Learning Minesweeper with Multirelational Learning

Multirelational Active Learning for Games

The relational concepts and the need for pattern recognition required to formulate playing notions in games such as Go and Minesweeper make them an ideal testbed for multirelational learning. However, with the exception of chess, games have received little attention from the research community in multirelational learning. Mio, a covering example-driven multirelational learning system, has been ...

Minesweeper, #minesweeper

We address the problem of finding an optimal policy for an agent playing the game of minesweeper. The problem is solved exactly, within the reinforcement learning framework, using a modified value iteration. Although it is a Partially Observable Markov Decision Problem (MDP), we show that, when using an appropriate state space, it can be defined and solved as a fully observable MDP. As has been...

Search Improvements in Multirelational Learning

In this thesis we lay the foundations to develop multirelational learning systems which can cope better with the challenges posed by structural and topological domains. Even though many interesting application domains contain structural or topological data, current multirelational systems have difficulties dealing with the complexity of the search space of theses domains, their indeterminacy, a...

MuSweeper: An Extensive Game for Collecting Mutual Exclusions

Mutual exclusions provide useful information for learning classes of concepts. We designed MuSweeper as a MineSweeper-like game to collect mutual exclusions from web users. Using the mechanism of an extensive game with Imperfect information, our experiments showed MuSweeper to collect mutual exclusions with high precision and efficiency.

On the Stability of Example-Driven Learning Systems: A Case Study in Multirelational Learning