7.2.6 Analysis of data from subject MT

The subject MT interacted with the simulation for a nominal 20 hours 53 minutes, including 31 starts, between 17th July and 10th August. The first non-negative score was 7922 after 11h 33m. Progressive maxima were 12337 after 15h 56m, 13971 after 18h 8m, 15147 after 20h 53m. Compared with AJ, MT achieved similar levels of score in a shorter practice time, but did not achieve as high a final score due to spending less time overall in practice.

The data from MT were divided into five intervals, with the boundaries corresponding with practice times of 0h 0m, 4h 8m, 7h 23m, 10h 48m, 16h 22m, and 20h 53m. Thus, the lengths of the five intervals were 4h 8m, 3h 15m, 3h 25m, 5h 34m, and 4h 31m. These are referred to here as intervals A to E respectively.

Initially, the same process was followed for MT as for AJ. Interval E served as the basis for defining 13 contexts, using again a chord `distance' of 2 for allowing the absorption of a chord in a larger group. For each context, rules for each training set were tested against each test set, as before.

Table 7.11: ROV visual context for MT

Table 7.12: ROV direction context for MT

Table 7.13: ROV non-graphic context for MT

The tables of results for the ROV contexts are 7.11, 7.12, and 7.13. For the ROV visual context, we see for interval A a low value for the improvement of performance over the default rule. This implies that at the outset, rules in terms of the attributes selected were not yet established. By the time we get to intervals D and E, however, the induced rules are performing well above the default rule, and we have a situation comparable to AJ's tables 7.4 and 7.5. The rules are not performing very well in absolute terms, which suggests that some feature of the actions in these contexts is not taken account of in the analysis.

As we would expect, given the pricing policy on the information, the use of the graphic information declines as time goes on. The context here called `ROV direction' is one of the contexts that is standing in for the ROV visual one at later times. The performance of the D and E rules in this table (7.12) somewhat suggest that the rules in this new context are in the process of development---there are better scores for the training and test sets drawn from the same interval than for training and test sets drawn from different intervals.

When we come to the ROV non-graphic context (Table 7.13), the results look rather erratic. One possibility is that in this context, there are actions that do not depend on the selected attributes, such as preformed sequences of actions. Another possibility is that this context itself is not a natural one, and that there could be a number of disparate contexts within it. We will return to this point shortly.

Table 7.14: Ship search context for MT

Table 7.15: Ship close context for MT

Table 7.16: Ship search with GPI context for MT

Table 7.17: Ship with GPI 2 context for MT

The results in the ship contexts are given in Tables 7.14, 7.15, 7.16, and 7.17. None of these display any convincing sign that there are rules in terms of the attributes selected. There is no context as ruly as AJ's ship positioning context (Table 7.8). What they do show, however, is shifts in patterns of sensor usage, and that this sensor usage differs substantially from that of AJ. In the absence of any signs to differentiate between them, we must say that it is not clear whether these contexts correspond to MT's task structure, or whether they are artefacts of the analysis.

Table 7.18: General cable context for MT

Table 7.19: Cable with GPI context for MT

The cable contexts (Tables 7.18 and 7.19) for MT show much the same picture as for AJ, but in the early intervals (not present in the AJ analysis) MT has the general position indicator on, and the rules induced are not as good during the initial learning, in intervals A and B. The cable operations are extremely simple, and it is not at all surprising that rules can be induced for these.

As well as the 9 contexts described here, there were 5 other contexts in which the number of examples was smaller. These contexts are more tentative than the others and the results based on them of less value.

In order to assess the variability of the figures obtained in the tables, the induction was run again with the 0 and 1 sets of data interchanged: i.e., rules were induced on the 1 data sets and tested on the 0 data sets. Because of the fluctuations between the 0 and 1 sets, in general both the overall accuracy figures and the default accuracy figures differed slightly between the two sets. For comparison, the three tables 7.20, 7.21, and 7.22 give alternative versions of Tables 7.11, 7.13, and 7.18 respectively.

Table 7.20: ROV visual context for MT

Table 7.21: ROV non-graphic context for MT

Table 7.22: General cable context for MT

These tables show a reassuring difference in detail and similarity in structure. Of the three shown here, the context which was least regular and predictable, ROV non-graphic (Tables 7.13 and 7.21), is also the one where the discrepancies between the two versions are greatest, whereas for the other two, where there appears to be more predictability, there is also less discrepancy between the versions. This adds to suspicions that the ROV non-graphic context corresponds less to MT's mental structure than some of the others.