Mining Balanced Sequential Patterns in RTS Games 1

International audienceThe video game industry has grown enormously over the last twenty years, bringing new challenges to the artificial intelli-gence and data analysis communities. We tackle here the problem of automatic discovery of strategies in real-time strategy games through pattern mining. Such patterns are the basic units for many tasks such as automated agent design, but also to build tools for the profession-ally played video games in the electronic sports scene. Our formal-ization relies on a sequential pattern mining approach and a novel measure, the balance measure, telling how a strategy is likely to win. We experiment our methodology on a real-time strategy game that is professionally played in the electronic sport community

A Pattern Mining Approach to Study Strategy Balance in RTS Games

International audienceWhereas purest strategic games such as Go and Chess seem timeless, the lifetime of a video game is short, influenced by popular culture, trends, boredom and technological innovations. Even the important budget and de- velopments allocated by editors cannot guarantee a timeless success. Instead, novelties and corrections are proposed to extend an inevitably bounded lifetime. Novelties can unexpectedly break the balance of a game, as players can discover unbalanced strategies that developers did not take into account. In the new context of electronic sports, an important challenge is to be able to detect game balance issues. In this article, we consider real time strategy games (RTS) and present an efficient pattern mining algorithm as a basic tool for game balance designers that enables one to search for unbalanced strategies in historical data through a Knowledge Discovery in Databases process (KDD). We experiment with our algorithm on StarCraft II historical data, played professionally as an electronic sport

Goal Reasoning: Papers from the ACS Workshop

This technical report contains the 14 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2015 Conference on Advances in Cognitive Systems (ACS-15) in Atlanta, Georgia on 28 May 2015. This is the fourth in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy; the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012; and the third was the Goal Reasoning Workshop at ACS-13 in Baltimore, Maryland in December 2013

Biopsychosocial Data Analytics and Modeling

Sustained customisation of digital health intervention (DHI) programs, in the context of community health engagement, requires strong integration of multi-sourced interdisciplinary biopsychosocial health data. The biopsychosocial model is built upon the idea that biological, psychological and social processes are integrally and interactively involved in physical health and illness. One of the longstanding challenges of dealing with healthcare data is the wide variety of data generated from different sources and the increasing need to learn actionable insights that drive performance improvement. The growth of information and communication technology has led to the increased use of DHI programs. These programs use an observational methodology that helps researchers to study the everyday behaviour of participants during the course of the program by analysing data generated from digital tools such as wearables, online surveys and ecological momentary assessment (EMA). Combined with data reported from biological and psychological tests, this provides rich and unique biopsychosocial data. There is a strong need to review and apply novel approaches to combining biopsychosocial data from a methodological perspective. Although some studies have used data analytics in research on clinical trial data generated from digital interventions, data analytics on biopsychosocial data generated from DHI programs is limited. The study in this thesis develops and implements innovative approaches for analysing the existing unique and rich biopsychosocial data generated from the wellness study, a DHI program conducted by the School of Science, Psychology and Sport at Federation University. The characteristics of variety, value and veracity that usually describe big data are also relevant to the biopsychosocial data handled in this thesis. These historical, retrospective real-life biopsychosocial data provide fertile ground for research through the use of data analytics to discover patterns hidden in the data and to obtain new knowledge. This thesis presents the studies carried out on three aspects of biopsychosocial research. First, we present the salient traits of the three components - biological, psychological and social - of biopsychosocial research. Next, we investigate the challenges of pre-processing biopsychosocial data, placing special emphasis on the time-series data generated from wearable sensor devices. Finally, we present the application of statistical and machine learning (ML) tools to integrate variables from the biopsychosocial disciplines to build a predictive model. The first chapter presents the salient features of the biopsychosocial data for each discipline. The second chapter presents the challenges of pre-processing biopsychosocial data, focusing on the time-series data generated from wearable sensor devices. The third chapter uses statistical and ML tools to integrate variables from the biopsychosocial disciplines to build a predictive model. Among its other important analyses and results, the key contributions of the research described in this thesis include the following: 1. using gamma distribution to model neurocognitive reaction time data that presents interesting properties (skewness and kurtosis for the data distribution) 2. using novel ‘peak heart-rate’ count metric to quantify ‘biological’ stress 3. using the ML approach to evaluate DHIs 4. using a recurrent neural network (RNN) and long short-term memory (LSTM) data prediction model to predict Difficulties in Emotion Regulation Scale (DERS) and primary emotion (PE) using wearable sensor data.Doctor of Philosoph

From learning to e-learning: mining educational data. A novel, data-driven approach to evaluate individual differences in students’ interaction with learning technology

In recent years, learning technology has become a very important addition to the toolkit of instructors at any level of education and training. Not only offered as a substitute in distance education, but often complementing traditional delivery methods, e-learning is considered an important component of modern pedagogy. Particularly in the last decade, learning technology has seen a very rapid growth following the large-scale development and deployment of e-learning financed by both Governments and commercial enterprises. These turned e-learning into one of the most profitable sectors of the new century, especially in recession times when education and retraining have become even more important and a need to maximise resources is forced by the need for savings. Interestingly, however, evaluation of e-learning has been primarily based on the consideration of users’ satisfaction and usability metrics (i.e. system engineering perspective) or on the outcomes of learning (i.e. gains in grades/task performance). Both of these are too narrow to provide a reliable effect of the real impact of learning technology on the learning processes and lead to inconsistent findings. The key purpose of this thesis is to propose a novel, data-driven framework and methodology to understand the effect of e-learning by evaluating the utility and effectiveness of e-learning systems in the context of higher education, and specifically, in the teaching of psychology courses. The concept of learning is limited to its relevance for students’ learning in courses taught using a mixture of traditional methods and online tools tailored to enhance teaching. The scope of elearning is intended in a blended method of delivery of teaching. A large sample of over 2000 students taking psychology courses in year 1 and year 2 was considered over a span of 5 five years, also providing the scope for the analysis of some longitudinal sub-samples. The analysis is accomplished using a psychologically grounded approach to evaluation, partially informed by a cognitive/ behavioural perspective (online usage) and a differential perspective (measures of cognitive and learning styles). Relations between behaviours, styles and academic performance are also considered, giving an insight and a direct comparison with existing literature. The methodology adopted draws heavily from data mining techniques to provide a rich characterisation of students/users in this particular context from the combination of three types of metrics: cognitive and learning styles, online usage and academic performance. Four different instruments are used to characterise styles: ASSIST (Approaches to learning, Entwistle), CSI (Cognitive Styles Inventory, Allinson & Hayes), TSI (Thinking Styles Inventory and the mental self-government theory, Sternberg) and VICS-WA (Verbal/Imager and Wholistc/Analytic Cognitive style, Riding, Peterson) which were intentionally selected to provide a varied set of tools. Online usage, spanning over the entire academic year for each student, is analysed applying web usage mining (WUM) techniques and is observed through different layers of interpretation accounting for behaviours from the single clicks to a student’s intentions in a single session. Academic performance was collated from the students’ records giving an insight in the end-of-year grades, but also into specific coursework submissions during the whole academic year allowing for a temporal matching of online use and assessment. The varied metrics used and data mining techniques applied provide a novel evaluation framework based on a rich profile of the learner, which in turn offers a valuable alternative to regression methods as a mean to interpret relations between metrics. Patterns emerging from styles and the way online material is used over time, proved to be valuable in discriminating differences in academic performance and useful in this context to identify significant group differences in both usage and academic performance. As a result, the understanding of the relations between e-learning usage, styles and academic performance has important practical implications to enhance students’ learning experience, in the automation of learning systems and to inform policymakers of the effects of learning technology has from a user and learner-centred approach to learning and studying. The success of the application of data mining methods offers an excellent starting point to explore further a data-driven approach to evaluation, support informed design processes of e-learning and to deliver suitable interventions to ensure better learning outcomes and provide an efficient system for institutions and organization to maximise the impact of learning technology for teaching and training

On the Design, Implementation and Application of Novel Multi-disciplinary Techniques for explaining Artificial Intelligence Models

284 p.Artificial Intelligence is a non-stopping field of research that has experienced some incredible growth lastdecades. Some of the reasons for this apparently exponential growth are the improvements incomputational power, sensing capabilities and data storage which results in a huge increment on dataavailability. However, this growth has been mostly led by a performance-based mindset that has pushedmodels towards a black-box nature. The performance prowess of these methods along with the risingdemand for their implementation has triggered the birth of a new research field. Explainable ArtificialIntelligence. As any new field, XAI falls short in cohesiveness. Added the consequences of dealing withconcepts that are not from natural sciences (explanations) the tumultuous scene is palpable. This thesiscontributes to the field from two different perspectives. A theoretical one and a practical one. The formeris based on a profound literature review that resulted in two main contributions: 1) the proposition of anew definition for Explainable Artificial Intelligence and 2) the creation of a new taxonomy for the field.The latter is composed of two XAI frameworks that accommodate in some of the raging gaps found field,namely: 1) XAI framework for Echo State Networks and 2) XAI framework for the generation ofcounterfactual. The first accounts for the gap concerning Randomized neural networks since they havenever been considered within the field of XAI. Unfortunately, choosing the right parameters to initializethese reservoirs falls a bit on the side of luck and past experience of the scientist and less on that of soundreasoning. The current approach for assessing whether a reservoir is suited for a particular task is toobserve if it yields accurate results, either by handcrafting the values of the reservoir parameters or byautomating their configuration via an external optimizer. All in all, this poses tough questions to addresswhen developing an ESN for a certain application, since knowing whether the created structure is optimalfor the problem at hand is not possible without actually training it. However, some of the main concernsfor not pursuing their application is related to the mistrust generated by their black-box" nature. Thesecond presents a new paradigm to treat counterfactual generation. Among the alternatives to reach auniversal understanding of model explanations, counterfactual examples is arguably the one that bestconforms to human understanding principles when faced with unknown phenomena. Indeed, discerningwhat would happen should the initial conditions differ in a plausible fashion is a mechanism oftenadopted by human when attempting at understanding any unknown. The search for counterfactualsproposed in this thesis is governed by three different objectives. Opposed to the classical approach inwhich counterfactuals are just generated following a minimum distance approach of some type, thisframework allows for an in-depth analysis of a target model by means of counterfactuals responding to:Adversarial Power, Plausibility and Change Intensity

The Invention of Good Games: Understanding Learning Design in Commercial Videogames

This work sought to help inform the design of educational digital games by the studying the design of successful commercial videogames. The main thesis question was: How does a commercially and critically successful modern video game support the learning that players must accomplish in order to succeed in the game (i.e. get to the end or win)? This work takes a two-pronged approach to supporting the main argument, which is that the reason we can learn about designing educational games by studying commercial games is that people already learn from games and the best ones are already quite effective at teaching players what they need to learn in order to succeed in the game. The first part of the research establishes a foundation for the argument, namely that accepted pedagogy can be found in existing commercial games. The second part of the work proposes new methods for analysing games that can uncover mechanisms used to support learning in games which can be employed even if those games were not originally designed as educational objects. In order to support the claim that ‘good’ commercial videogames already embody elements of sound pedagogy an explicit connection is made between game design and formally accepted theory and models in teaching and learning. During this phase of the work a significant concern was raised regarding the classification of games as ‘good’, so a new methodology using Borda Counts was devised and tested that combines various disjoint subjective reviews and rankings from disparate sources in non-trivial manner that accounts for relative standings. Complementary to that was a meta-analysis of the criteria used to select games chosen as subjects of study as reported by researchers. Then, several games were chosen using this new ranking method and analysed using another new methodology that was designed for this work, called Instructional Ethology. This is a new methodology for game design deconstruction and analysis that would allows the extraction of information about mechanisms used to support learning. This methodology combines behavioural and structural analysis to examine how commercial games support learning by examining the game itself from the perspective of what the game does. Further, this methodology can be applied to the analysis of any software system and offers a new approach to studying any interactive software. The results of the present study offered new insights into how several highly successful commercial games support players while they learn what they must learn in order to succeed in those games. A new design model was proposed, known as the 'Magic Bullet' that allows designers to visualize the relative proportions of potential learning in a game to assess the potential of a design