30,251 research outputs found
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
A Survey of Monte Carlo Tree Search Methods
Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Current learning machines have successfully solved hard application problems,
reaching high accuracy and displaying seemingly "intelligent" behavior. Here we
apply recent techniques for explaining decisions of state-of-the-art learning
machines and analyze various tasks from computer vision and arcade games. This
showcases a spectrum of problem-solving behaviors ranging from naive and
short-sighted, to well-informed and strategic. We observe that standard
performance evaluation metrics can be oblivious to distinguishing these diverse
problem solving behaviors. Furthermore, we propose our semi-automated Spectral
Relevance Analysis that provides a practically effective way of characterizing
and validating the behavior of nonlinear learning machines. This helps to
assess whether a learned model indeed delivers reliably for the problem that it
was conceived for. Furthermore, our work intends to add a voice of caution to
the ongoing excitement about machine intelligence and pledges to evaluate and
judge some of these recent successes in a more nuanced manner.Comment: Accepted for publication in Nature Communication
Decentralized Adaptive Helper Selection in Multi-channel P2P Streaming Systems
In Peer-to-Peer (P2P) multichannel live streaming, helper peers with surplus
bandwidth resources act as micro-servers to compensate the server deficiencies
in balancing the resources between different channel overlays. With deployment
of helper level between server and peers, optimizing the user/helper topology
becomes a challenging task since applying well-known reciprocity-based choking
algorithms is impossible due to the one-directional nature of video streaming
from helpers to users. Because of selfish behavior of peers and lack of central
authority among them, selection of helpers requires coordination. In this
paper, we design a distributed online helper selection mechanism which is
adaptable to supply and demand pattern of various video channels. Our solution
for strategic peers' exploitation from the shared resources of helpers is to
guarantee the convergence to correlated equilibria (CE) among the helper
selection strategies. Online convergence to the set of CE is achieved through
the regret-tracking algorithm which tracks the equilibrium in the presence of
stochastic dynamics of helpers' bandwidth. The resulting CE can help us select
proper cooperation policies. Simulation results demonstrate that our algorithm
achieves good convergence, load distribution on helpers and sustainable
streaming rates for peers
'I play, therefore I learn?' Measuring the Evolution of Perceived Learning and Game Experience in the Design Flow of a Serious Game
This article explores how the serious game Poverty Is Not a Game (PING) is experienced by high school students in its subsequent design stages. We first focus on the multifaceted construct of game experience and how it is related to serious games. To measure game experience we use the Game Experience Questionnaire and add a perceived learning
scale to account for the specificity of serious games in a classroom. Next, the data obtained from testing PING in 22 classrooms are analyzed. Results suggest that the evolution in the different design stages of the game is not just an issue of game experience, but also of usability. Furthermore, little evidence is found indicating that the learning experience changed positively during the different test phases. However, findings show a strong effect of the game experience on perceived learning while the game experience also varies
significantly between different classrooms
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