Proposte

There are several possible projects on disparate topics, categorised below. Roughly, each first-level dot corresponds to a project.

Argumentation in MAS

  • Explore distribution of argumentative processes.

Fairness of AI

  • Charting the Path to Ethical Excellence: Navigating Fair-by-Design Architecture for MAS
  • Fairness in Cooperative Multi-Agent Systems: Examining the Impact of Algorithmic Bias on Collaborative Decision-Making
  • Adversarial Learning for Fairness in Multi-Agent Reinforcement Learning: Mitigating Discrimination in Cooperative Environments
  • Fair Resource Allocation in Multi-Agent Systems: Balancing Efficiency and Equity
  • Exploring Fairness Trade-Offs in Multi-Agent Negotiation: An Investigation of Pareto Optimality and Social Welfare
  • Fairness-Aware Task Allocation in Multi-Agent Systems: Towards Equitable Workload Distribution and Outcome Optimization
  • Syntetich images generation and fair decision algorithms in skin disease prediction

Explainable AI

Symbolic Knowledge Extraction (SKE)

  • Design, prototype, and assess a novel symbolic knowledge extraction algorithm that supports recursive rules.

Symbolic Knowledge Injection (SKI)

  • Study, design, and implement the [Lyrics] symbolic knowledge injection algorithm. An implementation of Lyrics already exists but it is quite old and not mantained.
  • Study, design, and implement the [Fibred NN] symbolic knowledge injection algorithm.
  • Study, design, and implement the [GRAM] symbolic knowledge injection algorithm.
  • Study, design, and implement the [Knowledge-aware object detection] symbolic knowledge injection algorithm.
  • Study, design, and implement the [Semantic loss function] symbolic knowledge injection algorithm.
  • Design and implement the extension of the [PSyKI] library to support graph structured data and graph processing through deep learning frameworks (e.g., [Tensorflow-GNN]). Graph structured data must be supported both from the data domain (under the form of collections of graphs) and the knowledge domain (under the form of ontologies).
  • Design, prototype, and assess a novel knowledge base quality metric that defines the coverage of a given knowledge base w.r.t. a given dataset. The proposed metric should be integrated in the [PSyKI] library.
  • Design and implement the extension of the [PSyKI] library to support complex-structured datasets such as images, texts, etc. The extension should consider checking if the imported dataset type is compatible with the selected injection mechanism and its corresponding knowledge.

References

[Lyrics] Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Marco Gori. "LYRICS: A General Interface Layer to Integrate Logic Inference and Deep Learning"
[FIbred NN] Artur S. d'Avila Garcez, Dov M. Gabbay. "Fibring Neural Networks"
[GRAM] Choi, Edward, et al. "GRAM: graph-based attention model for healthcare representation learning." 
[Knowledge-aware object detection] Fang, Yuan, et al. "Object detection meets knowledge graphs."
[Semantic loss function] Xu, Jingyi, et al. "A semantic loss function for deep learning with symbolic knowledge."
[PSyKI] https://github.com/psykei/psyki-python
[Tensorflow-GNN] https://github.com/tensorflow/gnn