11 research outputs found
Bejeweled, Candy Crush and other Match-Three Games are (NP-)Hard
The twentieth century has seen the rise of a new type of video games targeted
at a mass audience of "casual" gamers. Many of these games require the player
to swap items in order to form matches of three and are collectively known as
\emph{tile-matching match-three games}. Among these, the most influential one
is arguably \emph{Bejeweled} in which the matched items (gems) pop and the
above gems fall in their place. Bejeweled has been ported to many different
platforms and influenced an incredible number of similar games. Very recently
one of them, named \emph{Candy Crush Saga} enjoyed a huge popularity and
quickly went viral on social networks. We generalize this kind of games by only
parameterizing the size of the board, while all the other elements (such as the
rules or the number of gems) remain unchanged. Then, we prove that answering
many natural questions regarding such games is actually \NP-Hard. These
questions include determining if the player can reach a certain score, play for
a certain number of turns, and others. We also
\href{http://candycrush.isnphard.com}{provide} a playable web-based
implementation of our reduction.Comment: 21 pages, 12 figure
Trainyard is NP-Hard
Recently, due to the widespread diffusion of smart-phones, mobile puzzle
games have experienced a huge increase in their popularity. A successful puzzle
has to be both captivating and challenging, and it has been suggested that this
features are somehow related to their computational complexity \cite{Eppstein}.
Indeed, many puzzle games --such as Mah-Jongg, Sokoban, Candy Crush, and 2048,
to name a few-- are known to be NP-hard \cite{CondonFLS97,
culberson1999sokoban, GualaLN14, Mehta14a}. In this paper we consider
Trainyard: a popular mobile puzzle game whose goal is to get colored trains
from their initial stations to suitable destination stations. We prove that the
problem of determining whether there exists a solution to a given Trainyard
level is NP-hard. We also \href{http://trainyard.isnphard.com}{provide} an
implementation of our hardness reduction
Large Peg-Army Maneuvers
Despite its long history, the classical game of peg solitaire continues to
attract the attention of the scientific community. In this paper, we consider
two problems with an algorithmic flavour which are related with this game,
namely Solitaire-Reachability and Solitaire-Army. In the first one, we show
that deciding whether there is a sequence of jumps which allows a given initial
configuration of pegs to reach a target position is NP-complete. Regarding
Solitaire-Army, the aim is to successfully deploy an army of pegs in a given
region of the board in order to reach a target position. By solving an
auxiliary problem with relaxed constraints, we are able to answer some open
questions raised by Cs\'ak\'any and Juh\'asz (Mathematics Magazine, 2000). To
appreciate the combinatorial beauty of our solutions, we recommend to visit the
gallery of animations provided at http://solitairearmy.isnphard.com.Comment: Conference versio
Push-Pull Block Puzzles are Hard
This paper proves that push-pull block puzzles in 3D are PSPACE-complete to
solve, and push-pull block puzzles in 2D with thin walls are NP-hard to solve,
settling an open question by Zubaran and Ritt. Push-pull block puzzles are a
type of recreational motion planning problem, similar to Sokoban, that involve
moving a `robot' on a square grid with obstacles. The obstacles
cannot be traversed by the robot, but some can be pushed and pulled by the
robot into adjacent squares. Thin walls prevent movement between two adjacent
squares. This work follows in a long line of algorithms and complexity work on
similar problems. The 2D push-pull block puzzle shows up in the video games
Pukoban as well as The Legend of Zelda: A Link to the Past, giving another
proof of hardness for the latter. This variant of block-pushing puzzles is of
particular interest because of its connections to reversibility, since any
action (e.g., push or pull) can be inverted by another valid action (e.g., pull
or push).Comment: Full version of CIAC 2017 paper. 17 page
NP-completeness of the game Kingdomino
Kingdomino is a board game designed by Bruno Cathala and edited by Blue
Orange since 2016. The goal is to place dominoes on a grid layout,
and get a better score than other players. Each domino cell has a
color that must match at least one adjacent cell, and an integer number of
crowns (possibly none) used to compute the score. We prove that even with full
knowledge of the future of the game, in order to maximize their score at
Kingdomino, players are faced with an NP-complete optimization problem
Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN
Game player modeling is a paradigm of computational models to exploit players’ behavior and experience using game and player analytics. Player modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the game. Player behavior focuses on dynamic and static information gathered at the time of gameplay. Player experience concerns the association of the human player during gameplay, which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s surroundings. In this paper, player experience modeling is studied based on the board puzzle game “Candy Crush Saga” using cognitive data of players accessed by physiological and peripheral devices. Long Short-Term Memory-based Deep Neural Network (LSTM-DNN) is used to predict players’ effective states in terms of valence, arousal, dominance, and liking by employing the concept of transfer learning. Transfer learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related problems. The homogeneous transfer learning approach has not been implemented in the game domain before, and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players’ engagement. Relevant not only from a player’s point of view, it is also a benchmark study for game developers who have been facing problems of “cold start” for innovative games that strengthen the game industrial economy
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
Assessing Influential Users in Live Streaming Social Networks
abstract: Live streaming has risen to significant popularity in the recent past and largely this live streaming is a feature of existing social networks like Facebook, Instagram, and Snapchat. However, there does exist at least one social network entirely devoted to live streaming, and specifically the live streaming of video games, Twitch. This social network is unique for a number of reasons, not least because of its hyper-focus on live content and this uniqueness has challenges for social media researchers.
Despite this uniqueness, almost no scientific work has been performed on this public social network. Thus, it is unclear what user interaction features present on other social networks exist on Twitch. Investigating the interactions between users and identifying which, if any, of the common user behaviors on social network exist on Twitch is an important step in understanding how Twitch fits in to the social media ecosystem. For example, there are users that have large followings on Twitch and amass a large number of viewers, but do those users exert influence over the behavior of other user the way that popular users on Twitter do?
This task, however, will not be trivial. The same hyper-focus on live content that makes Twitch unique in the social network space invalidates many of the traditional approaches to social network analysis. Thus, new algorithms and techniques must be developed in order to tap this data source. In this thesis, a novel algorithm for finding games whose releases have made a significant impact on the network is described as well as a novel algorithm for detecting and identifying influential players of games. In addition, the Twitch network is described in detail along with the data that was collected in order to power the two previously described algorithms.Dissertation/ThesisDoctoral Dissertation Computer Science 201