Physics-based Puzzle Research Articles

Physics-Based Task Generation through Causal Sequence of Physical Interactions

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 facilitate research that addresses this challenge. In this paper, first, we present a systematic approach for defining a physical scenario using a causal sequence of physical interactions between objects. Then, we propose a methodology for generating tasks in a physics-simulating environment using these defined scenarios as inputs. Our approach enables a better understanding of the granular mechanics required for solving physics-based tasks, thereby facilitating accurate evaluation of AI systems' physical reasoning capabilities. We demonstrate our proposed task generation methodology using the physics-based puzzle game Angry Birds and evaluate the generated tasks using a range of metrics, including physical stability, solvability using intended physical interactions, and accidental solvability using unintended solutions. We believe that the tasks generated using our proposed methodology can facilitate a nuanced evaluation of physical reasoning agents, thus paving the way for the development of agents for more sophisticated real-world applications.

Utilizing Generative Adversarial Networks for Stable Structure Generation in Angry Birds

This paper investigates the suitability of using Generative Adversarial Networks (GANs) to generate stable structures for the physics-based puzzle game Angry Birds. While previous applications of GANs for level generation have been mostly limited to tile-based representations, this paper explores their suitability for creating stable structures made from multiple smaller blocks. This includes a detailed encoding/decoding process for converting between Angry Birds level descriptions and a suitable grid-based representation, as well as utilizing state-of-the-art GAN architectures and training methods to produce new structure designs. Our results show that GANs can be successfully applied to generate a varied range of complex and stable Angry Birds structures.

The Reader as Player: Exploring the Notion of Aesthetic and Efferent Gaming

This article argues that videogames may be considered part of the fluid cross-media landscape of children’s literature and that they require a particular sort of active reader, namely a player. Departing from the concept of cybertext and using the physics-based puzzle game Unravel (Coldwood Interactive) as a main example, the article draws on theoretical discussions from game studies (Aarseth; Jenkins; Jørgensen; Schell; Sicart; Tronstad; Zimmermann; Yee) and on Louise Rosenblatt’s distinction between efferent and aesthetic reading to discuss what kinds of player engagement may constitute aesthetic and efferent gaming.

A Game AI Competition to Foster Collaborative AI Research and Development

Game AI competitions are important to foster research and development on Game AI and AI in general. These competitions supply different challenging problems that can be translated into other contexts, virtual or real. They provide frameworks and tools to facilitate the research on their core topics and provide means for comparing and sharing results. A competition is also a way to motivate new researchers to study these challenges. In this document, we present the Geometry Friends Game AI Competition. Geometry Friends is a two-player cooperative physics-based puzzle platformer computer game. The concept of the game is simple, though its solving has proven to be difficult. While the main and apparent focus of the game is cooperation, it also relies on other AI-related problems such as planning, plan execution, and motion control, all connected to situational awareness. All of these must be solved in real-time. In this paper, we discuss the competition and the challenges it brings, and present an overview of the current solutions.

Evolving Playable Content for Cut the Rope through a Simulation-Based Approach

In order to automatically generate high-quality game levels, one needs to be able to automatically verify that the levels are playable. The simulation-based approach to playability testing uses an artificial agent to play through the level, but building such an agent is not always an easy task and such an agent is not always readily available. We discuss this prob- lem in the context of the physics-based puzzle game Cut the Rope, which features continuous time and state space, mak- ing several approaches such as exhaustive search and reactive agents inefficient. We show that a deliberative Prolog-based agent can be used to suggest all sensible moves at each state, which allows us to restrict the search space so that depth-first search for solutions become viable. This agent is successfully used to test playability in Ropossum, a level generator based on grammatical evolution. The method proposed in this paper is likely to be useful for a large variety of games with similar characteristics.

The Computational Complexity of Angry Birds and Similar Physics-Simulation Games

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 this case 3-SAT. In addition, we are able to show that the original version of Angry Birds is within NP and therefore alsoNP-complete. These proofs can be extended to other physics-based games with similar mechanics.

Procedural Generation of Levels for Angry Birds Style Physics Games

This paper presents a procedural generation algorithm for levels in physics-based puzzle games similar to Angry Birds. The proposed algorithm creates levels consisting of various self-contained structures placed throughout a 2D area. Each structure can be placed either on the ground or atop floating platforms within the available level space. These structures are created using a variety of different block types and do not require predefined substructures or composite elements. Target object locations are determined based on a combination of factors, including structural protection, occupancy estimation and overall dispersion. Experiments were performed in order to determine the ideal input parameters for generating desirable levels. The expressivity of the generator was also evaluated and the results show that the proposed method can generate a wide variety of interesting levels.

The 2017 AIBIRDS Level Generation Competition

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. Submitted generators were required to deal with many physical reasoning constraints caused by the realistic nature of the game's environment, in addition to ensuring that the created levels were fun, challenging, and solvable. This year's competition was a significant improvement over the previous year, with a greater number of participants and more advanced generators. In this paper, we describe the framework, rules, submitted generators, and results for this competition. We also provide some background information on related research and other video game AI competitions and discuss what can be learned from this year's competition. There are several game and real-world applications for this type of research, and we provide some examples of the types of levels we would like future competition entries to generate.

Tanager: A Generator of Feasible and Engaging Levels for <italic>Angry Birds</italic>

Generating feasible levels for physics-based puzzle games is a complex and time-consuming task. This is because the mechanics of these games are based on realistic physics, so simulations are required to evaluate the playability of the generated levels. Recently, a few generators have been able to produce considerably complex levels in the context of the Angry Birds game, a very famous title of this genre. However, none of these generators are able to guarantee the playability of their produced levels. This paper presents Tanager, a level generator based on a genetic algorithm that is capable of producing feasible levels for the Angry Birds game. Evaluating playability in this game requires checking both the stability of the stacked blocks and the possibility of killing all the pigs with the given amount of birds. These two components are handled by the algorithm through a simulation. The first one is calculated by measuring the overall velocity of the blocks and the second is defined by an intelligent agent, which plays the levels. Three sets of experiments are conducted to evaluate Tanager. The first one measures the performance of the genetic algorithm underneath Tanager. The second one explores the expressivity of the generated levels considering their structural characteristics. The third one measures design quality of levels via an online user study. Results show that Tanager is capable of generating a considerable variety of feasible levels that are as engaging and enjoyable as those manually designed. However, the generated levels are less challenging than the hand-authored ones.

Speeding up Search-Based Algorithms for Level Generation in Physics-Based Puzzle Games

This research uses Machine Learning (ML) techniques in order to aid search-based (SB) algorithms to improve level generation for physics-based puzzle games. These algorithms’ performance are improved by reducing simulation time when the ML techniques are applied. Classification algorithms prevent levels with undesired traits to be evaluated during the simulation phase of the SB procedures. An Angry Birds clone is used for conducting the experiments and results report improvement using the combined approach against every SB algorithm by itself.

Playing a puzzle video game with changing requirements improves executive functions

Recent research suggests a causal link between action video game playing and enhanced attention and visual-perceptual skills. In contrast, evidence linking action video games and enhanced executive function is equivocal. We investigated whether action and non-action video games enhance executive function. Fifty-five inexperienced video game players played one of four different games: an action video game (Modern Combat), a physics-based puzzle game (Cut the Rope), a real-time strategy game (Starfront Collision), and a fast paced arcade game (Fruit Ninja) for 20h. Three pre and post training tests of executive function were administered: a random task switching, a flanker, and a response inhibition task (Go/No-go). Only the group that trained on the physics-based puzzle game significantly improved in all three tasks relative to the pre-test. No training-related improvements were seen in other groups. These results suggest that playing a complex puzzle game that demands strategizing, reframing, and planning improves several aspects of executive function.