A Selective Macro-learning Algorithm and its Application to the NxN Sliding-Tile Puzzle

One of the most common mechanisms used for speeding up problem solvers is macro-learning. Macros are sequences of basic operators acquired during problem solving. Macros are used by the problem solver as if they were basic operators. The major problem that macro-learning presents is the vast number of macros that are available for acquisition. Macros increase the branching factor of the search space and can severely degrade problem-solving efficiency. To make macro learning useful, a program must be selective in acquiring and utilizing macros. This paper describes a general method for selective acquisition of macros. Solvable training problems are generated in increasing order of difficulty. The only macros acquired are those that take the problem solver out of a local minimum to a better state. The utility of the method is demonstrated in several domains, including the domain of NxN sliding-tile puzzles. After learning on small puzzles, the system is able to efficiently solve puzzles of any size.Comment: See http://www.jair.org/ for an online appendix and other files accompanying this articl

Use-driven concept formation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 161-165).When faced with a complex task, humans often identify domain-specific concepts that make the task more tractable. In this thesis, I investigate the formation of domain-specific concepts of this sort. I propose a set of principles for formulating domain-specific concepts, including a new inductive bias that I call the equivalence class principle. I then use the domain of two-player, perfect-information games to test and refine those principles. I show how the principles can be applied in a semiautomated fashion to identify strategically-important visual concepts, discover highlevel structure in a game's state space, create human-interpretable descriptions of tactics, and uncover both offensive and defensive strategies within five deterministic, perfect-information games that have up to forty-two million states apiece. I introduce a visualization technique for networks that discovers a new strategy for exploiting an opponent's mistakes in lose tic-tac-toe; discovers the optimal defensive strategies in five and six men's morris; discovers the optimal offensive strategies in pong hau k'i, tic-tac-toe, and lose tic-tac-toe; simplifies state spaces by up to two orders of magnitude; and creates a hierarchical depiction of a game's state space that allows the user to explore the space at multiple levels of granularity. I also introduce the equivalence class principle, an inductive bias that identifies concepts by building connections between two representations in the same domain. I demonstrate how this principle can be used to rediscover visual concepts that would help a person learn to play a game, propose a procedure for using such concepts to create succinct, human-interpretable descriptions of offensive and defensive tactics, and show that these tactics can compress important information in the five men's morris state space by two orders of magnitude.by Jennifer M. Roberts.Ph.D