I need to classify objects with ~50 features into 3-4 different classes, there are no labeled examples. Moreover there is no absolutely correct class for any object. However I do have cost value for every particular classification, but explicit form of the cost function is not known. I need to create classification rules that minimize cost function.
To be more specific, I am trying to teach a robot to survive effectively in an environment. There are many different objects and relations in this environment. At every step robot classifies the environment as safe, dangerous, etc, and performs an action according to the classification. This action changes environment and affects robot's level of satisfaction. Robot lives using these rules of classification for some time (epoch ~= 100 steps). After this epoch (~100 steps) robot tries to change the classification rules to increase his average level of satisfaction (-cost function) in next epoch.
So my question is: what classification/clustering algorithms should I consider using to achieve the goal? Are there any articles to read on relative topics? Is there a way ANN or SVM can fit such cases without knowing gradient of the cost function? I'll appreciate any help.