12 citations · 25 across the 16 of their papers we have counts for
17 papers
Physics-Based Task Generation through Causal Sequence of Physical Interactions
Chathura Gamage, Vimukthini Pinto, Matthew Stephenson +1
Performing tasks in a physical environment is a crucial yet challenging problem for AI systems operating in the real world. Physics simulation-based tasks are often employed to fac…
ChatGPT4PCG Competition: Character-like Level Generation for Science Birds
Pittawat Taveekitworachai, Febri Abdullah, Mury F. Dewantoro +3
This paper presents the first ChatGPT4PCG Competition at the 2023 IEEE Conference on Games. The objective of this competition is for participants to create effective prompts for Ch…
NovPhy: A Testbed for Physical Reasoning in Open-world Environments
Chathura Gamage, Vimukthini Pinto, Cheng Xue +4
Due to the emergence of AI systems that interact with the physical environment, there is an increased interest in incorporating physical reasoning capabilities into those AI system…
Don't do it: Safer Reinforcement Learning With Rule-based Guidance
Ekaterina Nikonova, Cheng Xue, Jochen Renz
During training, reinforcement learning systems interact with the world without considering the safety of their actions. When deployed into the real world, such systems can be dang…
Measuring Difficulty of Novelty Reaction
Ekaterina Nikonova, Cheng Xue, Vimukthini Pinto +3
Current AI systems are designed to solve close-world problems with the assumption that the underlying world is remaining more or less the same. However, when dealing with real-worl…
Phy-Q as a measure for physical reasoning intelligence
Cheng Xue, Vimukthini Pinto, Chathura Gamage +3
Humans are well-versed in reasoning about the behaviors of physical objects and choosing actions accordingly to accomplish tasks, while it remains a major challenge for AI. To faci…