6 citations · 7 across the 4 of their papers we have counts for
8 papers · 1 filter
Deceptive Level Generation for Angry Birds
Chathura Gamage, Matthew Stephenson, Vimukthini Pinto +1
The Angry Birds AI competition has been held over many years to encourage the development of AI agents that can play Angry Birds game levels better than human players. Many differe…
Hi-Phy: A Benchmark for Hierarchical Physical Reasoning
Cheng Xue, Vimukthini Pinto, Chathura Gamage +2
Reasoning about the behaviour of physical objects is a key capability of agents operating in physical worlds. Humans are very experienced in physical reasoning while it remains a m…
Using Restart Heuristics to Improve Agent Performance in Angry Birds
Tommy Liu, Jochen Renz, Peng Zhang +1
Over the past few years the Angry Birds AI competition has been held in an attempt to develop intelligent agents that can successfully and efficiently solve levels for the video ga…
Agent-Based Adaptive Level Generation for Dynamic Difficulty Adjustment in Angry Birds
Matthew Stephenson, Jochen Renz
This paper presents an adaptive level generation algorithm for the physics-based puzzle game Angry Birds. The proposed algorithm is based on a pre-existing level generator for this…
A Continuous Information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarking
Matthew Stephenson, Damien Anderson, Ahmed Khalifa +4
This paper introduces an information-theoretic method for selecting a subset of problems which gives the most information about a group of problem-solving algorithms. This method w…
Towards Explainable Inference about Object Motion using Qualitative Reasoning
Xiaoyu Ge, Jochen Renz, Hua Hua
The capability of making explainable inferences regarding physical processes has long been desired. One fundamental physical process is object motion. Inferring what causes the mot…