial)/+55(51)3308 7019
Av. Bento Gonçalves 9500
91501-970
Porto Alegre, RS, Brazil