Computer agents are increasingly deployed in settings in which they make decisions with people, such as electronic commerce, collaborative interfaces, and cognitive assistants. However, the scientific evaluation of computational strategies for human-computer decision-making is a costly process, involving time, effort and personnel. This paper investigates the use of Peer Designed Agents (PDA) — computer agents developed by human subjects — as a tool for facilitating the evaluation process of automatic negotiators that were developed by researchers. It compared the performance between automatic negotiators that interacted with PDAs to automatic negotiators that interacted with actual people in different domains. The experiments included more than 300 human subjects and 50 PDAs developed by students. Results showed that the automatic negotiators outperformed PDAs in the same situations in which they outperformed people, and that on average, they exhibited the same measure of generosity towards their negotiation partners. These patterns were significant for all types of domains, and for all types of automated negotiators, despite the fact that there were individual differences between the behavior of PDAs and people. The study thus provides an empirical proof that PDAs can alleviate the evaluation process of automatic negotiators, and facilitate their design.

Animating in-game avatars using real time motion capture data is highly appealing and is becoming more widespread due to the accessibility of researchers and developers to consumer priced depth sensors [Microsoft Kinect 2010]. Depth sensors allow for a cheap and robust motion capture solution which can be naturally adapted to games. However, in spite of the many advantages of using real-time motion capture data for animating avatars in games, there are two major challenges. The first is due to the limitations of the current tracking techniques in producing smooth, noise free, accurate animation in real-time. The second, and more acute problem, stems from the fact that in most games, the movements of the animated avatar are expected to be more expressive than the player's actual movements. In such cases, one would like to visually enhance the player's motion to display exaggerated or even super-natural motions.