2,410 research outputs found
Rolling Horizon Evolutionary Algorithms for General Video Game Playing
IEEE Game-playing Evolutionary Algorithms, specifically Rolling Horizon Evolutionary Algorithms, have recently managed to beat the state of the art in win rate across many video games. However, the best results in a game are highly dependent on the specific configuration of modifications introduced over several papers, each adding additional parameters to the core algorithm. Further, the best previously published parameters have been found from only a few human-picked combinations, as the possibility space has grown beyond exhaustive search. This paper presents the state of the art in Rolling Horizon Evolutionary Algorithms, combining all modifications described in literature, as well as new ones. We then use a parameter optimiser, the N-Tuple Bandit Evolutionary Algorithm, to find the best combination of parameters in 20 games from the General Video Game AI Framework. Further, we analyse the algorithm's parameters and some interesting combinations revealed through the optimisation process. Lastly, we find new state of the art solutions on several games by automatically exploring the large parameter space of RHEA
Rolling Horizon Coevolutionary planning for two-player video games
This paper describes a new algorithm for decision making in two-player real-time video games. As with Monte Carlo Tree Search, the algorithm can be used without heuristics and has been developed for use in general video game AI. The approach is to extend recent work on rolling horizon evolutionary planning, which has been shown to work well for single-player games, to two (or in principle many) player games. To select an action the algorithm co-evolves two (or in the general case N) populations, one for each player, where each individual is a sequence of actions for the respective player. The fitness of each individual is evaluated by playing it against a selection of action-sequences from the opposing population. When choosing an action to take in the game, the first action is chosen from the fittest member of the population for that player. The new algorithm is compared with a number of general video game AI algorithms on a two-player space battle game, with promising results
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
AI Researchers, Video Games Are Your Friends!
If you are an artificial intelligence researcher, you should look to video
games as ideal testbeds for the work you do. If you are a video game developer,
you should look to AI for the technology that makes completely new types of
games possible. This chapter lays out the case for both of these propositions.
It asks the question "what can video games do for AI", and discusses how in
particular general video game playing is the ideal testbed for artificial
general intelligence research. It then asks the question "what can AI do for
video games", and lays out a vision for what video games might look like if we
had significantly more advanced AI at our disposal. The chapter is based on my
keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad
audience.Comment: in Studies in Computational Intelligence Studies in Computational
Intelligence, Volume 669 2017. Springe
Rolling Horizon NEAT for General Video Game Playing
This paper presents a new Statistical Forward Planning (SFP) method, Rolling
Horizon NeuroEvolution of Augmenting Topologies (rhNEAT). Unlike traditional
Rolling Horizon Evolution, where an evolutionary algorithm is in charge of
evolving a sequence of actions, rhNEAT evolves weights and connections of a
neural network in real-time, planning several steps ahead before returning an
action to execute in the game. Different versions of the algorithm are explored
in a collection of 20 GVGAI games, and compared with other SFP methods and
state of the art results. Although results are overall not better than other
SFP methods, the nature of rhNEAT to adapt to changing game features has
allowed to establish new state of the art records in games that other methods
have traditionally struggled with. The algorithm proposed here is general and
introduces a new way of representing information within rolling horizon
evolution techniques.Comment: 8 pages, 5 figures, accepted for publication in IEEE Conference on
Games (CoG) 202
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