There has been a long-standing debate in the field of user-interface research regarding which area to focus on: building entirely automated interface agents or building tools that enhance direct manipulation by the users. This paper reviews key challenges and opportunities for building mixed-initiative user interfaces that enable users and intelligent agents to collaborate efficiently by combining the above-mentioned areas into one hybrid system that would help leverage the advantages of both the areas. The expected costs and benefits of the agent’s action are studied and compared to the costs and benefits of the agent’s inaction. A large portion of the paper deals with managing uncertainties that agents may have regarding the needs of the user. Three categories of actions are adopted according to the inferred probability the agent calculates regarding the user’s intent. No action is taken if the agent deems it correct, the agent engages the user in a dialog to understand the user’s intent if the intent is unclear or the agent goes ahead and provides the action if the inferred probability of the user wanting the action is high. The LookOut system for scheduling and meeting management, which is an automation service integrated with Microsoft Outlook, has been used as an example to explain this system.
The paper describes multiple interaction modalities and their attributes which I found interesting. These range from manual operation mode to basic automated-assistance mode to social-agent model. An alerting feature in the manual operation mode made the user aware that the agent would have taken some action at that point if it was in an automatic mode. I particularly found this interesting since this gives the users a sense of how the system reacts. If that happened to be helpful to the user, their trust level would automatically increase and as a result, the likelihood of them using the system the next time increases. I also liked the option of engaging in a dialog with the user as an option for action. During times of uncertainty, rather than guessing the intent of the user and risk damaging the trust of the user on the system, I feel that it is better to clarify the user’s intent by asking the user. It was also good to know that the automated systems analyze the user’s behavior and learn from it. The system takes into account past interactions and modifies its parameters to make a more informed decision the next time around.
While the agent learns from the relationship established between the length of the email and the time taken by the user to respond to it, I feel that a key factor missing is the current attention span of the user. While this is hard to gauge and learn from, I feel that it plays an equally important role in determining the time taken to respond to an email thereby affecting the agent’s judgment.
- Does the system account for scenarios involving the absence of the user immediately after the opening of an email? Would the lack of response have a negative impact on the feedback loop of the system?
- Apart from the current program context, the user’s sequence of actions and choice of words used in queries, what are some additional attributes that can be considered by the agent while inferring the goal of the user?
- I found the concept of context-dependent changes to utilities extremely interesting. Have there been studies conducted that involve a comprehensive set of scenarios that can affect the utility of a system based on context?
