| Novelty Generation Framework for AI Agents in Angry Birds Style Physics Games C Gamage, V Pinto, C Xue, M Stephenson, P Zhang, J Renz 2021 IEEE Conference on Games (CoG), 1-8, 2021 | 16 | 2021 |
| Phy-Q as a measure for physical reasoning intelligence C Xue, V Pinto, C Gamage, E Nikonova, P Zhang, J Renz Nature Machine Intelligence 5 (1), 83-93, 2023 | 13* | 2023 |
| Science Birds Novelty: an Open-world Learning Test-bed for Physics Domains C Xue, V Pinto, P Zhang, C Gamage, E Nikonova, J Renz Proceedings of the AAAI Spring Symposium on Designing Artificial …, 2022 | 12 | 2022 |
| Measuring the Performance of Open-World AI Systems V Pinto, J Renz, C Xue, K Doctor, D Aha Proceedings of the AAAI Spring Symposium on Designing Artificial …, 2020 | 9 | 2020 |
| NovPhy: A Testbed for Physical Reasoning in Open-world Environments C Gamage, V Pinto, C Xue, P Zhang, E Nikonova, M Stephenson, J Renz arXiv preprint arXiv:2303.01711, 2023 | 3 | 2023 |
| The Difficulty of Novelty Detection in Open-World Physical Domains: An Application to Angry Birds V Pinto, C Xue, CN Gamage, J Renz arXiv preprint arXiv:2106.08670, 2021 | 2 | 2021 |
| Rapid Open-World Adaptation by Adaptation Principles Learning C Xue, E Nikonova, P Zhang, J Renz arXiv preprint arXiv:2312.11138, 2023 | 1 | 2023 |
| Measuring difficulty of novelty reaction E Nikonova, C Xue, V Pinto, C Gamage, P Zhang, J Renz arXiv preprint arXiv:2207.13857, 2022 | 1 | 2022 |
| Efficient Open-world Reinforcement Learning via Knowledge Distillation and Autonomous Rule Discovery E Nikonova, C Xue, J Renz arXiv preprint arXiv:2311.14270, 2023 | | 2023 |
| Don't do it: Safer Reinforcement Learning With Rule-based Guidance E Nikonova, C Xue, J Renz arXiv preprint arXiv:2212.13819, 2022 | | 2022 |
| Improve the Active Subspace Method by Partitioning the Parameter Space C Xue The Australian National University, 2018 | | 2018 |
Jochen RenzProfessor, Australian National UniversityVerified email at anu.edu.au
