I studied the articles on Neural Networks and Deep Learning from Michael Nielsen and developed a simple neural network based on his examples. I understand how backpropagation works and I already taught my neural network to not only play TicTacToe but also improve his own play by learning from his own successes using backpropagation.
Going forward with my experiments, I am facing the problem, that I won't always be able to show the network good moves to use for learning (maybe because I simply don't know what is correct in a certain situation), but I might be required to show it bad moves to avoid (because some of the bad moves are obvious). Teaching the network what to do using backpropagation is easy, but I haven't found a way to teach it what to avoid using similar techniques.
Is it possible to teach simple neural networks using negative examples like this or do I need other techniques? My gut feeling says, that it might be possible to "invert" gradient descent into gradient ascent to solve this problem. Or is it more complicated than this?