In several real-world applications, a learner has access to multiple environment simulators, each with a different precision (e.g., simulation accuracy) and cost (e.g., computational time). In such a scenario, the learner faces the trade-off between selecting expensive accurate simulators or preferring cheap imprecise ones. We formalize this setting as a multi-fidelity variant of the stochastic bestarm identification problem, where querying the original arm is expensive, but multiple and biased approximations (i.e., fidelities) are available at lower costs.

One not to remember video game Uno. One not to remember video game Uno. Are digital board games as fun as the real thing? When our family board game night got cancelled, I sampled digital spins on the classics instead. I'm not sure I should have bothered - with one exception The whole point of video games is to be faster, more visually arresting, and less reliant on other humans than old games played with dice and cards. But a recent family board game night was derailed by clashing schedules and family civil war, so I spent a Saturday night trying them out on the iPhone instead.

Can't Stop is a jeopardy stochastic game played on an octagonal game board with four six-sided dice. Optimal strategies have been computed for some simplified versions of Can't Stop by employing retrograde analysis and value iteration combined with Newton's method. These computations result in databases that map game positions to optimal moves. Solving the original game, however, is infeasible with current techniques and technology. This paper describes the creation of heuristic strategies for solitaire Can't Stop by generalizing an existing heuristic and using genetic algorithms to optimize the generalized parameters. The resulting heuristics are easy to use and outperform the original heuristic by 19%. Results of the genetic algorithm are compared to the known optimal results for smaller versions of Can't Stop, and data is presented showing the relative insensitivity of the particular genetic algorithm used to the balance between reduced noise and increased population diversity.