908 research outputs found
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
Solitons in a Baby-Skyrme model with invariance under area preserving diffeomorphisms
We study the properties of soliton solutions in an analog of the Skyrme model
in 2+1 dimensions whose Lagrangian contains the Skyrme term and the mass term,
but no usual kinetic term. The model admits a symmetry under area preserving
diffeomorphisms. We solve the dynamical equations of motion analytically for
the case of spinning isolated baryon type solitons. We take fully into account
the induced deformation of the spinning Skyrmions and the consequent
modification of its moment of inertia to give an analytical example of related
numerical behaviour found by Piette et al.. We solve the equations of motion
also for the case of an infinite, open string, and a closed annular string. In
each case, the solitons are of finite extent, so called "compactons", being
exactly the vacuum outside a compact region. We end with indications on the
scattering of baby-Skyrmions, as well as some considerations as the properties
of solitons on a curved space.Comment: 30 pages, 5 figures, revtex, major modifications, conclusions
modifie
Learning Policies from Self-Play with Policy Gradients and MCTS Value Estimates
In recent years, state-of-the-art game-playing agents often involve policies
that are trained in self-playing processes where Monte Carlo tree search (MCTS)
algorithms and trained policies iteratively improve each other. The strongest
results have been obtained when policies are trained to mimic the search
behaviour of MCTS by minimising a cross-entropy loss. Because MCTS, by design,
includes an element of exploration, policies trained in this manner are also
likely to exhibit a similar extent of exploration. In this paper, we are
interested in learning policies for a project with future goals including the
extraction of interpretable strategies, rather than state-of-the-art
game-playing performance. For these goals, we argue that such an extent of
exploration is undesirable, and we propose a novel objective function for
training policies that are not exploratory. We derive a policy gradient
expression for maximising this objective function, which can be estimated using
MCTS value estimates, rather than MCTS visit counts. We empirically evaluate
various properties of resulting policies, in a variety of board games.Comment: Accepted at the IEEE Conference on Games (CoG) 201
Habitability and Biosignatures of Hycean Worlds
We investigate a new class of habitable planets composed of water-rich
interiors with massive oceans underlying H2-rich atmospheres, referred to here
as Hycean worlds. With densities between those of rocky super-Earths and more
extended mini-Neptunes, Hycean planets can be optimal candidates in the search
for exoplanetary habitability and may be abundant in the exoplanet population.
We investigate the bulk properties (masses, radii, and temperatures), potential
for habitability, and observable biosignatures of Hycean planets. We show that
Hycean planets can be significantly larger compared to previous considerations
for habitable planets, with radii as large as 2.6 Earth radii (2.3 Earth radii)
for a mass of 10 Earth masses (5 Earth masses). We construct the Hycean
habitable zone (HZ), considering stellar hosts from late M to sun-like stars,
and find it to be significantly wider than the terrestrial-like HZ. While the
inner boundary of the Hycean HZ corresponds to equilibrium temperatures as high
as ~500 K for late M dwarfs, the outer boundary is unrestricted to arbitrarily
large orbital separations. Our investigations include tidally locked `Dark
Hycean' worlds that permit habitable conditions only on their permanent
nightsides and `Cold Hycean' worlds that see negligible irradiation. Finally,
we investigate the observability of possible biosignatures in Hycean
atmospheres. We find that a number of trace terrestrial biomarkers which may be
expected to be present in Hycean atmospheres would be readily detectable using
modest observing time with the James Webb Space Telescope (JWST). We identify a
sizable sample of nearby potential Hycean planets that can be ideal targets for
such observations in search of exoplanetary biosignatures
Ludii -- The Ludemic General Game System
While current General Game Playing (GGP) systems facilitate useful research
in Artificial Intelligence (AI) for game-playing, they are often somewhat
specialised and computationally inefficient. In this paper, we describe the
"ludemic" general game system Ludii, which has the potential to provide an
efficient tool for AI researchers as well as game designers, historians,
educators and practitioners in related fields. Ludii defines games as
structures of ludemes -- high-level, easily understandable game concepts --
which allows for concise and human-understandable game descriptions. We
formally describe Ludii and outline its main benefits: generality,
extensibility, understandability and efficiency. Experimentally, Ludii
outperforms one of the most efficient Game Description Language (GDL)
reasoners, based on a propositional network, in all games available in the
Tiltyard GGP repository. Moreover, Ludii is also competitive in terms of
performance with the more recently proposed Regular Boardgames (RBG) system,
and has various advantages in qualitative aspects such as generality.Comment: Accepted at ECAI 202
Unnatural Oscillon Lifetimes in an Expanding Background
We consider a classical toy model of a massive scalar field in 1+1 dimensions
with a constant exponential expansion rate of space. The nonlinear theory under
consideration supports approximate oscillon solutions, but they eventually
decay due to their coupling to the expanding background. Although all the
parameters of the theory and the oscillon energies are of order one in units of
the scalar field mass , the oscillon lifetime is exponentially large in
these natural units. For typical values of the parameters, we see oscillon
lifetimes scaling approximately as where is
the oscillon energy and the constant is on the order of 5 to 15 for
expansion rates between and .Comment: 7 pages, 2 .eps figures; v2: expanded discussion of decay, fixed
typos; version to appear in Physics Letters
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
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
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