6,567 research outputs found
Shallow decision-making analysis in General Video Game Playing
The General Video Game AI competitions have been the testing ground for
several techniques for game playing, such as evolutionary computation
techniques, tree search algorithms, hyper heuristic based or knowledge based
algorithms. So far the metrics used to evaluate the performance of agents have
been win ratio, game score and length of games. In this paper we provide a
wider set of metrics and a comparison method for evaluating and comparing
agents. The metrics and the comparison method give shallow introspection into
the agent's decision making process and they can be applied to any agent
regardless of its algorithmic nature. In this work, the metrics and the
comparison method are used to measure the impact of the terms that compose a
tree policy of an MCTS based agent, comparing with several baseline agents. The
results clearly show how promising such general approach is and how it can be
useful to understand the behaviour of an AI agent, in particular, how the
comparison with baseline agents can help understanding the shape of the agent
decision landscape. The presented metrics and comparison method represent a
step toward to more descriptive ways of logging and analysing agent's
behaviours
Evolving UCT alternatives for general video game playing
We use genetic programming to evolve alternatives
to the UCB1 heuristic used in the standard UCB formulation
of Monte Carlo Tree Search. The fitness
function is the performance of MCTS based on the
evolved equation on playing particular games from
the General Video Game AI framework. Thus, the
evolutionary process aims to create MCTS variants
that perform well on particular games; such variants
could later be chosen among by a hyper-heuristic
game-playing agent. The evolved solutions could
also be analyzed to understand the games better. Our
results show that the heuristic used for node selection
matters greatly to performance, and the vast majority
of heuristics perform very badly; furthermore,
we can evolve heuristics that perform comparably
to UCB1 in several games. The evolved heuristics
differ greatly between games.peer-reviewe
Ensemble decision systems for general video game playing
Ensemble Decision Systems offer a unique form of decision making that allows
a collection of algorithms to reason together about a problem. Each individual
algorithm has its own inherent strengths and weaknesses, and often it is
difficult to overcome the weaknesses while retaining the strengths. Instead of
altering the properties of the algorithm, the Ensemble Decision System augments
the performance with other algorithms that have complementing strengths. This
work outlines different options for building an Ensemble Decision System as
well as providing analysis on its performance compared to the individual
components of the system with interesting results, showing an increase in the
generality of the algorithms without significantly impeding performance.Comment: 8 Pages, Accepted at COG201
General Game Heuristic Prediction Based on Ludeme Descriptions
This paper investigates the performance of different general-game-playing
heuristics for games in the Ludii general game system. Based on these results,
we train several regression learning models to predict the performance of these
heuristics based on each game's description file. We also provide a condensed
analysis of the games available in Ludii, and the different ludemes that define
them.Comment: 4 pages, 1 figure, 2 table
General Board Game Concepts
Many games often share common ideas or aspects between them, such as their
rules, controls, or playing area. However, in the context of General Game
Playing (GGP) for board games, this area remains under-explored. We propose to
formalise the notion of "game concept", inspired by terms generally used by
game players and designers. Through the Ludii General Game System, we describe
concepts for several levels of abstraction, such as the game itself, the moves
played, or the states reached. This new GGP feature associated with the ludeme
representation of games opens many new lines of research. The creation of a
hyper-agent selector, the transfer of AI learning between games, or explaining
AI techniques using game terms, can all be facilitated by the use of game
concepts. Other applications which can benefit from game concepts are also
discussed, such as the generation of plausible reconstructed rules for
incomplete ancient games, or the implementation of a board game recommender
system
Flow-based Intrinsic Curiosity Module
In this paper, we focus on a prediction-based novelty estimation strategy
upon the deep reinforcement learning (DRL) framework, and present a flow-based
intrinsic curiosity module (FICM) to exploit the prediction errors from optical
flow estimation as exploration bonuses. We propose the concept of leveraging
motion features captured between consecutive observations to evaluate the
novelty of observations in an environment. FICM encourages a DRL agent to
explore observations with unfamiliar motion features, and requires only two
consecutive frames to obtain sufficient information when estimating the
novelty. We evaluate our method and compare it with a number of existing
methods on multiple benchmark environments, including Atari games, Super Mario
Bros., and ViZDoom. We demonstrate that FICM is favorable to tasks or
environments featuring moving objects, which allow FICM to utilize the motion
features between consecutive observations. We further ablatively analyze the
encoding efficiency of FICM, and discuss its applicable domains
comprehensively.Comment: The SOLE copyright holder is IJCAI (International Joint Conferences
on Artificial Intelligence), all rights reserved. The link is provided as
follows: https://www.ijcai.org/Proceedings/2020/28
Text-based Adventures of the Golovin AI Agent
The domain of text-based adventure games has been recently established as a
new challenge of creating the agent that is both able to understand natural
language, and acts intelligently in text-described environments.
In this paper, we present our approach to tackle the problem. Our agent,
named Golovin, takes advantage of the limited game domain. We use genre-related
corpora (including fantasy books and decompiled games) to create language
models suitable to this domain. Moreover, we embed mechanisms that allow us to
specify, and separately handle, important tasks as fighting opponents, managing
inventory, and navigating on the game map.
We validated usefulness of these mechanisms, measuring agent's performance on
the set of 50 interactive fiction games. Finally, we show that our agent plays
on a level comparable to the winner of the last year Text-Based Adventure AI
Competition
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