Based on prior knowledge, we can analyze the relationship between the feature "maint" (price of the maintenance) and the target variable (decision to buy the car).

Since the target variable is the rating of the decision to buy the car, we need to look for patterns or trends in the feature values of "maint" that correspond to different ratings.

Let's analyze the possible values of the "maint" feature and their relationship with the target variable:

- Unacceptable: It is possible that higher maintenance costs (very high) are associated with an unacceptable rating, as buyers may consider the cost of maintenance too high for their liking.

- Acceptable: It is likely that lower maintenance costs (low or medium) are associated with an acceptable rating, as buyers may find the cost of maintenance within their budget or reasonable.

- Good: It is possible that a range of maintenance costs (medium or low) are associated with a good rating, as buyers may consider these costs as affordable and reasonable.

- Very Good: It is likely that low maintenance costs (low) are associated with a very good rating, as buyers may find the cost of maintenance exceptionally low and economical.

Based on this analysis, we can create the dictionary with the following information:

```json
{
	"unacceptable": ["very high"],
	"acceptable": ["high", "medium", "low"],
	"good": ["medium", "low"],
	"very good": ["low"]
}
```

Please note that the given values for the "maint" feature are categorical, and we have associated different target classes with the relevant values of the feature.