Puzzle Solving Research Articles

Toward Understanding the Effects of Intelligence of a Virtual Character during an Immersive Jigsaw Puzzle Co-Solving Task

In virtual reality, creating intelligent virtual characters has been a long-lasting endeavor. However, while researchers have investigated several aspects of a virtual character's intelligence, little attention has been paid to the impact of the implemented intelligence levels assigned to a virtual character during human-virtual character collaboration. Thus, we conducted a within-group study ( \(N=24\) ) to explore how three different intelligence levels (low vs. medium vs. high) assigned to a virtual character can impact how study participants perceive that virtual character and interact with the task they are instructed to complete. Specifically, for our study, we developed a jigsaw puzzle game and instructed our participants to solve it with the help of a virtual character. During the jigsaw puzzle solving process, we collected application logs related to how the participants executed the task and observed the virtual environment. Moreover, after each condition, we asked the participants to respond using a questionnaire that examined their social presence, how they perceived the character's intelligence and compared it with their own, and how they rated the virtual character's realism. Our results indicated that the different intelligence levels assigned to the virtual characters impacted participants’ responses on several variables, including co-presence, perceived intelligence, intelligence comparison, and character interaction and behavior realism. Moreover, based on the collected logged data, we found that the intelligence levels assigned to our virtual character significantly impacted the performance of our participants. Our results could be valuable to the research community for creating more engaging experiences with intelligent virtual characters for collaborative tasks in immersive environments.

Out-BREAK!: An IDWeek 2023 Escape Room to Break Out of the Educational Mold.

An escape room is a cooperative game that has been adapted into medical education and major academic conferences. We describe the design, development, and implementation of an educational ID-themed escape room activity entitled "Out-BREAK!" at an international conference, IDWeek 2023. An anonymous survey was conducted to collect demographic data, assess participant satisfaction with the escape room puzzles, and gauge participant interest in game-based learning. Thirty escape room sessions were held over 3 days and included 201 participants. Escape room survey respondents (n = 132) were younger and more likely to be trainees compared with in-person IDWeek attendants. Among 131 responses, all respondents enjoyed the experience and would recommend the escape room activity to friends. Survey respondents enjoyed the puzzle solving (93%), medical content (92%), and team building (79%) components. Only 35% of the respondents had ever previously participated in game-based learning; 95% thought the escape room was a valuable teaching method. Among the 72 survey respondents involved in medical education, almost all (90%) said they were interested in incorporating escape rooms into medical education. The Out-BREAK! escape room at IDWeek 2023 was successfully implemented and well received. Despite only a third of participants having prior experience with game-based learning, almost all respondents perceived the escape room to be an effective teaching method.

Transmogrifying Anatomy Learning by Kaizen Strategies and Game-Based Education.

The study explores the significance of continuous improvement through Kaizen in the evolving landscape of anatomy education. In this study, our objectives were twofold: 1) to assess the effectiveness of incorporating games in the first-year medical curriculum for reinforcing anatomy knowledge, and 2) to explore whether game-based sessions elicit improved student responses in the learning of anatomy. A total of 100 first-year Bachelor of Medicine and Bachelor of Surgery (MBBS) students at All India Institute of Medical Sciences (AIIMS), Bibinagar, Hyderabad, Telangana, India, were exposed to game-based learning which involved six rounds: acronym mnemonics (Redolent), jigsaw puzzle solving (Dumbfound), Filling gaps in concept maps (Blogging), Connecting images (Kinship), case scenario creation (Penman), and rapid-fire round (Rattling). At the end of the intervention, a test was taken and feedback was obtained from all the participants using a prevalidated questionnaire prepared based on a 5-point Likert scale. Questionnaire responses were subjected to descriptive analysis, and reliability analysis (Cronbach's α) was performed to evaluate the internal consistencies of items. A paired t-test indicated that there was a significantly large difference between before (mean (M) = 17.2, standard deviation (SD) = 9.1) and after (M = 25.9, SD = 8), t(99) = 18.4, p < .001, signifying that the performance of the students was far better with game-based learning approaches than conventional learning. Combining game-based education with Kaizen principles in anatomy education not only prepares students for success in their academic pursuits but also empowers them to navigate the complexities of the ever-evolving healthcare landscape with confidence and proficiency.

Sex differences in visuospatial cognition- a female advantage in jigsaw puzzle solving.

Mentally visualizing objects, understanding relationships between two- or three- dimensional objects, and manipulating objects in space are some examples of visuospatial abilities. Numerous studies have shown that male participants outperform female participants in visuospatial tasks, particularly in mental rotation. One exception is solving jigsaw puzzles. Performance by seven- to eight-year-old girls was found to be superior to that of boys of the same age (Kocijan et al. 2017). No study, however, has confirmed this finding in an adult population, where sex differences are often detectable. Seventy-nine young adult participants were given four different jigsaw puzzles and the Shepard and Metzler mental rotation test (MRT) with two main goals: First, to investigate possible sex differences in jigsaw puzzle solving, and second, to explore a potential relationship between mental rotation and jigsaw puzzle solving. We hypothesized that female participants would outperform males in the jigsaw puzzles but males would outperform females in the MRT. The findings confirmed this hypothesis. Notably, the male performance in jigsaw puzzle solving was attributed to their sex and mediated by their higher MRT scores. These results yielded two key insights. First, they indicate a dissociation between these two visuospatial abilities, jigsaw puzzle solving and mental rotation; and second, female and male participants capitalize on their distinct cognitive strengths when solving visuospatial tasks.

Solving puzzles using knowledge-based automation: biomimicry of human solvers

The human brain’s remarkable efficiency in solving puzzles through pictorial information processing serves as a valuable inspiration for computational puzzle solving. In this study, we present a nucleation algorithm for automated puzzle solving, developed based on statistical analysis of an empirical database. This algorithm effectively solves puzzles by choosing pieces with infrequent and iridescent edges as nucleation centers, followed by the identification of neighboring pieces with high resemblances from the remaining puzzle pieces. For the 8 different pictures examined in this study, both empirical data and computer simulations consistently demonstrate a power-law relationship between solving time and the number of puzzle pieces, with an exponent less than 2. We explain this relationship through the nucleation model and explore how the exponent is influenced by the color pattern of the puzzle picture. Moreover, our investigation of puzzle-solving processes reveals distinct principal pathways, akin to protein folding behavior. Our study contributes to the development of a cognitive model for human puzzle solving and color pattern recognition.

Pictorial and Apictorial Polygonal Jigsaw Puzzles from Arbitrary Number of Crossing Cuts

Jigsaw puzzle solving, the problem of constructing a coherent whole from a set of non-overlapping unordered visual fragments, is fundamental to numerous applications, and yet most of the literature of the last two decades has focused thus far on less realistic puzzles whose pieces are identical squares. Here we formalize a new type of jigsaw puzzle where the pieces are general convex polygons generated by cutting through a global polygonal shape/image with an arbitrary number of straight cuts, a generation model inspired by the celebrated Lazy caterer’s sequence. We analyze the theoretical properties of such puzzles, including the inherent challenges in solving them once pieces are contaminated with geometrical noise. To cope with such difficulties and obtain tractable solutions, we abstract the problem as a multi-body spring-mass dynamical system endowed with hierarchical loop constraints and a layered reconstruction process. We define evaluation metrics and present experimental results on both apictorial and pictorial puzzles to show that they are solvable completely automatically.

EnigmaScan: Streamlining Matrix Puzzle Solving through Image Recognition

EnigmaScan: Streamlining Matrix Puzzle Solving through Image Recognition

THE ROLE OF THERAPEUTIC CHESS IN EDUCATIONTRAUMAS AND PEDAGOGY

In this article as a result of the "Therapeutic Chess" project, which is a new teaching technique designed with an interdisciplinary method on kindergarten-primary school children, the increase in children's interest in chess lesson and expressing themselves in mathematics, music, painting, puzzle solving, board games and philosophical level. It has been observed that the students' ability to learn how to act has increased, and it is aimed to contribute to the socialization, normalization process of children who have experienced trauma, to introduce this technique to more widespread use and to transfer it to teachers.

Exploring Inter-Brain Electroencephalogram Patterns for Social Cognitive Assessment During Jigsaw Puzzle Solving.

Social interaction enables the smooth progression of our daily lives. Mounting evidence from recent hyperscanning neuroimaging studies indicates that key components of social behavior can be evaluated using inter-brain oscillations and connectivity. However, mapping out inter-brain networks and developing neurocognitive theories that explain how humans co-create and share information during social interaction remains challenging. In this study, we developed a jigsaw puzzle-solving game with hyperscanning electroencephalography (EEG) signals recorded to investigate inter-brain activities during social interactions involving cooperation and competition. Participants were recruited and paired into dyads to participate in the multiplayer jigsaw puzzle game with 32-channel EEG signals recorded. The corresponding event-related potentials (ERPs), brain oscillations, and inter-brain functional connectivity were analyzed. The results showed different ERP morphologies of P3 patterns in competitive and cooperative contexts, and brain oscillations in the low-frequency band may be an indicator of social cognitive activities. Furthermore, increased inter-brain functional connectivity in the delta, theta, alpha, and beta frequency bands was observed in the competition mode compared to the cooperation mode. By presenting comparable and valid hyperscanning EEG results alongside those of previous studies using traditional paradigms, this study demonstrates the potential of utilizing hyperscanning techniques in real-life game-playing scenarios to quantitatively assess social cognitive interactions involving cooperation and competition. Our approach offers a promising platform with potential applications in the flexible assessment of psychiatric disorders related to social functioning.

Optimized real-time sudoku puzzle solving and solution overlay on images and videos using advanced computer vision and TensorFlow

This paper presents a novel approach to solving Sudoku puzzles using computer vision techniques. Sudoku, a popular logic-based number placement game, poses an interesting challenge for automated solutions. The proposed Sudoku solver employs image processing and computer vision algorithms to extract the puzzle grid from a given image. The system then employs optical character recognition (OCR) to recognize and digitize the numbers within the grid. The image preprocessing involves techniques such as edge detection, contour identification, and perspective transformation to enhance the accuracy of grid extraction. Subsequently, OCR is applied to recognize the digits within each cell of the Sudoku grid. The solver then utilizes a backtracking algorithm to systematically fill in the missing numbers while adhering to the rules of Sudoku. The computer vision-based Sudoku solver offers advantages in terms of versatility, as it can handle puzzles of varying difficulty levels and appearances. The system's performance is evaluated on a diverse set of Sudoku puzzles, demonstrating its effectiveness in accurately solving puzzles with different grid sizes and layouts. The results indicate that the proposed approach achieves competitive solving times while maintaining a high level of accuracy. The presented Sudoku solver serves as a testament to the potential of computer vision in solving complex puzzles, opening avenues for further research in the integration of artificial intelligence and image processing for recreational problem-solving applications.

Rule-based explaining module: Enhancing the interpretability of recurrent relational network in Sudoku solving

Computer vision has gained significant attention in the field of information technology due to its widespread application that addresses real-world challenges, surpassing human intelligence in tasks such as image recognition, classification, natural language processing, and even game playing. Sudoku, a challenging puzzle that has captivated many people, exhibits a complexity that has attracted researchers to leverage deep learning techniques for its solution. However, the reliance on black-box neural networks has raised concerns about transparency and explainability. In response to this challenge, we present the Rule-based Explaining Module (REM), which is designed to provide explanations of the decision-making processes using Recurrent Relational Networks (RRN). Our proposed methodology is to bridge the gap between complex RRN models and human understanding by unveiling the specific rules applied by the model at each stage of the Sudoku puzzle solving process. Evaluating REM on the Minimum Sudoku dataset, we achieved an accuracy of over 98.00%.

Verbal-analytical rather than visuo-spatial Raven's puzzle solving favors Raven's-like puzzle generation.

Raven's advanced progressive matrices (APM) comprise two types of representational codes, namely visuo-spatial and verbal-analytical, that are used to solve APM puzzles. Studies using analytical, behavioral, and imaging methods have supported the multidimensional perspectives of APM puzzles. The visuo-spatial code is expected to recruit operations more responsive to the visual perception tasks. In contrast, the verbal-analytical code is expected to use operations more responsive to the logical reasoning task and may entail different cognitive strategies. Acknowledging different representational codes used in APM puzzle-solving is critical for a better understanding of APM's performance and their relationship with other tasks, especially creative reasoning. We used the eye-tracking method to investigate the role of two representational codes, visuo-spatial and verbal-analytical, in strategies involved in solving APM puzzles and in generating an APM-like puzzle by using a creative-reasoning task (CRT). Participants took longer time to complete the verbal-analytical than visuo-spatial puzzles. In addition, visuo-analytical than visual-spatial puzzles showed higher progressive and regressive saccade counts, suggesting the use of more response elimination than constructive matching strategies employed while solving verbal-analytical than visuo-spatial puzzles. We observed higher CRT scores when it followed verbal-analytical (Mdn = 84) than visuo-spatial (Mdn = 73) APM puzzles, suggesting puzzle-solving specific strategies affect puzzle-creating task performance. The advantage of verbal-analytical over visuo-spatial puzzle-solving has been discussed in light of shared cognitive processing between APM puzzle-solving and APM-like puzzle-creating task performance.

Changing Rules While Playing: On the Affective Potential of In-Game Rule Changes

Abstract The essay deals with a powerful feature of games, the phenomenon of in-game rule change or, more specifically, the occasional, even if regulated, breach of contract between the game and the player, which has a dramatic effect on the player’s experience. I attempt to define this phenomenon through the concepts of operational rules, meaningful play, board state and victory points, and then review our perceptions and preconceptions of board games, which are seen in most cases as a balanced means of social puzzle solving. The examples discussed demonstrate that some modern games are far more interactive, dynamic, and less balanced than we might imagine, which makes rule changes during play possible and dramatic. (GZK)

Research on the Reasons for the Success of Genshin Impact

Genshin Impact has taken the gaming world by storm. Developed and published by miHoYo, this game has captured the attention of gamers worldwide with its stunning visuals, interesting storyline, and engaging gameplay. Genshin Impact is an open-world game that allows players to explore a vast world and interact with a variety of characters. The game also has very diverse mechanics, including combat, adventure, puzzle solving, character development, and more. Players need to constantly upgrade their character level, combat ability, and equipment level in order to better explore and complete quests in the game. This paper will explore the world of Genshin Impact, using its scenario design, character design, and analysis of cultural output to examine why Genshin Impact is so popular. Its diverse mechanics, beautiful graphics, rich character settings and storyline, unique Chinese elements, exquisite graphics and sound effects, and Genshin Impact's positive cultural output have made the game much loved and sought after by players.

DESIGNING HORROR SURVIVAL MYSTERY GAME ”REMEMBER THE DAYS” COMBINING 2D AND 3D ELEMENTS.

“Remember The Days” is a survival horror single player game for Windows, that combines 2D and 3D elements with a pixelated and low poly art style. As a detective trapped in an old abandoned haunted house, players must solve puzzles, and be able to manage limited resources to survive and escape. The game’s immersive storytelling is told through environmental cues and artifacts, complemented by a haunting soundtrack and ambient sounds that add to the eerie atmosphere. This game will bring a spine-tingling horror experience of exploration, puzzle solving, and survival. The game will be develop using Unity Engine and C as the main programming language.

Is scientific knowledge socially constructed? A Bayesian account of Laboratory Life.

In the book Laboratory Life Latour and Woolgar present an account of how scientific "facts" are formed through a process of microsocial interactions among individuals and "inscription devices" in the lab initially described as social construction. The process moves through a series of steps during which the details and nature of the object become more and more certain until all qualifications are dropped, and the "fact" emerges as secure scientific knowledge. An alternative to this account is described based on a Bayesian probabilistic framework which arrives at the same end point. The motive force for the constructivist approach appears to involve social processes of convincing colleagues while the Bayesian approach relies on the consistency of theory and evidence as judged by the participants. The role of social processes is discussed in Bayesian terms, the acquisition and asymmetry of information, and its analogy to puzzle solving. Some parallels between the Bayesian and constructivist accounts are noted especially in relation to information theory.

Siamese-Discriminant Deep Reinforcement Learning for Solving Jigsaw Puzzles with Large Eroded Gaps

Jigsaw puzzle solving has recently become an emerging research area. The developed techniques have been widely used in applications beyond puzzle solving. This paper focuses on solving Jigsaw Puzzles with Large Eroded Gaps (JPwLEG). We formulate the puzzle reassembly as a combinatorial optimization problem and propose a Siamese-Discriminant Deep Reinforcement Learning (SD2RL) to solve it. A Deep Q-network (DQN) is designed to visually understand the puzzles, which consists of two sets of Siamese Discriminant Networks, one set to perceive the pairwise relations between vertical neighbors and another set for horizontal neighbors. The proposed DQN considers not only the evidence from the incumbent fragment but also the support from its four neighbors. The DQN is trained using replay experience with carefully designed rewards to guide the search for a sequence of fragment swaps to reach the correct puzzle solution. Two JPwLEG datasets are constructed to evaluate the proposed method, and the experimental results show that the proposed SD2RL significantly outperforms state-of-the-art methods.

Educational Game Scenario Model Based On Imperative Game Goal Typology

Educational games are digital products resulting from the application of game-based learning concepts. The main factors forming educational games include combining a learning subject with challenging elements and engaging visualization. Most educational games translate the challenge component of the game in the form of multiple choice quizzes, questions, answers, or puzzle solving. This result causes the mechanical structure of educational games less interesting than the mechanics of entertainment games. This paper proposes a scenario development model for educational games derived from the game goal typology commonly found in entertainment games. This model is expected to be a reference for developing educational game scenarios that can enrich the types of mechanics and increase the engagement of an educational game. The proposed model in the research has been tested on two educational games, both of which received high attractiveness scores of 1.632 and 1.542 respectively on the User Experience Question (UEQ) scale.

Review of Programming for the Puzzled

This book is about learning to program while solving puzzles. The motivation for the author to use this approach is to gain better attention from students, many of whom do not like to program just for the sake of programming. As mentioned in the informative preface, this book reflects the author's "attempt at teaching programming by building a bridge between the recreational world of algorithmic puzzles and the pragmatic world of computer programming," because the same analytical skills required for puzzle solving are also needed for "translating specifications into programming constructs, as well as discovering errors in early versions of code, called the debugging process."

Enabling scalable and unlinkable payment channel hubs with oblivious puzzle transfer

Payment channel networks (PCNs) are effective techniques for extending the scalability of cryptocurrencies. It achieves this by establishing a direct off-line channel from the sender to the receiver, going through one intermediary (aka. the hubs). In such scenarios, the hubs know the origin and destination of each transaction flowing through them, which jeopardizes the privacy of the underlying systems. Unfortunately, former efforts in ensuring transaction unlinkability either rely on trusted mixing services, are inefficient constructed (e.g., constructed inefficient cryptographic primitives), or have limited applicability. In this paper, we present ObliHub, an efficient payment channel scheme that conceals transaction direction information to the hubs. The core technique of ObliHub in achieving unlinkability is our tailored oblivious puzzle transfer protocol (OPT), which enables puzzle solving among the payer, the hub, and the receiver to be conducted in an oblivious manner – the hub center neither knows where a puzzle hint came from nor who acquired it. The implementation of ObliHub only requires efficient cryptographic primitives, and compared with A2L (a state-of-the-art Bitcoin-compatible PCH using homomorphic encryption), ObliHub saves 0.2 seconds in computation time over previous solutions and improves transfer throughput. Besides, our scheme is in accord with Universal Composability (UC) framework and we provide a comprehensive security analysis of it.