An Efficient Approach of Sokoban Level Generation

This article describes an algorithm for the procedural generation of the Sokoban puzzle. This algorithm can generate Sokoban levels according to the given parameters. The algorithm is meant to generate Sokoban levels efficiently but maintains acceptable quality. This article provides evidence that this algorithm is efficient and produces levels with a quality comparable with other existing levels which can be found online.The approach contains two parts. They are forward process and backward process. The forward process creates the goal position and empty room for the result. And the backward process makes initial status further away from its goal status. In each iteration of the forward or backward process, a box and a direction will be selected based on the strategies being set in the generator parameters. The number iterations are able to be configured by changing the parameters. With certain configuration, the generated levels can be with acceptable average quality. The detailed explanations are also included in this article

Generació procedural de contingut via Reinforcement Learning

L'objectiu d'aquest projecte és la creació de nivells per al joc de puzles Sokoban mitjançant Reinforcement Learning com a mètode de generació procedural de contingut. Per usar Reinforcement Learning, s'ha representat la generació de nivells com una tasca iterativa on, a cada iteració i mitjançant un sistema de recompenses, es modifica el tauler del joc per obtenir un nivell que tingui una solució. Al llarg del desenvolupament d'aquest treball s'han estudiat diverses estratègies i configuracions en els diferents mòduls del projecte per assolir un sistema capaç de generar nivells de manera estable. Per comprovar la dificultat dels nivells que es creen també s'ha desenvolupat un sistema de validació de la dificultat que relaciona informació extreta del nivell amb un conjunt de nivells etiquetats per dificultat. Els resultats del projecte mostren que el sistema és capaç de generar nivells de manera estable.The aim of this project is to create levels for the puzzle game Sokoban with Reinforcement learning as a Procedural Content Generation method. In order to use Reinforcement Learning, the level generation has been represented as an iterative task where in each step and with a reward system, the Sokoban board is modified to reach a solvable level. During the development of this project, different strategies and configurations have been studied to create Sokoban levels stably. To validate the difficulty of the levels created, a difficulty validation system has been developed, it relates information about the generated levels with a difficulty-labeled dataset of Sokoban levels. The results of the project show that the system is capable of generating solvable levels in a stable way.El objetivo de este proyecto es crear niveles para el juego de puzzles Sokoban mediante Reinforcement Learning como método de generación procedimental de contenido . Para utilizar Reinforcement Learning, la generación de niveles se ha representado como una tarea iterativa donde en cada iteración y con un sistema de recompensas, se modifica el tablero del juego para alcanzar un nivel solucionable. Durante el desarrollo de este proyecto se han estudiado diferentes estrategias y configuraciones en los distintos módulos para conseguir un sistema capaz de producir niveles consistentemente. Para validar la dificultad de los niveles creados, se ha desarrollado un sistema de validación de la dificultad que relaciona información sobre los niveles generados con un conjunto de datos etiquetados con la dificultad. Los resultados del proyecto muestran que el sistema es capaz de generar niveles solucionables de manera estable

Computer-based estimation of the difficulty of chess tactical problems

In intelligent tutoring systems, it is important for the system to understand how difficult a problem is for the student. However, it is an open question how to automatically assess such difficulty. The aim of our research is to find formalized measures of difficulty that could be used in automated assessment of the difficulty of a mental task for a human. We present a computational approach to estimating the difficulty of problems in which the difficulty arises from the combinatorial complexity of problems where a search among alternatives is required. Our approach is based on a computer heuristic search for building search trees that are “meaningful” from a human's point of view. We demonstrate that by analyzing properties of such trees, the program is capable to predict how difficult it would be for a human to solve the problem. In the experiments with chess tactical problems our program was able to differentiate between easy and difficult problems with a high level of accuracy

Computer-based estimation of the difficulty of chess tactical problems

In intelligent tutoring systems, it is important for the system to understand how difficult a problem is for the student. However, it is an open question how to automatically assess such difficulty. The aim of our research is to find formalized measures of difficulty that could be used in automated assessment of the difficulty of a mental task for a human. We present a computational approach to estimating the difficulty of problems in which the difficulty arises from the combinatorial complexity of problems where a search among alternatives is required. Our approach is based on a computer heuristic search for building search trees that are “meaningful” from a human's point of view. We demonstrate that by analyzing properties of such trees, the program is capable to predict how difficult it would be for a human to solve the problem. In the experiments with chess tactical problems our program was able to differentiate between easy and difficult problems with a high level of accuracy

Effective player guidance in logic puzzles

Pen & paper puzzle games are an extremely popular pastime, often enjoyed by demographics normally not considered to be ‘gamers’. They are increasingly used as ‘serious games’ and there has been extensive research into computationally generating and efficiently solving them. However, there have been few academic studies that have focused on the players themselves. Presenting an appropriate level of challenge to a player is essential for both player enjoyment and engagement. Providing appropriate assistance is an essential mechanic for making a game accessible to a variety of players. In this thesis, we investigate how players solve Progressive Pen & Paper Puzzle Games (PPPPs) and how to provide meaningful assistance that allows players to recover from being stuck, while not reducing the challenge to trivial levels. This thesis begins with a qualitative in-person study of Sudoku solving. This study demonstrates that, in contrast to all existing assumptions used to model players, players were unsystematic, idiosyncratic and error-prone. We then designed an entirely new approach to providing assistance in PPPPs, which guides players towards easier deductions rather than, as current systems do, completing the next cell for them. We implemented a novel hint system using our design, with the assessment of the challenge being done using Minimal Unsatisfiable Sets (MUSs). We conducted four studies, using two different PPPPs, that evaluated the efficacy of the novel hint system compared to the current hint approach. The studies demonstrated that our novel hint system was as helpful as the existing system while also improving the player experience and feeling less like cheating. Players also chose to use our novel hint system significantly more often. We have provided a new approach to providing assistance to PPPP players and demonstrated that players prefer it over existing approaches