We describe the first results of our work towards a concept inventory for Algorithms and Data Structures. Based on expert interviews and the analysis of 400 exams we were able to identify several core topics which are prone to error. In a pilot study, we verified misconceptions known from the literature and identified previously unknown misconceptions related to Algorithms and Data Structures. In addition to this, we report on methodological issues and point out the importance of a two-pronged approach to data collection.

No abstract

Abstract-This paper investigates the intelligent moving and path-finding of groups in real-time strategy (RTS) games exemplified by the open source game Glest. We utilize the technique of Flocking for achieving a smooth and natural movement of a group of units and expect grouping to decrease the amount of unit losses in RTS games. Furthermore, we present a setting in which Flocking will improve the game progress. But we also demonstrate a situation where Flocking fails. To prevent these annoying situations, we combined Flocking with Influence Maps (IM) to find safe paths for the flock in real time. This combination turns out to be an excellent alternative to normal movement in Glest and most likely in other RTS games.

In systems optimization and machine learning multiple alternative solutions may exist in different parts of decision space for the same parts of the Pareto-front. The detection of equivalent Pareto-subsets may be desirable. In this paper we introduce a niching method that approximates Paretooptimal solutions with diversity mechanisms in objective and decision space. For diversity in objective space we use rake selection, a selection method based on the distances to reference lines in objective space. For diversity in decision space we introduce a niching approach that uses the density-based clustering method DBSCAN. The clustering process assigns the population to niches while the multi-objective optimization process concentrates on each niche independently. We introduce an indicator for the adaptive control of clustering processes, and extend rake selection by the concept of adaptive corner points. The niching method is experimentally validated on parameterized test function with the help of the S-metric.

Popular games often have a high-quality graphic design but quite simple-minded non player characters (NPC). Recently, Computational Intelligence (CI) methods have been discovered as suitable methods to revive NPC, making games more interesting, challenging, and funny. We present a fairly large study of human players on the simple arcade game PacMan, controlling the ghosts behaviors by simple strategies, neural networks or evolutionary algorithms. The player's fun is of course a subjective experience, but we presume that it is related to the psychological flow concept. We deal with the question whether flow is a more reliable measure than asking human players directly for the fun experienced during the game. In order to detect flow, we introduce a measure based on the interaction time fraction between the human-controlled PacMan and the ghosts, and compare the outcome to the results of a fun measure suggested by Yannakakis and Hallam [1].