The system was set to use a "decaying" average to compute the stereotype. That is, the stereotype was a 90%/10% mix of the previous and revised value, with the revised value obtained by starting with the previous value and performing belief revision on it using the current dialogue. This ensured that the most recent evidence had a greater weighting. Appropriate preconditions were set up for the plan rules to ensure that the system could make all of the necessary inferences about beliefs and intentions. These rules are given in table 4.1
The "dry-land" algorithm is particularly helpful in this example, for the dialogue [discuss-details,chat,chat] (see figure 4.15). The final chat has no precondition and so ordinary belief revision does nothing. However, it can be explained by both of the user not intending a window seat and the user not believing that one is available. The dry-land algorithm adopts the simplest combination of these. It is also useful at the offer/chat decision. For example, if the system were to choose chat, the system would expect the user to update his model with an explanation that the system believes that either the user does not want a window seat or that the user believes the system has a window seat and will therefore take the initiative himself. That the dry-land algorithm is useful twice in this example indicates that it may be significantly important in other types of dialogue, but these are yet to be investigated.
Some demonstrations are presented now to show how the stereotype model adapts over the course of a sequence of dialogues. Demonstration 1 looks at the updating of the system's model of the user's model, in response to the system's act on the first turn. Demonstration 2 looks at the system's revision of its own model in response to the user's act on the second turn. Demonstration 3 looks at the sequence of belief states that result from a sequence of dialogues.