Zoekresultaten

Spate

Developing students’ understanding of statistical graphs

Poster presented at the 14th Congress of the European Society for Research in Mathematics Education, Free University of Bozen-Bolsano, Italy.

When the parts of the sum are greater than the whole : assessing the peak-and-end-theory for a heterogeneous, multi-episodic tourism experience

Emotions are a key component of tourism experiences, as emotions make experiences more valued and more memorable. Peak-and-end-theory states that overall experience evaluations are best predicted by the emotions at the most intense and final moments of an experience. Peak-and-end-theory has mostly been studied for relatively simple experiences. Recent insights suggest that peak-and-end-theory does not necessarily hold for tourism experiences, which tend to be more heterogeneous and multi-episodic in nature. Through the novel approach of using electrophysiological measures in combination with experience reconstruction, the applicability of the peak-and-end-theory to the field of tourism is addressed by studying a musical theatre show in a theme park resort. Findings indicate that for a multi-episodic tourism experience, hypotheses from the peak-and-end-theory are rejected for the experience as a whole, but supported for individual episodes within the experience. Furthermore, it is shown that electrophysiology sheds a new light on the temporal dynamics of experience

Engineering emergence

Dit proefschrift presenteert twee theoretische kaders voor het ontwerpen van games en beschrijft hoe game designers deze kunnen inzetten om het game ontwerpproces te stroomlijnen. Er bestaan op dit moment meerdere ontwerptheorie¨en voor games, maar geen enkele kan rekenen op een breed draagvlak binnen de game industrie. Vooral academische ontwerptheorie¨en hebben regelmatig een slechte reputatie. Het eerste kader dat game designers inzicht biedt in spelregels en hun werking heet Machinations en maakt gebruik van dynamische, interactieve diagrammen. Het tweede theoretische kader van dit proefschrift, Mission/Space, richt zich op level-ontwerp en spelmechanismen die de voortgang van een speler bepalen. In tegenstelling tot bestaande modellen voor level-ontwerp, bouwt Mission/Space voort op het idee dat er in een level twee verschillende structuren bestaan. Mission-diagrammen worden gebruikt om de structuur van taken en uitdagingen voor de speler te formaliseren, terwijl space-diagrammen de ruimtelijke constructie formaliseren. Beide constructies zijn aan elkaar gerelateerd, maar zijn niet hetzelfde. De verschillende wijzen waarop missies geprojecteerd kunnen worden op een bepaalde ruimte speelt uiteindelijk een belangrijke rol in de totstandkoming van de spelervaring.

Translations and boundaries in the gap between HCI Theory and design practice

The gap between research and design practice has long been a concern for the HCI community. In this article, we explore how different translations of HCI knowledge might bridge this gap. A literature review characterizes the gap as having two key dimensions - one between general theory and particular artefacts and a second between academic HCI research and professional UX design practice. We report on a 5-year engagement between HCI researchers and a major media company to explore how a particular piece of HCI research, the trajectories conceptual framework, might be translated for and with UX practitioners. We present various translations of this framework and fit them into the gap we previously identified. This leads us to refine the idea of translations, suggesting that they may be led by researchers, by practitioners or co-produced by both as boundary objects. We consider the benefits of each approach.

Heuristic Coordination in Cooperative Multi-Agent Reinforcement Learning

Key to reinforcement learning in multi-agent systems is the ability to exploit the fact that agents only directly influence only a small subset of the other agents. Such loose couplings are often modelled using a graphical model: a coordination graph. Finding an (approximately) optimal joint action for a given coordination graph is therefore a central subroutine in cooperative multi-agent reinforcement learning (MARL). Much research in MARL focuses on how to gradually update the parameters of the coordination graph, whilst leaving the solving of the coordination graph up to a known typically exact and generic subroutine. However, exact methods { e.g., Variable Elimination { do not scale well, and generic methods do not exploit the MARL setting of gradually updating a coordination graph and recomputing the joint action to select. In this paper, we examine what happens if we use a heuristic method, i.e., local search, to select joint actions in MARL, and whether we can use outcome of this local search from a previous time-step to speed up and improve local search. We show empirically that by using local search, we can scale up to many agents and complex coordination graphs, and that by reusing joint actions from the previous time-step to initialise local search, we can both improve the quality of the joint actions found and the speed with which these joint actions are found.