Slides from the Seminar on Artificial Intelligence and Logics (SNAIL)

Slides are named according to the date they took place. Below is a more detailed list of seminars.

• 2023_03_31: Escalonando Disciplinas por Programação Lógica
  • Ana Yoon Faria de Lima, Briza Mel Dias de Sousa, Bruno Vianna Bitelli, Daniel Pessoa Cardeal, Jessica Yumi Nakano Sato, Lorenzo Bertin Salvador
  Abstract

  Em nossa apresentação, vamos discutir como usamos a linguagem lógica ASP Potassco para implementar um escalonador inteligente de disciplinas para o departamento de Ciência da Computação da USP. Um escalonador adequado é um programa que gera boas grades horárias, dadas algumas informações sobre as disciplinas e professores. A formulação lógica do problema é desafiadora, já que exige a existência simultânea de regras rígidas que delimitem a estrutura válida de uma grade horária e as preferências abstratas de qualidade dos alunos e professores. Para lidar com a diversidade semântica das regras, o grupo utilizou de maneira inteligente os sistemas de otimização de ASP em conjunto com rotinas próprias de testes unitários, o que levou a resultados tão bons quanto as soluções criadas manualmente pelo departamento.
• 2023_04_18: Machine Learning for Graphs and Some Applications to Polymer Science
  • David Kohan Marzagão
    Bio

    David is currently a Lecturer in Artificial Intelligence at King’s College London. He also works with David Clifton and Clive Siviour at the IBME, University of Oxford, in investigating how machine learning techniques can help finding structure-property relationships in the context of polymer design. At Oxford, he is currently a college lecturer in mathematics and statistics at Chirst Church. Before that, David completed a PhD in Computer Science at King’s under the supervision of Peter McBurney and Kathleen Steinhöfel following a first degree in pure mathematics at the University of São Paulo, Brazil. His research interests include multi-agent systems, machine learning, provenance, graph kernels, and stochastic processes.
  Abstract

  In this talk, we will discuss how to compare graphs using graph kernel methods. In a nutshell, even deciding whether two graphs are isomorphic is not trivial, let alone determining a degree of similarity. We will discuss some common techniques, and then apply those to the analysis and design of polymers in chemistry. We will also discuss how to create explanations of decisions based on such algorithms.
• 2023_05_05: Desvendando o ChatGPT
  • Bruna Bazaluk
    Bio

    Bacharela em Ciência da Computação pelo IME-USP e mestranda em Ciência da Computação no IME-USP no Laboratório de Lógica, Inteligência Artificial e Métodos Formais.
  ChatGPT, o famoso chat que sabe responder qualquer coisa! Será mesmo? Mas como ele funciona? Quais suas limitações? A IA vai dominar o mundo? Neste seminário tentarei responder algumas dessas perguntas dando intuições de como o ChatGPT funciona, mostrar exemplos ao vivo de quando ele não responde como esperado e discutir seus possíveis efeitos na sociedade.
• 2023_08_25: Thinking with Circuits: From Logic to Probabilistic and Back
  • Renato Lui Geh
    Bio

    Renato received his BSc and MSc in Computer Science at the University of São Paulo and is currently an incoming PhD student at the University of California, Los Angeles (UCLA).
  We explore the world of logic and probabilistic circuits from the basics, showing how circuits are able to unify logic and probabilistic reasoning under a single framework. Our journey starts from the century-old origins of logic circuits, rooted in the foundations of Computer Science and knowledge representation, and ends in state-of-the-art applications of probabilistic circuits in neurosymbolic machine learning and reasoning. No background besides basic understanding of logic and probabilities is needed to follow this talk.
• 2023_09_01:
  • Renato Lui Geh
    Bio

    Renato received his BSc and MSc in Computer Science at the University of São Paulo and is currently an incoming PhD student at the University of California, Los Angeles (UCLA).
  dPASP is a new declarative programming language based around probabilistic-logic programming. The idea behind dPASP is to provide an intuitive and flexible language for neurosymbolic learning. In a nutshell, by combining the high-level reasoning of probabilistic and logic programming, with the low-level perception of neural networks, dPASP offers a powerful hybrid toolbox for inference and learning in a wide variety of possible semantics, enabling the presence of contradictions and imprecision within the knowledge base. In this talk, we provide a high-level introduction to dPASP, showcasing features of the dPASP system. We then show the current challenges and possible ideas for further research in the field.