| Plug-and-blend: a framework for plug-and-play controllable story generation with sketches Z Lin, MO Riedl Proceedings of the AAAI Conference on Artificial Intelligence and …, 2021 | 57* | 2021 |
| Explore, exploit or listen: Combining human feedback and policy model to speed up deep reinforcement learning in 3d worlds Z Lin, B Harrison, A Keech, MO Riedl arXiv preprint arXiv:1709.03969, 2017 | 42 | 2017 |
| Beyond prompts: Exploring the design space of mixed-initiative co-creativity systems Z Lin, U Ehsan, R Agarwal, S Dani, V Vashishth, M Riedl arXiv preprint arXiv:2305.07465, 2023 | 22 | 2023 |
| Novgrid: A flexible grid world for evaluating agent response to novelty J Balloch, Z Lin, M Hussain, A Srinivas, R Wright, X Peng, J Kim, M Riedl arXiv preprint arXiv:2203.12117, 2022 | 19 | 2022 |
| Generationmania: Learning to semantically choreograph Z Lin, K Xiao, M Riedl Proceedings of the AAAI Conference on Artificial Intelligence and …, 2019 | 17 | 2019 |
| Creative wand: a system to study effects of communications in co-creative settings Z Lin, R Agarwal, M Riedl Proceedings of the AAAI Conference on Artificial Intelligence and …, 2022 | 10 | 2022 |
| Neuro-symbolic world models for adapting to open world novelty J Balloch, Z Lin, R Wright, X Peng, M Hussain, A Srinivas, J Kim, MO Riedl arXiv preprint arXiv:2301.06294, 2023 | 9 | 2023 |
| An Ontology of Co-Creative AI Systems Z Lin, M Riedl arXiv preprint arXiv:2310.07472, 2023 | 3 | 2023 |
| A Controllable Co-Creative Agent for Game System Design R Agarwal, Z Lin, M Riedl arXiv preprint arXiv:2308.02317, 2023 | 2 | 2023 |
| External Model Motivated Agents: Reinforcement Learning for Enhanced Environment Sampling R Bhagat, J Balloch, Z Lin, J Kim, M Riedl arXiv preprint arXiv:2407.00264, 2024 | | 2024 |
Mark RiedlProfessor of Computing, Georgia Institute of TechnologyVerified email at cc.gatech.edu
