Qualitative Physics in &lt;italic&gt;Angry Birds&lt;/italic&gt;

Wałęga, Przemysław Andrzej; Zawidzki, Michał; Lechowski, Tomasz

doi:10.1109/tciaig.2016.2561080

Cited by 16 publications

(14 citation statements)

References 9 publications

(8 reference statements)

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“…Image understanding and qualitative reasoning in games: Applications in scene understanding from 2D pictures and computer games require inferring physical relations among objects [3], [9], [10]. Determination of stability of stacked objects and supportedness among objects have been studied, commonly with qualitative reasoning approaches [11]. Determination of stable final configuration of constructions has also been studied in computer games, like Angry Bird [12]- [14].…”

Section: Related Workmentioning

confidence: 99%

A Formal Framework for Robot Construction Problems: A Hybrid Planning Approach

Ahmad,

Erdem,

Patoglu

2019

Preprint

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We study robot construction problems where multiple autonomous robots rearrange stacks of prefabricated blocks to build stable structures. These problems are challenging due to ramifications of actions, true concurrency, and requirements of supportedness of blocks by other blocks and stability of the structure at all times. We propose a formal hybrid planning framework to solve a wide range of robot construction problems, based on Answer Set Programming. This framework not only decides for a stable final configuration of the structure, but also computes the order of manipulation tasks for multiple autonomous robots to build the structure from an initial configuration, while simultaneously ensuring the stability, supportedness and other desired properties of the partial construction at each step of the plan. We prove the soundness and completeness of our formal method with respect to these properties. We introduce a set of challenging robot construction benchmark instances, including bridge building and stack overhanging scenarios, discuss the usefulness of our framework over these instances, and demonstrate the applicability of our method using a bimanual Baxter robot.

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Section: Related Workmentioning

confidence: 99%

A Formal Framework for Robot Construction Problems: A Hybrid Planning Approach

Ahmad,

Erdem,

Patoglu

2019

Preprint

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“…In the domain of Angry Birds AI competition , there has been some work on using qualitative reasoning (Walega, Zawidzki, and Lechowsk 2016) or logic formalisation (Calimeri et al 2016) to analyse behaviours of two-dimensional rigid objects in simulation. The rules are often empirically obtained and are specific to the problem domain.…”

Section: Background and Related Workmentioning

confidence: 99%

Towards Explainable Inference about Object Motion using Qualitative Reasoning

Ge¹,

Renz²,

Hua³

2018

Preprint

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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 such inferences. The simulation requires a precise model of the underlying domain to work well and is essentially a black-box from which one can hardly obtain any useful explanation. By contrast, qualitative reasoning methods have the advantage in making transparent inferences with ambiguous information, which makes it suitable for this task. However, there has been no suitable qualitative theory proposed for object motion in three-dimensional space. In this paper, we take this challenge and develop a qualitative theory for the motion of rigid objects. Based on this theory, we develop a reasoning method to solve a very interesting problem: Assuming there are several objects that were initially at rest and now have started to move. We want to infer what action causes the movement of these objects.

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“…The eventual goal of this competition is to design agents that can play new levels as well as, or better than, the best human players. Many of the previous agents that have participated in this competition employed a variety of techniques, including qualitative reasoning [8], internal simulation analysis [9], [10], logic programming [11], heuristics [12], Bayesian inferences [13], [14], and structural analysis [15].…”

Section: Introductionmentioning

confidence: 99%

The 2017 AIBIRDS Level Generation Competition

Stephenson

Renz

et al. 2019

IEEE Trans. Games

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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 and predict the outcome of actions with only limited environmental information. Agents entered into this competition were required to solve a wide assortment of previously unseen levels within a set time limit. The physical reasoning and planning required to solve these levels are very similar to those of many realworld problems. This year's competition featured some of the best agents developed so far and even included several new AI techniques such as deep reinforcement learning. Within this paper we describe the framework, rules, submitted agents and results for this competition. We also provide some background information on related work and other video game AI competitions, as well as discussing some potential ideas for future AIBIRDS competitions and agent improvements.

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Qualitative Physics in <italic>Angry Birds</italic>

Cited by 16 publications

References 9 publications

A Formal Framework for Robot Construction Problems: A Hybrid Planning Approach

A Formal Framework for Robot Construction Problems: A Hybrid Planning Approach

Towards Explainable Inference about Object Motion using Qualitative Reasoning

The 2017 AIBIRDS Level Generation Competition

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