SUMMARY
This paper, that was published in 1999, reviews principles that can be used when coupling automated services with direct manipulation. Multiple principles for mixed-initiative UI have been listed in this paper, such as developing significant value-added automation, inferring ideal action in the light of costs, benefits, and uncertainties, continuing to learn by observing, etc. The author focusses on the LookOut project – an automated scheduling service for Microsoft Outlook – which was an attempt to aid users in automatically adding appointments to their calendar based on the messages that were currently viewed by the user. He then discusses about the decision-making capabilities of this system under uncertainty – LookOut was designed to parse the header, subject, and body of a message and employ a probabilistic classification system in order to identify the intent of the user. The LookOut system also offered multiple interaction modalities which included direct manipulation, basic automated-assistance, social-agent modality. The author also discusses inferring beliefs from user goals as well as mapping these beliefs to actions. The author discusses the importance of timing these automated services such that they are not invoked before the user is ready for the service.
REFLECTION
I found it very interesting to read about these principles of mixed-initiative UI considering that they were published in 1999 – which, incidentally, was when I first learnt to use a computer! I found that the principles being considered were fairly wide-spread considering the year of publication. However, principles such as ‘considering uncertainty about use’s goals’ and ‘employing dialog to resolve key uncertainties’ could have perhaps been addressed by performing behavior modeling. I was happy to learn that the LookOut system had multiple interaction modalities that could be configured by the user and was surprised to learn that the system employed an automated speech recognition system that was able to understand human speech. It did, however, make me wonder about how this system performed with respect to different accents; even though the words under consideration were basic words such as ‘yes’, ‘yeah’, ‘sure’, I wondered about the performance of the system. I also thought that it was nice that the system was able to identify if a user seemed disinterested and that the system waited in order to obtain a response. I also felt that it was a good design strategy to implement a continued training mechanism and that users could dictate a training schedule for the same. However, if the user were to dictate a training schedule, I wonder if it would cause a difference in the user’s behavior versus if they were to act without knowing that their data would be monitored at that given point in time (consent would be needed, but perhaps randomly observing user behavior would ensure that the user is not made too conscious about their actions).
QUESTIONS
- Not having explored the AI systems of the 90s, I am unaware about the way these systems work. The paper mentions that the LookOut system was designed to continue to learn from users, how was this feedback loop implemented? Was the model re-trained periodically?
- Since data and the bias present in the data used to train a model is very important, how were the messages collected in this study obtained? The paper mentions that the version of LookOut being considered by the paper was trained using 500 relevant and 500 irrelevant messages – how was this data obtained and labeled?
- With respect to the monitoring of the length of time between the review of a message and the manual invocation of the messaging service, the authors studied the relationship based on the size of the message and the time users dwell on the same. What was the demographic of the people used as part of this study? Would there exist a difference in the time taken when considering native versus non-native English speakers?
