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 $m$, the oscillon lifetime is exponentially large in these natural units. For typical values of the parameters, we see oscillon lifetimes scaling approximately as $\tau \propto \exp(k E/m)/m$ where $E$ is the oscillon energy and the constant $k$ is on the order of 5 to 15 for expansion rates between $H=0.02m$ and $H=0.01m$.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