Attributed grammatical evolution using shared memory spaces and dynamically typed semantic function specification

In this paper we introduce a new Grammatical Evolution (GE) system designed to support the speci cation of problem semantics in the form of attribute grammars (AG). We discuss the motivations behind our system design, from its use of shared memory spaces for attribute storage to the use of a dynamically type programming language, Python, to specify grammar semantics. After a brief analysis of some of the existing GE AG system we outline two sets of experiments carried out on four symbolic regression type (SR) problems. The rst set using a context free grammar (CFG) and second using an AG. After presenting the results of our experiments we highlight some of the potential areas for future performance improvements, using the new functionality that access to Python interpreter and storage of attributes in shared memory space provides

Procedural content generation for games using grammatical evolution and attribute grammars

The benefits of using some type of automation to reduce the time and cost of software development is generally accepted in most domains, video games1 included. While there are a wide variety of automation techniques available we shall focus on the technique used to produce content for games, commonly referred to as Procedural Content Generation (PCG). PCG uses some form of algorithmic approach to generate content, rather than doing so manually. The content produced using PCG needs to be meaningful within the context of the overall design aesthetic of a game, so assessment of the role the content produced will have within the game, along with the impact it will have on the overall design is extremely important if any PCG tool is to be of use to a game designer. Grammatical Evolution (GE), a grammar-based Evolutionary Algorithm (EA), is a widely used method for automatically generating solutions to a wide variety of problems across a diverse set of domains. GE operates by producing potential solutions (usually in the form of programs), to a predefined problem, by combining symbols specified in Backus-Naur Form (BNF), a convenient way of describing a Context Free Grammar (CFG). A CFG provides a means of specifying the syntax of programs, by outlining a set of rules which control the sequences of symbols allowed to appear in each program. While a CFG provides a means of specifying program syntax, it does not support specification of semantics, information which could guide the generation of more meaningful programs

EVO-CBG - an evolutionary system for automatically generating character behaviours for game environments

In this paper we discuss the need to extend the standard types of character behaviours found in game environments if we are to create new and more compelling gaming experiences. We propose using techniques from Evolutionary Algorithms and research from Game Design to create a system that can help game designers extend standard types of behaviours. This system automatically produces behaviours that are designed to optimize predefined parameters in the game environment. We outline experiments conducted using an implementation of this system to produce behaviours for the game Ms. Pac-Man, along with providing an overview of the results obtained. Finally we discuss these results and the potential they show for our system to help game designers not only create character behaviours, but also diagnose the effect that adding or removing certain mechanics will have on the overall gaming experience

The impact of task difficulty and performance scores on student engagement and progression

Background: This article considers the impact of differential task difficulty on student engagement and progression within an Irish primary school context. Gaining and maintaining student engagement during learning tasks such as homework is a significant and understandable on-going challenge for teachers. The findings of this study hold the potential to support teachers' decision-making processes regarding the development of student tasks. Purpose: The research study aimed to explore the impact of task difficulty on student engagement and subsequent progression in the computerised navigation task Pac-Man. The central research questions addressed in this article were; do subtle variances in task difficulty impact on student volition and consequently, will this result in a significant variance in students' levels of improvement? Sample: Sixty students from a large urban, coeducational primary school in the south of Ireland were identified as a suitable sample cohort. All students were in their final year of primary school within the Irish education system and were between 11 and 12 years of age. Design and methods: The study employed the use of the popular arcade game Pac-Man. In a test-retest approach, 60 primary school students completed the standard computerised navigation task with a seven-day interval. Between testing, participants were randomly subdivided into three cohorts. Each cohort of 20 participants received a different version of the Pac-Man game to practise with for one week. Cohort A received a version of the Pac-Man game of lesser difficulty, Cohort B received the standard Pac-Man game and Cohort C received a version of greater difficulty. A paired-samples t-test (repeated measures) was employed to compare the scores achieved by each of the three cohorts both pre- and post-practice. As an indication of the resulting effect size for each cohort the eta-squared statistic was subsequently calculated. In order to support any future meta-analysis, Cohen's d statistic is also provided in this paper. Analysis of variance (ANOVA) was employed to explore differences between groups with regard to progression scores and number of games played when practising. Results: The results of this small scale study found the cohort who received the easier version of the task presented the greatest overall improvement in performance between the pre- and post-tests. No statistically significant difference was found in the change in scores of the three cohorts - potentially due to the small sample size. However, paying attention to the size of the effect indicated that, over seven days, there was an 80% improvement in performance for Cohort A, 63% improvement for Cohort B and 26% improvement for Cohort C. The results highlight the negative impact of increased task difficulty on students' volition and consequently, on overall progress in the task. Conclusions: Further research with larger student populations would be needed to assess the generalisability of the results. However, the findings suggest that when designing tasks to promote student learning, particularly self-directed tasks such as homework, it is important that teachers afford ample opportunity for student success