4,003 research outputs found
The 2014 International Planning Competition: Progress and Trends
We review the 2014 International Planning Competition (IPC-2014), the eighth
in a series of competitions starting in 1998. IPC-2014 was held in three separate
parts to assess state-of-the-art in three prominent areas of planning research: the
deterministic (classical) part (IPCD), the learning part (IPCL), and the probabilistic
part (IPPC). Each part evaluated planning systems in ways that pushed the edge of
existing planner performance by introducing new challenges, novel tasks, or both.
The competition surpassed again the number of competitors than its predecessor,
highlighting the competition’s central role in shaping the landscape of ongoing
developments in evaluating planning systems
Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms
We investigate the impact of supervised prediction models on the strength and
efficiency of artificial agents that use the Monte-Carlo Tree Search (MCTS)
algorithm to play a popular video game Hearthstone: Heroes of Warcraft. We
overview our custom implementation of the MCTS that is well-suited for games
with partially hidden information and random effects. We also describe
experiments which we designed to quantify the performance of our Hearthstone
agent's decision making. We show that even simple neural networks can be
trained and successfully used for the evaluation of game states. Moreover, we
demonstrate that by providing a guidance to the game state search heuristic, it
is possible to substantially improve the win rate, and at the same time reduce
the required computations.Comment: Proceedings of the 2018 IEEE Conference on Computational Intelligence
and Games (CIG'18); pages 445-452; ISBN: 978-1-5386-4358-
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