Xiaoyu Ge

Australian National University | ANU · Research School of Computer Science

This paper presents several proofs for the computational complexity of the popular physics-based puzzle game AngryBirds. By using a combination of different gadgets within this game’s environment, we can demonstrate that the problem of solving Angry Birds levels is NP-hard. Proof of NP-hardness is by reduction from a known NP-complete problem, in t...

Understanding physical relations between objects, especially their support relations, is crucial for robotic manipulation. In this paper, we propose a method for extracting more detailed physical knowledge from a set of RGB-D images taken from the same scene but from different views using qualitative reasoning and intuitive physical models. Rather...

In this paper we present several proofs for the computational complexity of the physics-based video game Angry Birds. We are able to demonstrate that solving levels for different versions of Angry Birds is either NP-hard, PSPACE-hard, PSPACE-complete or EXPTIME-hard, depending on the maximum number of birds available and whether the game engine is...

The physics-based simulation game Angry Birds has been heavily researched by the AI community over the past five years, and has been the subject of a popular AI competition that is currently held annually as part of a leading AI conference. Developing intelligent agents that can play this game effectively has been an incredibly complex and challeng...

Could this be the year that AI is going to surpass human performance in playing the popular video game Angry Birds? The organizers of the annual AIBIRDS competition discuss the challenges involved.

Understanding physical relations between objects, especially their support relations, is crucial for robotic manipulation. There has been work on reasoning about support relations and structural stability of simple configurations in RGB-D images. In this paper, we propose a method for extracting more detailed physical knowledge from a set of RGB-D...

This paper presents a structure generation algorithm which converts rough human drawings into stable structures comprised of rectangular blocks, suitable for physics-based 2D environments. Generating viable structures for a physics-based environment imposes many additional requirements above those of most traditional sketch-based domains. Our metho...

This paper presents a structure generation algorithm which converts rough human drawings into stable structures comprised of rectangular blocks,suitable for physics-based 2D environments. Generating viable structures for a physics-based environment imposes many additional requirements above those of most traditional sketch-based domains. Our method...

The physics-based simulation game Angry Birds has been heavily researched by the AI community over the past five years, and has been the subject of a popular AI competition that is being held annually as part of a leading AI conference. Developing intelligent agents that can play this game effectively has been an incredibly complex and challenging...

The capability of making explainable inferences regarding physical processes has long been desired. One fundamental physical process is object motion. Inferring what causes the motion of a group of objects can even be a challenging task for experts, e.g., in forensics science. Most of the work in the literature relies on physics simulation to draw...

This paper presents an overview of the second AIBIRDS level generation competition, held jointly at the 2017 IEEE Conference on Computational Intelligence and Games, and the 26th International Joint Conference on Artificial Intelligence. This competition tasked entrants with developing a level generator for the physics-based puzzle game Angry Birds...

This paper presents an overview of the sixth AIBIRDS competition, held at the 26th International Joint Conference on Artificial Intelligence. This competition tasked participants with developing an intelligent agent which can play the physics-based puzzle game Angry Birds. This game uses a sophisticated physics engine that requires agents to reason...

Building Artificial Intelligence (AI) that can successfully interact with the physical world in a comprehensive and human-like way is a big challenge. Physics simulation games, i.e., video games where the game world simulates real-world physics, offer a simplified and controlled environment for developing and testing Artificial Intelligence. It all...

Developing Artificial Intelligence (AI) that is capable of understanding and interacting with the real world in a sophisticated way has long been a grand vision of AI. There is an increasing number of AI agents coming into our daily lives and assisting us with various daily tasks ranging from house cleaning to serving food in restaurants. While dif...

The Angry Birds AI Competition (aibirds.org) has been held annually since 2012 in conjunction with some of the major AI conferences, most recently with IJCAI 2015. The goal of the competition is to build AI agents that can play new Angry Birds levels as good as or better than the best human players. Successful agents should be able to quickly analy...

The aim of the Angry Birds AI competition (AIBIRDS) is to build intelligent agents that can play new Angry Birds levels better than the best human players. This is surprisingly difficult for AI as it requires similar capabilities to what humans need for successfully interacting with the physical world, one of the grand challenges of AI. As such the...

Many current computer vision approaches for object detection can only detect objects that have been learned in advance. In this paper, we present a method that uses qualitative stability analysis to infer the existence of unknown objects in certain areas of the images based on gravity and stability of already detected objects. Our method recursivel...

Building Artificial Intelligence (AI) that can successfully interact with the physical world in a comprehensive and human-like way is a big challenge. Physics simulation games, i.e., video games where the game world simulates real-world physics, offer a simplified and controlled environment for developing and testing Artificial Intelligence. It all...

Intelligent agents perceive the world mainly through images captured at different time points. Being able to track objects from one image to another is fundamental for understanding the changes of the world. Tracking becomes challenging when there are multiple perceptually indistinguishable objects (PIOs), i.e., objects that have the same appearanc...

Entities in two-dimensional space are often approximated using rectangles that are parallel to the two axes that define the space, so-called minimum-bounding rectangles (MBRs). MBRs are popular in Computer Vision and other areas as they are easy to obtain and easy to represent. In the area of Qualitative Spatial Reasoning, many different spatial re...