Based on prior knowledge, we can analyze the relationship between the feature "lug_boot" (size of luggage boot) and the task of rating the decision to buy the car.

There are three possible categories for the "lug_boot" feature: "small", "med", and "big". We will analyze how each category relates to the rating task.

To generate the dictionary, we will go through the dataset and group the "lug_boot" values for each rating category (unacceptable, acceptable, good, and very good).

Here is the dictionary based on the analysis:

```json
{
	"unacceptable": ["small", "med", "big"],
	"acceptable": ["small", "med", "big"],
	"very good": ["big"],
	"good": ["med", "big"]
}
```

Explanation:
- For the "unacceptable" rating, all three categories of "lug_boot" (small, med, big) are represented.
- For the "acceptable" rating, all three categories of "lug_boot" are represented as well.
- For the "very good" rating, only the "big" category of "lug_boot" is represented.
- For the "good" rating, both "med" and "big" categories of "lug_boot" are represented.

Note that we only include the categories that are represented in each rating class. If there are other, hard-to-predict values for "lug_boot", they are not necessary to include in the dictionary.