Designing the Spectator Experience

Interaction is increasingly a public affair, taking place in our theatres, galleries, museums, exhibitions and on the city streets. This raises a new design challenge for HCI, questioning how a performer s interaction with a computer experienced is by spectators. We examine examples from art, performance and exhibition design, comparing them according to the extent to which they hide, partially reveal, transform, reveal or even amplify a performerts manipulations. We also examine the effects of these manipulations including movements, gestures and utterances that take place around direct input and output. This comparison reveals four broad design strategies: `secretive,' where manipulations and effects are largely hidden; `expressive,' where they are revealed, enabling the spectator to fully appreciate the performer's interaction; `magical,' where effects are revealed but the manipulations that caused them are hidden; and finally `suspenseful,' where manipulations are apparent, but effects only get revealed when the spectator takes their turn.

Hybrid Graph Heuristics within a Hyper-heuristic Approach to Exam Timetabling Problems

This paper is concerned with the hybridization of two graph coloring heuristics (Saturation Degree and Largest Degree), and their application within a hyperheuristic for exam timetabling problems. Hyper-heuristics can be seen as algorithms which intelligently select appropriate algorithms/heuristics for solving a problem. We developed a Tabu Search based hyper-heuristic to search for heuristic lists (of graph heuristics) for solving problems and investigated the heuristic lists found by employing knowledge discovery techniques. Two hybrid approaches (involving Saturation Degree and Largest Degree) including one which employs Case Based Reasoning are presented and discussed. Both the Tabu Search based hyper-heuristic and the hybrid approaches are tested on random and real-world exam timetabling problems. Experimental results are comparable with the best state-of-the-art approaches (as measured against established benchmark problems). The results also demonstrate an increased level of generality in our approach.