Dimitris Kalles

ANALYZING STUDENT PERFORMANCE IN DISTANCE LEARNING WITH GENETIC ALGORITHMS AND DECISION TREES

Students that enroll in the undergraduate program on informatics at the Hellenic Open University (HOU) demonstrate significant difficulty in advancing beyond the introductory course. We have embarked in an effort to analyze their academic performance throughout the academic year, as measured by homework assignments, and attempt to derive short rules that explain and predict success or failure in the final exams. In this paper we review previous approaches, compare them with genetic algorithm-based induction of decision trees, and argue why our approach has a potential for developing into an alert tool.

Player Co-Modelling in a Strategy Board Game: Discovering How to Play Fast

In this article we experiment with a 2-player strategy board game where playing models are developed using reinforcement learning and neural networks. The models are developed to speed up automatic game development based on human involvement at varying levels of sophistication and density when compared to fully autonomous playing. The experimental results suggest a clear and measurable association between the ability to win games and the ability to do that fast, while at the same time demonstrating that there is a minimum level of human involvement beyond which no learning really occurs.

Social Reinforcement Learning in Game Playing

In this work we discuss Social Reinforcement Learning on self-trained agents. We simulate social learning by implementing a tournament on an existing board game that utilizes reinforcement learning for playing and learning. The socially trained agents are compared to self-trained agents and their superior performance is noted. The findings and the infrastructure requirements mandate the development of a Social Reinforcement Learning Multi-Agent-Based Simulation platform.

A Learning Analytics Methodology for Student Profiling

On a daily basis, a large amount of data is gathered through the participation of students in e-learning environments. This wealth of data is an invaluable asset to researchers as they can utilize it in order to generate conclusions and identify hidden patterns and trends by using big data analytics techniques. The purpose of this study is a threefold analysis of the data that are related to the participation of students in the online forums of their University. In one hand the content of the messages posted in these fora can be efficiently analyzed by text mining techniques. On the other hand, the network of students interacting through a forum can be adequately processed through social network analysis techniques. Still, the combined knowledge attained from both of the aforementioned techniques, can provide educators with practical and valuable information for the evaluation of the learning process, especially in a distance learning environment. The study was conducted by using real data originating from the online forums of the Hellenic Open University (HOU). The analysis of the data has been accomplished by using the R and the Weka tools, in order to analyze the structure and the content of the exchanged messages in these fora as well as to model the interaction of the students in the discussion threads.

Building a social multi-agent system simulation management toolbox

The development of a novel Multi-Agent-Based Social Simulation (MABS) platform is undertaken after considering the advantages and disadvantages of existing platforms. We study their adaptability and usage in an existing strategy board game and attempt to model tournaments in social environments. To facilitate this experimentation, we arrive at the need to develop a new platform which features dynamic handling of game objects at runtime.

A platform for large-scale game-playing multi-agent systems on a high performance computing infrastructure

The simulation of societies requires vast amounts of computing resources, which must be managed over distributed or high performance computing infrastructures to provide for cost-effective experimentation. To that end, this paper presents a novel platform for the segmentation and management of social simulation experiments in game-playing multi-agent systems; the platform, also serves as a working proof of concept for similar experiments. The platform is managed through a web-based graphical user interface, to combine the advantages of powerful grid infrastructure middleware and sophisticated workflow systems in a way that some generic functionality is sacrificed for the benefit of obtaining a smooth and brief learning curve, without compromising security. The paper sets out the architecture and implementation details of the platform and demonstrates its use with two sample games, RLGame and Rock Scissors Paper, to underline the scale of the experiments and to indicate the class of social simulation problems that it can help investigate. The platform can be loosely coupled with analytics software for data mining; for our sample problems, this analysis leads to associating the learning mechanism each agent employs with its eventual performance ranking.

EXAMPLES AS INTERACTION: ON HUMANS TEACHING A COMPUTER TO PLAY A GAME

This paper reviews an experiment in human-computer interaction, where interaction takes place when humans attempt to teach a computer to play a strategy board game. We show that while individually learned models can be shown to improve the playing performance of the computer, their straightforward composition results in diluting what was earlier learned. This observation suggests that interaction cannot be easily distributed when one hopes to harness multiple human experts to develop a quality computer player. This is related to similar approaches in robot task learning and to classic approaches to human learning and reinforces the need to develop tools that facilitate the mix of human-based tuition and computer self-learning.

Human Rating Methods on Multi-agent Systems

Modern artificial intelligence approaches study game-playing agents in multi-agent social environments, in order to better simulate the real world playing behaviors; these approaches have already produced promising results. In this paper we present the results of applying human rating systems for competitive games with social activity, to evaluate synthetic agents’ performance in multi-agent systems. The widely used Elo and Glicko rating systems are tested in large-scale synthetic multi-agent game-playing social events, and their rating outcome is presented and analyzed.

Using Genetic Algorithms and Decision Trees for a posteriori Analysis and Evaluation of Tutoring Practices based on Student Failure Models

Many students who enrol in the undergraduate program on informatics at the Hellenic Open University (HOU) fail the introductory course exams and drop out. We analyze their academic performance, derive short rules that explain success or failure in the exams and use the accuracy of these rules to reflect on specific tutoring practices that could enhance success.

Learning in Multi Agent Social Environments with Opponent Models

We examine how synthetic agents interact in social environments employing a variety of agent training strategies against diverse opponents. Such agent training and playing methods indicate that quality playing relies more on the correct set-up of the learning mechanism than on experience. The experimentation provides valuable insight into the potential of an agent to compete against other agents in its environment and yet manage to also co-operate so that this particular environment allows for the emergence of a competitive champion agent, which will represent its group in further contests. Additionally, by investigating performance while constraining the number of moves we gain interesting insight into competitive learning and playing with resource constraints.