Based on my prior knowledge, I can analyze the relationship between the feature "maint" and the target variable "decision to buy this car". Since "maint" represents the price of maintenance, it can be assumed that the higher the maintenance price, the more likely the decision to buy the car would be rated lower. Thus, we can expect a relationship between higher maintenance prices and lower ratings.

Based on this analysis, I can generate a dictionary with the specific details as follows:

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

In this case, the feature values are hard to predict for the "good" and "very good" target classes. Therefore, only the feature value "very high" is included for the "unacceptable" target class, and "high" for the "acceptable" target class. The feature values "low" and "medium" are included for the "very good" and "good" target classes, respectively.

Please note that this analysis is based on assumptions and prior knowledge. The actual relationship between the feature and the target might vary depending on the specific dataset and context.