OPtiMaL - Online Procedural Content Generation via Multi-objective Optimisation and Learning

About the Project

OPtiMaL (Online Procedural Content Generation via Multi-objective Optimisation and Learning) aims to introduce generative AI algorithms that are capable of creating multimodal content with multi-dimensional diversity, which is aware of human behaviour and adapts to human experience. OPtiMaL adopts a new multi-objective optimisation and learning approach to procedural content generation, which is generally applicable across many domains.

The approaches developed in OPtiMaL will be validated initially on digital games with direct applications to domains that involve the simulation of human behaviour including construction, manufacturing, education, and autonomous driving. OPtiMaL is a collaborative project between one of the leading centres of Al in games research worldwide - Institute of Digital Games, Malta - and one of the world's leading research groups in computational intelligence - SUSTech, China.

One of the long-term goals of the project is to position Malta centrally on the global Al research map and place China as a global leader in Al in digital games.

Partners

The project is spearheaded by the Institute of Digital Games, Malta and SUSTech, China.

Project OPtiMaL received funding from the Malta Council for Science and Technology (MCST) and the Ministry for Science and Technology of the People’s Republic of China (MOST), through the SINO-MALTA Fund 2022 (Science and Technology Cooperation).

Events

13-04-2024 | Workshop on Computational Intelligence and Games

The recent workshop on Computational Intelligence and Games, held on April 13, 2024, at Southern University of Science and Technology (SUSTech) in Shenzhen, was a resounding success. Organised by one of OPtiMaL's coordinators, Jialin Liu the workshop brought together over 50 participants, including leading researchers, students, and industry professionals in the field of AI and games.

It was an honor to host H.E. John Busuttil, Ambassador of Malta to China, who delivered an insightful address that added depth to the discussions and highlighted the importance of the ongoing collaboration in AI research. The workshop was filled with engaging sessions and keynote presentations from experts such as Georgios N. Yannakakis, Jichen Zhu, Mike Preuss, and others. The involvement of Tencent, one of the largest gaming companies, brought an industry perspective to the event, further enriching the discourse on AI and gaming.

Held at the College of Engineering Building at SUSTech, China, the OptiMal funded workshop was supported by several prestigious organizations, including the Department of Computer Science and Engineering (CSE) and the Research Institute of Trustworthy Autonomous System (RITAS), with backing from the IEEE Computational Intelligent Society (CIS). Attendees had the opportunity to network, discuss recent advancements, and explore future directions in AI and games. For more information about the workshop, visit https://aingames.cn/CIG2024/.

17-01-2024 | Professor Georgios Yannakakis Takes OptiMaL to South America

Professor Yannakakis's recent visit to South America served as a dual opportunity – not only did it allow him to share the exciting developments of the OPtiMaL Project, but it also fostered collaboration and knowledge exchange with experts in the region. A significant highlight of his journey was his invited talk at "Congreso Futuro" the largest popular science conference in Latin America. This prestigious event annually convenes visionaries, scientists, and thought leaders to delve into the future of science, technology, and innovation. Serving as a unique platform, Congreso Futuro enables the dissemination of groundbreaking research and ideas to a diverse and engaged audience. During his captivating presentation, Professor Yannakakis unveiled the aims and achievements of the OPtiMaL Project, alongside other insightful topics, offering a glimpse into the forefront of AI innovation.

03-11-2023 | Innovation Through Collaboration: OPtiMaL Project's Research Meeting at IDG

Our IDG Board Room buzzed with innovation, hosting the bright minds students from Sustech. Following our kick-off meeting, we continued to advance the realm of AI through multi-objective optimization and learning as part of the Sino-Malta Fund 2022 initiative.

24-10-2023 | Project OPtiMaL Kick-Off Meeting

The kick-off meeting of the Sino-Malta Project OPtiMaL occurred at the Institute of Digital Games - University of Malta with visiting partners Dr Jialin Liu and Prof Xin Yao from Southern University of Science and Technology (SUSTech)! We launched our brand new website and discussed ways towards realising online and multimodal procedural content generators! Stay tuned from more news and developments for the next two years!

23-10-2023 | Innovative Horizons in AI & Games: IEEE Workshop

Co-organized by the IEEE Computational Intelligence Society and the Institute of Digital Games - University of Malta this event was a melting pot of AI and Computational Intelligence enthusiasts and experts. A diverse range of topics explored, from AI/CI evolution, academia’s AI strategies, to the latest in Deep Attention-Based Models in Astronomy and Medicine, and the future of Multi-Objective Evolutionary Optimization. Lively discussions and networking opportunities that sparked innovative ideas and potential collaborations.

"In a significant stride towards advancing artificial intelligence research and development, the University of Malta proudly hosted two landmark events this October: the IEEE Workshop on AI and Computational Intelligence and the kick-off meeting for the ground-breaking OptiMaL project..."
Newspoint, University of Malta
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Coordinators

Prof. Georgios N. Yannakakis
University of Malta
Institute of Digital Games
georgios.yannakakis@um.edu.mt

Dr. Jialin LIU
Southern University of Science and Technology
Department of Computer Science and Engineering
liujl@sustech.edu.cn

Published Papers

Li, H., Zhou, J., & Zhang, Q. (2024). Rolling horizon co-evolution for snake AI competition. In Intelligent Information Processing XII (pp. 260–274). Cham: Springer Nature Switzerland.
Wang, Z., Hu, C., Liu, J., & Yao, X. (2024). Negatively Correlated Ensemble Reinforcement Learning for Online Diverse Game Level Generation. International Conference on Learning Representations (ICLR).
Zhang, Q., Liu, J., & Yao, X. (2024). An efficient many objective optimization algorithm with few parameters. Swarm and Evolutionary Computation, 83(1).

Zhao, Y., Hu, C., & Liu, J. (2024). Playing with Monte-Carlo Tree Search. IEEE Computational Intelligence Magazine, 19(1).

Yuan, B., Gui, S., Zhang, Q., Wang, Z., Wen, J., Mao, B., Liu, J., & Yao, X. (2024). FairerML: An Extensible Platform for Analysing, Visualising, and Mitigating Biases in Machine Learning. IEEE Computational Intelligence Magazine.

Wang, Z., Li, Y., Du, H., Liu, J., & Yannakakis, G. N. (2024). Fun As Moderate Divergence: Evaluating Experience-Driven PCG via RL. IEEE Transactions on Games (under review).

Pei, J., Liu, J., & Mei, Y. (2024). Learning from Offline and Online Experiences: A Hybrid Adaptive Operator Selection Framework. In Genetic and Evolutionary Computation Conference.

Zhang, H., Li, Q., & Yao, X. (2024). An Adaptive Interactive Routing-Packing Strategy for Split Delivery Vehicle Routing Problem with 3D Loading Constraints. In Genetic and Evolutionary Computation Conference.

Zhang, H., Li, Q., & Yao, X. (2024). PEACH: A Multi-Objective Evolutionary Algorithm for Complex Vehicle Routing with Three-Dimensional Loading Constraints. In Genetic and Evolutionary Computation Conference.

Nan, Y., Ishibuchi, H., Shu, T., & Shang, K. (2024). Analysis of Real-World Constrained Multi-Objective Problems and Performance Comparison of Multi-Objective Algorithms. In Genetic and Evolutionary Computation Conference.

Nan, Y., Ishibuchi, H., Shu, T., & Shang, K. (2024). Gradient-Guided Local Search for Large-Scale IGD/IGD+ Subset Selection. In Genetic and Evolutionary Computation Conference.

Nan, Y., Shu, T., & Ishibuchi, H. (2024). Performance Evaluation of Evolutionary Multi-Objective Algorithms using Real-World Problems with an Additional Total Constraint Violation Objective. In IEEE Congress on Evolutionary Computation.

Xia, Q., Wang, J., Huang, C., & Yao, X. (2024). Reliability-aware Network Slicing based on Multi-objective Optimization. In IEEE Congress on Evolutionary Computation.

Qu, L., Wang, C., & Shi, Y. (2024). Brain Storm Optimization Based Swarm Learning for Diabetic Retinopathy Image Classification. In IEEE Congress on Evolutionary Computation.

Resources

Project OPtiMaL Flyer

The OPtiMaL flyer contains information about the project’s concept, objectives and partners.