research meeting. It is also necessary to estimate the relative quality of the user, EQfi(t),
and Friday's decision making, EQ a ( t ). We assume that the user's decision-making EQ f, ( t )
is high with respect to Friday's, EQ k ( t ). The uncertainty about user intentions makes it very hard for Friday to consistently make correct decisions about the time at which the user will arrive at meetings, although its sensors (e.g., GPS device) give some indication of the user's location.  When dealing with more important meetings, the cost of Friday's errors is higher. Thus, in some cases, the decision-making quality of the user and Friday will be similar, i.e., EQH(t) ~ EQA (t); while in other cases, there will be an order of magnitude difference, i.e., EQ H (t) ~ 10* EQ A (t). The wait cost function, W (t), will be much larger for big meetings than small and increase rapidly as other attendees wait longer in the meeting room. Finally, the cost of delays, i.e., Dcost, can vary by about an order of magnitude. In particular, the cost of rescheduling meetings varies greatly, e.g., the cost of rescheduling small informal meetings with colleagues is far less than rescheduling a full lecture room at 5 PM Friday.

The parameters laid out above show how parameters vary from decision to decision. For a specific decision, we use Markovian response probabilities (e.g., when the user is in her office, the average response time is five minutes), exponentially increasing wait costs, and constant decision-making quality (though it changes from decision to decision) to calculate the EU of interesting strategies. Calculating the EU of different strategies using the values for different parameters shown above allows us to draw the following conclusions (Table 5 in Section 5.3 presents a quantitative illustration of these predictions):
- The strategy e should not be used, since for all combinations of user location and

meeting importance the EU of this strategy is very low.

- Multiple strategies are required, since for different user locations and meeting importance different strategies are optimal.

- Since quite different strategies are required when the user is in different locations, the AA reasoning will need to change strategies when the user changes location.

- No strategy has a reasonable EU for all possible parameter instantiations, hence always using the same strategy will occasionally cause dramatic failures.

- For most decisions, strategies will end with the agent taking a decision, since strategies ending with the user in control generally have very low EU.
These predictions provide important guidance about a successful solution for AA in the E-Elves. In particular, they make clear that the approach must flexibly choose between different strategies and adjust depending on the meeting type and user location.

Section 2.2 described the unsuccessful C4.5 approach to AA in E-Elves and identified several reasons for the mistakes that occurred. In particular, rigidly transferring control to one entity and ignoring potential team costs involved in an agent's decision were highlighted as reasons for the dramatic mistakes in Friday's autonomy reasoning. Reviewing the C4.5 approach in the light of the notion of strategies, we see that Friday learned one strategy and stuck with that strategy. In particular, originally, Friday would wait indefinitely for a user response, i.e., it would follow strategy e , if it had learned to transfer control. As shown later