Caveat Venditor: Technologically Protected Subsidized Goods and the Customers Who Hack Them

This paper explores the issues surrounding the subsidization of a technology based durable good and on the delicate dance between the producer trying to protect their profit, competitors trying to to create and sell aftermarket goods, and innovative customers who attempt to use the goods in completely unplanned, and unprofitable ways. A number of case studies are presented that highlighted the ease with which customers can tinker with subsidized products. These include Microsoft's Xbox, Netpliances' i-Opener, the Sony AIBO, and the general problems of the prepaid phone industry. A number of legal cases are also presented, although these focus more on the issue of other firms attempting to make competing aftermarket products for subsidized primary goods. The difficult question of what can a company do is explored. The question of who the company can go after for infringement, be it the reverse engineering programmer, or the college student sharing such information on her homepage, is also examined. Finally, this paper examines the moral issues involved when consumers hack these items.

A hybrid approach to fast and accurate localization for legged robots

SUMMARYThis paper describes a hybrid approach to a fast and accurate localization method for legged robots based on Fuzzy-Markov (FM) and Extended Kalman Filters (EKF). Both FM and EKF techniques have been used in robot localization and exhibit different characteristics in terms of processing time, convergence, and accuracy. We propose a Fuzzy-Markov–Kalman (FM–EKF) localization method as a combined solution for a poor predictable platform such as Sony Aibo walking robots. The experimental results show the performance of EKF, FM, and FM-EKF in a localization task with simple movements, combined behaviors, and kidnapped situations. An overhead tracking system was adopted to provide a ground truth to verify the performance of the proposed method.

Applying the Appraisal Theory of Emotion to Human-Agent Interaction

When people interact with one another, there is a series of conscious and unconscious evaluations used to judge the situation in order to determine an emotional response. This research examines whether the emotional appraisals that individuals use when interacting with other humans, can be applied to human-agent interactions, and whether the attributes of the non-human agent affect the nature of these appraisals. Participants work with one of three non-human teammates to accomplish a series of tasks. These agents are a real dog, a robotic dog (Sony AIBO), and a nondescript robot (Lego NXT). Participants' emotional reaction is measured through subjective questionnaires, physiological data (EKG & galvanic skin response), and vocal analysis. Taken together this set of measurements forms a detailed picture of how humans react emotionally to agents during their task interaction. It is predicted that agent form will influence participants' appraisals and emotional reactions.