The reinforcement learning paradigm has the aim to determine the optimal actions for a robot. A typical example is a maze finding robot, but reinforcement learning can also be used for training a robot to play the pong game. The principle is based on a reward function. If the robot is able to solve a problem, he gets a score from the underlying game engine. The score can be positive, if the robot reaches the end of a maze, or it can be negative, if he is colliding with an obstacle. The principle itself is working quite well, that means for simpler applications it is possible to train a robot to play a game with reinforcement learning.
Chatbots are a different category of artificial intelligence. They are working not with actions but with natural language. Person #1 is opening a dialogue with “Hi, I'm Alice”, while person #2 is responding with “Nice to meet you”. What is missing here is an underlying game which is played. There is no reward available for printing out a certain sentence. In some literature the problem of language grounding was discussed seriously, but with an unclear result. It seems, that a classical game for example pong, and a chatbot conversation doesn't have much in common.
Is it possible to combine Reinforcement Learning with chatbot design? The problem is, that a speech-act should be connected to a reward. That means, a well formulated sentence gets +10 points but a weak sentence gets -10 points. How can this be evaluated?