Human perceptions of artificial surfaces for field hockey

Measuring the performance of a sports surface is typically derived from a series of field and laboratory tests that assess the playing properties under simulated game conditions. However, from a player’s perspective their own comfort and confidence in the surface and its playing characteristics are equally if not more important. To date no comparative study to measure playing preference tests has been made. The aim of this research was to develop a suitable method for eliciting player perceptions of field hockey pitches and determine the key themes that players consider when assessing field hockey pitches. To elicit meaningful unbiased human perceptions of a playing surface, an individual subjective analysis was carried out, using interviews and inductive analysis of the recorded player statements. A qualitative analysis of elite hockey players (n = 22) was performed to obtain their perceptions immediately after a competitive match. The significant surface characteristics that emerged as part of an inductive analysis of their responses were grouped together and formed five general themes or dimensions: player performance, playing environment, pitch properties, ball interaction and player interaction. Each dimension was formed from a hierarchy of sub-themes. During the analysis, relationships between the dimensions were identified and a structured relationship model was produced to highlight each relationship. Players’ responses suggested that they perceived differences between pitches and that the majority of players considered a ‘hard’ pitch with a ‘low’ ball bounce facilitating a ‘fast’ game speed was desirable. However, further research is required to understand the relative importance of each theme and to develop appropriate measurement strategies to quantify the relevant engineering properties of pitch materials

Using First Order Inductive Learning as an Alternative to a Simulator in a Game Artificial Intelligence

Currently many game artificial intelligences attempt to determine their next moves by using a simulator to predict the effect of actions in the world. However, writing such a simulator is time-consuming, and the simulator must be changed substantially whenever a detail in the game design is modified. As such, this research project set out to determine if a version of the first order inductive learning algorithm could be used to learn rules that could then be used in place of a simulator. By eliminating the need to write a simulator for each game by hand, the entire Darmok 2 project could more easily adapt to additional real-time strategy games. Over time, Darmok 2 would also be able to provide better competition for human players by training the artificial intelligences to play against the style of a specific player. Most importantly, Darmok 2 might also be able to create a general solution for creating game artificial intelligences, which could save game development companies a substantial amount of money, time, and effort.Ram, Ashwin - Faculty Mentor ; Ontañón, Santi - Committee Member/Second Reade

Playing simple loony dots and boxes endgames optimally

We explain a highly efficient algorithm for playing the simplest type of dots and boxes endgame optimally (by which we mean "in such a way so as to maximise the number of boxes that you take"). The algorithm is sufficiently simple that it can be learnt and used in over-the-board games by humans. The types of endgames we solve come up commonly in practice in well-played games on a 5x5 board and were in fact developed by the authors in order to improve their over-the-board play.Comment: 20 pages; minor revisions made after referee's report. To be published in "Integers

Playing Muller Games in a Hurry

This work studies the following question: can plays in a Muller game be stopped after a finite number of moves and a winner be declared. A criterion to do this is sound if Player 0 wins an infinite-duration Muller game if and only if she wins the finite-duration version. A sound criterion is presented that stops a play after at most 3^n moves, where n is the size of the arena. This improves the bound (n!+1)^n obtained by McNaughton and the bound n!+1 derived from a reduction to parity games