Transfer Learning in Spatial Reasoning Puzzles

Transfer learning is the process of using knowledge gained while solving one problem to solve a new, previously unencountered problem. Current research has concentrated on analogical transfer - a mechanic is able to fix a type of car he has never seen before by comparing it to cars he has fixed before. This approach is typical of case-based reasoning systems and has been successful on a wide variety of problems [Watson, 1997]. When a new problem is encountered, a database of previously solved problems is searched for a problem with similar features. The solution to the most similar problem is selected, adapted and then applied to the new problem. Similar methods exist for adapting reinforcement learning policies [Taylor and Stone, 2009]. We refer to the above approaches as solution adaptation algorithms - a pair of problems are matched on similarity and the solution to the first problem, after some adaptation, is applied to the second problem. The solution adaptation approach requires three things.