A Bayesian Ensemble Regression Framework on the Angry Birds Game

An ensemble inference mechanism is proposed on the Angry Birds domain. It is based on an efficient tree structure for encoding and representing game screenshots, where it exploits its enhanced modeling capability. This has the advantage to establish an informative feature space and modify the task of game playing to a regression analysis problem. To this direction, we assume that each type of object material and bird pair has its own Bayesian linear regression model. In this way, a multi-model regression framework is designed that simultaneously calculates the conditional expectations of several objects and makes a target decision through an ensemble of regression models. Learning procedure is performed according to an online estimation strategy for the model parameters. We provide comparative experimental results on several game levels that empirically illustrate the efficiency of the proposed methodology.Comment: Angry Birds AI Symposium, ECAI 201

Marketing applications: from Angry Birds to happy marketers

This marketing teaching case is focussed on the rapidly emergent industry associated with Apps for mobile devices. After setting the context in respect of the awe-inspiring numbers associated with these markets the case is made that innovative marketing is happening across all parts of the basic marketing framework – the 4Ps. The case presents many specific examples of marketing related decision making and outcomes, focussing on games-Apps such as Rovio’s best-selling Angry Birds game

ANARKISME DALAM ANGRY BIRDS

Angry Birds bukan hanya permainan digital yang lucu, seperti yang kerapkali dianggap banyak orang selama ini. Banyak hal yang terungkap di balik kelucuan itu, di antaranya, aksi anarkistis. Aksi anarkistis tersebut mengingatkan pikiran kita pada beberapa peristiwa serupa yang kerapkali terjadi di negeri ini. Untuk mengungkapkan hal itu, penulis menggunakan teori semiotik Roland Barthes dan eksplorasi ilmiah yang dilakukan oleh Huesmann. Teori Barthes digunakan untuk membedah tanda-tanda yang terdapat pada games tersebut, sedangkan teori Huesmann digunakan untuk mengeksplorasi efek yang ditimbulkan dari persinggungan antara manusia dan games yang bertemakan kekerasan tersebut. Sementara itu, pandangan Dittmar digunakan untuk mengurai afek yang muncul dari kebiasaan itu. Banyak hasil eksplorasi, salah satu di antaranya penelitian Huesmann, membuktikan bahwa tayangan kekerasan, disadari atau tidak, akan memberikan dampak tertentu kepada penontonnya. Angry Birds dan sederet permainan serupa juga disadari atau tidak akan meninggalkan jejak serupa pada kurun waktu tertentu. Tulisan ini memaparkan aspek anarkisme ditinjau dari sudut semiotik dan dampak yang mungkin muncul dalam games tersebut. Key Words: Angry Birds, games, kematian, anarkis, efek, dan afek. Angry Birds is not just a cute, funny, digital games as commonly considered by many people during this time. Many things are revealed behind the cuteness of it, among other things, the anarchism. Anarchism are reminded of our thoughts on some events that occur in several parts of country. To reveal it, I uses the semiotic theory of Roland Barthes and scientific exploration of L. Rowell Huesmann. Barthes' theory explorates the signs contained in those games while Huesmann theory explores the effect of the critical interface between humans and violent-themed games. Meanwhile, Dittmar's views used to disentangle affect of that habit. A lot of the exploration results, as Huesmann did, proving that a violent, realized or not, will give the specific impact to someone. Angry Birds and a series of similar games also realized or will not leave a trace in similar time. This paper described the anarchism viewed from semiotic, and the effect and affect that may appear in the games. Key Words: Angry Birds, games, death, anarchy, effect, and affect

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.Comment: 11 pages, 10 figures, 2 tables, Accepted at the 19th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 23

The Computational Complexity of Angry Birds

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 challenging problem for traditional AI techniques to solve, even though the game is simple enough that any human player could learn and master it within a short time. In this paper we analyse how hard the problem really is, presenting several proofs for the computational complexity of Angry Birds. By using a combination of several gadgets within this game's environment, we are able to demonstrate that the decision problem of solving general levels for different versions of Angry Birds is either NP-hard, PSPACE-hard, PSPACE-complete or EXPTIME-hard. Proof of NP-hardness is by reduction from 3-SAT, whilst proof of PSPACE-hardness is by reduction from True Quantified Boolean Formula (TQBF). Proof of EXPTIME-hardness is by reduction from G2, a known EXPTIME-complete problem similar to that used for many previous games such as Chess, Go and Checkers. To the best of our knowledge, this is the first time that a single-player game has been proven EXPTIME-hard. This is achieved by using stochastic game engine dynamics to effectively model the real world, or in our case the physics simulator, as the opponent against which we are playing. These proofs can also be extended to other physics-based games with similar mechanics.Comment: 55 Pages, 39 Figure

PENGEMBANGAN PERANGKAT PEMBELAJARAN FUNGSI KUADRAT MELALUI MODEL KOOPERATIF TIPE NUMBERED HEADS TOGETHER BERBANTUAN SOFTWARE AUTOGRAPH DENGAN GAME ANGRY BIRDS

Iwannitona. 2017. Pengembangan Perangkat Pembelajaran Fungsi Kuadrat melalui Model Kooperatif Tipe Numbered Heads Together berbantuan Software Autograph dengan Game Angry Birds.Penggunaan IT dalam proses pembelajaran merupakan salah satu amanat yang disampaikan dalam Kurikulum 2013. IT sudah menjadi bagian dalam kehidupan sehari-hari siswa dalam berbagai kebutuhan. Game merupakan aktivitas favorit siswa berbasis IT, Angry Birds adalah salah satunya. Software Autograph adalah salah satu software berbasis IT yang dapat membantu pengguna terutama pada pembelajaran Fungsi Kuadrat. Fungsi Kuadrat adalah materi yang masih kurang diminati siswa karena membutuhkan penalaran dan keterampilan yang baik terkait hubungan antara penguraian materi dengan aplikasinya dalam kehidupan nyata. Meningkatkan dan memotivasi cara belajar siswa melalui penerapan model pembelajaran kooperatif tipe NHT yang mengintegrasikan IT didalamnya. Penelitian ini dilakukan untuk mengembangkan perangkat pembelajaran pada materi Fungsi Kuadrat dengan menerapkan model pembelajaran kooperatif tipe NHT berbantuan Software Autograph dan Game Angry Birds. Perangkat pembelajaran yang dikembangkan terdiri dari Modul Pembelajaran, RPP, LAS, dan THB. Penelitian ini merupakan penelitian pengembangan yang mengacu pada tahapan yang dikemukakan oleh Plomp terdiri atas tiga tahapan. Tahap pertama Preliminary Research yaitu kegiatan awal yang bertujuan untuk mengumpulkan informasi terkait kebutuhan dan keadaan dilapangan. Tahap kedua Prototyping Stage bertujuan untuk mengembangkan produk sesuai dengan hasil analisis dari tahapan Preliminary Research. Tahapan ketiga yaitu Assessment Phase merupakan kegiatan melakukan ujicoba lapangan untuk melihat ketercapaian hasil penelitian yang bertujuan untuk mendapatkan produk yang praktis dan efektif. Subjek dalam penelitian ini adalah siswa kelas X MAN Model Banda Aceh. Berdasarkan hasil penelitian yang dilakukan dalam mengembangkan perangkat pembelajaran yang terdiri dari Modul, RPP, LAS, dan THB dengan model pembelajaran kooperatif tipe NHT berbantuan Software Autograph dan Game Angry Birds pada materi Fungsi Kuadrat. Perangkat pembelajaran dinyatakan valid, praktis, dan efektif.Kata Kunci: Fungsi Kuadrat, Pembelajaran Kooperatif Tipe NHT, Software Autograph, Game Angry Birds

The Difficulty of Novelty Detection in Open-World Physical Domains: An Application to Angry Birds

Detecting and responding to novel situations in open-world environments is a key capability of human cognition. Current artificial intelligence (AI) researchers strive to develop systems that can perform in open-world environments. Novelty detection is an important ability of such AI systems. In an open-world, novelties appear in various forms and the difficulty to detect them varies. Therefore, to accurately evaluate the detection capability of AI systems, it is necessary to investigate the difficulty to detect novelties. In this paper, we propose a qualitative physics-based method to quantify the difficulty of novelty detection focusing on open-world physical domains. We apply our method in a popular physics simulation game, Angry Birds. We conduct an experiment with human players with different novelties in Angry Birds to validate our method. Results indicate that the calculated difficulty values are in line with the detection difficulty of the human players