Somnuk Phon-Amnuaisuk

Evolving and Discovering Tetris Gameplay Strategies

Abstract This work is motivated by one of the important characteristics of an intelligent system: the ability to automatically discover new knowledge. This work employs an evolutionary technique to search for good solutions and then employs a data mining technique to extract knowledge implicitly encoded in the evolved solutions. In this paper, Genetic Algorithm (GA) is employed to evolve a solution for randomly generated tetromino sequences. In contrast to previous works in this area where an evolutionary strategy was employed to evolve weights (i.e., preferences) of predefined evaluation functions which were then used to determine players’ actions, we directly evolve the gameplay actions. Each chromosome represents a plausible gameplay strategy and its fitness is evaluated by simulating the actual gameplay using gameplay instructions from each chromosome. In each simulation, 13 attributes relevant to the gameplay, i.e., contour patterns and actions of each tetromino, are recorded from the best evolved games. This produces 6583 instances which we then apply Apriori algorithm to extract association patterns from them. The result illustrates that sensible gameplay strategies can be successfully extracted from evolved games even though the GA was not informed about these gameplay strategies.

On the binaural brain entrainment indicating lower heart rate variability.

Products based on binaural beat technology are popular as these claim to relax the user by altering (i.e. entraining) the brain’s neuronal rhythm and a Google search will result in more than a million hits [1]. Whilst this is possible in certain approaches, for example in photic frequency following effects such as in brain-computer interfaces [2]; binaural brain entrainments uses two beats (usually sinusoidal tones) that are different in frequency by a small amount that generates a third pseudo-rhythm at the difference of the two frequencies [3]. Furthermore, binaural brain entrainment is also claimed to allow altered states of consciousness and hemispheric synchronisation [4]. However, the effects of this entrainment on the cardiac rhythm appear to be understudied, though equally important as the effects on the brain. Hence, the investigation here is set out with this aim.

Exploring Swarm-Based Visual Effects

In this paper, we explore the visual effects of animated 2D line strokes and 3D cubes. A given 2D image is segmented into either 2D line strokes or 3D cubes. Each segmented object (i.e., line stroke or each cube) is initialised with the position and the colour of the corresponding pixel in the image. The program animates these objects using the boid framework. This simulates a flocking behavior of line strokes in a 2D space and cubes in a 3D space. In this implementation the animation runs in a cycle from the disintegration of the original image to a swarm of line strokes or 3D cubes, then the swarm moves about and then integrates back into the original image.

Computational Intelligence in Information Systems

This book constitutes the refereed proceedings of the Fourth International Neural Network Symposia series on Computational Intelligence in Information Systems, INNS-CIIS 2014, held in Bandar Seri Begawan, Brunei in November 2014. INNS-CIIS aims to provide a platform for researchers to exchange the latest ideas and present the most current research advances in general areas related to computational intelligence and its applications in various domains. The 34 revised full papers presented in this book have been carefully reviewed and selected from 72 submissions. They cover a wide range of topics and application areas in computational intelligence and informatics.

Learning to Play Tetris from Examples

We model a Tetris player using Artificial Neural Network (ANN). In contrast to most previous works which learned weights of predefined heuristic functions, our model learns the actual actions i.e., given a board state and a tetrominoe, where the piece should be placed on the board. The problem is formulated as a classification problem where the model learns to associate a board state with fruitful actions. We compare an ANN player with a random player and a Best First Search (BFS) player. The random player and the BFS player provide baselines for uninformed search and informed search, while the ANN player learns from the examples extracted from BFS runs and its performance lies be- tween both baselines. We observe that generalisation appears to be very hard from the nature of the game itself. Fitting Tetrimino pieces together demands an exact match. This means we cannot expect similar solutions to closely related patterns. In this paper, we explore the problem from a soft computing perspective, present our experimental design and provide a critical discussion of the results of our experiment.

GA-Tetris Bot: Evolving a Better Tetris Gameplay Using Adaptive Evaluation Scheme

Genetic Algorithm (GA) is employed to evolve a solution for any given tetromino sequence. In contrast to previous works in this area where an evolutionary strategy was employed to evolve weights (i.e., preferences) of predefined evaluation functions which then were used to determine players’ actions, we directly evolve the actions. Each chromosome represents a plausible gameplay strategy and its fitness is evaluated by simulating the game and rating the gameplay quality using two fitness evaluation approaches: evaluating the whole board at once and evaluating local parts of the board in which they will be expanded to the whole board as the evolution progresses. We compare the results of these two evaluation tactics and also compare the evolved gameplay with actual human gameplay.

Transcribing Bach Chorales Using Particle Swarm Optimisations

This paper reports a novel application of particle swarm optimisation to polyphonic transcription task. The system transforms an input audio into activation strength of pitches in the desired range. This transformation begins with audio information in time-domain to frequency-domain and finally, to activation strength of pitches (a.k.a. piano-roll representation). We can infer the likely sounding pitches by comparing the observed activation strength of input audio to reference Tone-models. Although each Tone-model is learned offline from the pitches one wish to perform transcription with, this process often only approximates the Tone-model characteristics due to the variations in volume and other effects introduced from the manner of note executions. Hence, predicting sounding notes based solely on Tone-models gives inaccurate predictions. Here, we apply PSO to search for an optimum aggregation of different predicted pitches that best represents the input activation strength. We describe our problem formulation and the design of our approach. The experimental results show our approach to be of potential in the task of polyphonic transcription.

Evolving 3D Models Using Interactive Genetic Algorithms and L-Systems

The modeling of 3D objects is popularly obtained using a shell/boundary approach. This involves manipulating vertices and planes in a three-dimensional space using computers. Manually creating a 3D model in this way allows a designer full control over the creative processes but at the expense of long working hours. In this work, we explore the hybrid framework between the Interactive Genetic Algorithm (IGA) and the L-system. The L-system generates a 3D model from its production rules and the IGA evolves the 3D model by evolving the L-system’s production rules. In this study, we investigate whether the approach can successfully steer the 3D model design using subjective preference feedback from users. We analyze and discuss the creative processes in the proposed hybrid system and present the models generated by our approach.

Multi-disciplinary Trends in Artificial Intelligence

Inference for Probabilistic Argumentation has been focusing on computing the probability that a given argument or proposition is acceptable. In this paper, we formalize such tasks as computing marginal acceptability probabilities given some evidence and learning probabilistic parameters from a dataset. We then show that algorithms for them can be composed by finely joining a basic PA inference algorithm and existing algorithms for the corresponding tasks in Probabilistic Logic Programming or even Bayesian networks.

Learning to Navigate in a 3D Environment

In this paper, we investigate the knowledge acquisition and the learning ability of an agent in a three-dimensional (3D) environment using data mining techniques. We apply three data mining techniques: naive Bayes, decision tree and apriori; to a human-controlled navigation and then investigate the characteristic of knowledge discovered from each of these techniques. The results shows that the agent is able to learn to navigate automatically in the environment but with different outcomes and limitations.