229 research outputs found
Traditional Wisdom and Monte Carlo Tree Search Face-to-Face in the Card Game Scopone
We present the design of a competitive artificial intelligence for Scopone, a
popular Italian card game. We compare rule-based players using the most
established strategies (one for beginners and two for advanced players) against
players using Monte Carlo Tree Search (MCTS) and Information Set Monte Carlo
Tree Search (ISMCTS) with different reward functions and simulation strategies.
MCTS requires complete information about the game state and thus implements a
cheating player while ISMCTS can deal with incomplete information and thus
implements a fair player. Our results show that, as expected, the cheating MCTS
outperforms all the other strategies; ISMCTS is stronger than all the
rule-based players implementing well-known and most advanced strategies and it
also turns out to be a challenging opponent for human players.Comment: Preprint. Accepted for publication in the IEEE Transaction on Game
Biasing MCTS with Features for General Games
This paper proposes using a linear function approximator, rather than a deep
neural network (DNN), to bias a Monte Carlo tree search (MCTS) player for
general games. This is unlikely to match the potential raw playing strength of
DNNs, but has advantages in terms of generality, interpretability and resources
(time and hardware) required for training. Features describing local patterns
are used as inputs. The features are formulated in such a way that they are
easily interpretable and applicable to a wide range of general games, and might
encode simple local strategies. We gradually create new features during the
same self-play training process used to learn feature weights. We evaluate the
playing strength of an MCTS player biased by learnt features against a standard
upper confidence bounds for trees (UCT) player in multiple different board
games, and demonstrate significantly improved playing strength in the majority
of them after a small number of self-play training games.Comment: Accepted at IEEE CEC 2019, Special Session on Games. Copyright of
final version held by IEE
Enhancing the Monte Carlo Tree Search Algorithm for Video Game Testing
In this paper, we study the effects of several Monte Carlo Tree Search (MCTS)
modifications for video game testing. Although MCTS modifications are highly
studied in game playing, their impacts on finding bugs are blank. We focused on
bug finding in our previous study where we introduced synthetic and human-like
test goals and we used these test goals in Sarsa and MCTS agents to find bugs.
In this study, we extend the MCTS agent with several modifications for game
testing purposes. Furthermore, we present a novel tree reuse strategy. We
experiment with these modifications by testing them on three testbed games,
four levels each, that contain 45 bugs in total. We use the General Video Game
Artificial Intelligence (GVG-AI) framework to create the testbed games and
collect 427 human tester trajectories using the GVG-AI framework. We analyze
the proposed modifications in three parts: we evaluate their effects on bug
finding performances of agents, we measure their success under two different
computational budgets, and we assess their effects on human-likeness of the
human-like agent. Our results show that MCTS modifications improve the bug
finding performance of the agents
Automated Video Game Testing Using Synthetic and Human-Like Agents
In this paper, we present a new methodology that employs tester agents to
automate video game testing. We introduce two types of agents -synthetic and
human-like- and two distinct approaches to create them. Our agents are derived
from Reinforcement Learning (RL) and Monte Carlo Tree Search (MCTS) agents, but
focus on finding defects. The synthetic agent uses test goals generated from
game scenarios, and these goals are further modified to examine the effects of
unintended game transitions. The human-like agent uses test goals extracted by
our proposed multiple greedy-policy inverse reinforcement learning (MGP-IRL)
algorithm from tester trajectories. MGPIRL captures multiple policies executed
by human testers. These testers' aims are finding defects while interacting
with the game to break it, which is considerably different from game playing.
We present interaction states to model such interactions. We use our agents to
produce test sequences, run the game with these sequences, and check the game
for each run with an automated test oracle. We analyze the proposed method in
two parts: we compare the success of human-like and synthetic agents in bug
finding, and we evaluate the similarity between humanlike agents and human
testers. We collected 427 trajectories from human testers using the General
Video Game Artificial Intelligence (GVG-AI) framework and created three games
with 12 levels that contain 45 bugs. Our experiments reveal that human-like and
synthetic agents compete with human testers' bug finding performances.
Moreover, we show that MGP-IRL increases the human-likeness of agents while
improving the bug finding performance
Knowledge-based fast evolutionary MCTS for general video game playing
General Video Game Playing is a game AI domain in which the usage of game-dependent domain knowledge is very limited or even non existent. This imposes obvious difficulties when seeking to create agents able to play sets of different games. Taken more broadly, this issue can be used as an introduction to the field of General Artificial Intelligence. This paper explores the performance of a vanilla Monte Carlo Tree Search algorithm, and analyzes the main difficulties encountered when tackling this kind of scenarios. Modifications are proposed to overcome these issues, strengthening the algorithm's ability to gather and discover knowledge, and taking advantage of past experiences. Results show that the performance of the algorithm is significantly improved, although there remain unresolved problems that require further research. The framework employed in this research is publicly available and will be used in the General Video Game Playing competition at the IEEE Conference on Computational Intelligence and Games in 2014
- …