I. More arch learning In this lecture we finished the classic example of learning about an arch by the presentation of a series of pre-classified (i.e., arch or not arch) examples. It's important that you not learn the wrong thing from this: in the example you've seen, negative examples involved differences in links and positive examples involved differences in nodes. That's not necessarily always going to be so, so banish that thought from your mind. This kind of learning is an example of inductive inference, which we've talked about in the past. Inductive inference, which could also be called learning by induction, is about generalizing some concept from a set of examples. As we've noted in the past, and as your textbook reinforces, it's the kind of learning that keeps us alive, both as individuals and as a species. It may result in us being overly cautious, but in a hostile world that's probably better than being overly fearless. In any case, inductive learning highlights something that humans do amazingly well and that computers can't do at all without lots of help. We, humans, can figure out from a series of examples what's salient...that is, we somehow can figure out which features in a set of examples are important and which can be ignored. We do it well, we do it quickly, and we don't really have a good idea as to how we do it. II. Learning as the search for the best representation Way back at the beginning of the term, we talked about an intelligent agent as being a Reasoning and Representation System (RRS). With formal logic as our constant, unchangeable reasoner, anything that our learning program is going to do while learning must be represented as changes to the representation. That is, a learning program starts with some initial representation of what it knows, and as it learns it modifies that representation to reflect new knowledge. Thus, learning is about searching for a better representation than what the program started with...a representation that includes what's been learned. III. Learning with a different representation For any given learning task, as with any AI task, there may be many ways of representing both old and new knowledge. An arch learner isn't constrained to just semantic nets for representing what it knows about arches. An arch learner could, for example, represent that same knowledge as a decision tree. IV. Other issues in learning Different types of learning give rise to different looks at the same issues. Above, we talked about the issue of salience...how does a learner know what's important. In a task where a learner wants to learn a sequence of operations to perform some task, or perhaps a sequence of moves or decisions to win at some game, the salience question is recast as one of credit assignment or blame assignment. If a sequence of moves leads to success, which of those moves should be given the most credit for that success? If a sequence of moves leads to failure, which moves should be given the most blame for that failure? These are issues that come up again and again in the learning world.
Last revised: December 7, 2004