Combinatorial methods play a crucial role in artificial intelligence (AI), particularly in solving problems where discrete structures and decisions are involved. This workshop is focused on advanced combinatorial techniques and their applications in AI. Topics of interest include, but are not limited to: 

• Use of reinforcement learning, neural networks, and other AI methods to enhance combinatorial problem-solving

• Integration of machine learning techniques with combinatorial optimization

• Real-world applications of combinatorial methods in AI, including scheduling, resource allocation, network design, etc.

• Combinatorial optimization 

• Linear-integer programming

• Applications to statistics (e.g., feature selection)

• Heuristics

• Exact algorithms 

• Computational complexity  

• Parameterized complexity 

• Applications which include combinatorics

• Workflow development which includes a combinatorial component  

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We invite you to submit extended abstracts (two pages, excluding references) in PDF format through this link by August 20, 2024.

 

Contributions will be reviewed for quality and relevance to the workshop’s theme. Theoretical and applied papers and papers that capture best practices and lessons learned from field studies are encouraged. Submission of preliminary results would also be considered.

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Authors of accepted abstracts will present their work at the workshop either as regular talks or as posters.

 

The workshop organizers plan to invite the authors of distinguished extended abstracts to submit a full paper of their work in internationally renowned journals (SCI, EI, and/or Scopus indexed).

 

Workshop Organizers and Program Committee

Joseph Barr, Embry-Riddle Aeronautical University, USA
Faisal Abu-Khzam, Lebanese American University, Lebanon
Peter Shaw,  Chinese Academy Of Science, China