In any game, play style is a concept that describes the technique and strategy employed by a player to achieve a goal. Identifying a player's style is desirable as it can enlighten players on which approaches work better or worse in different scenarios and inform developers of the value of design decisions. In previous work, we demonstrated an unsupervised LSTM-autoencoder clustering approach for play-style identification capable of handling multidimensional variable length player trajectories. The efficacy of our model was demonstrated on both complete and partial trajectories in both a simulated and natural environment. Lastly, through state frequency analysis, the properties of each of the play styles were identified and compared. This work expands on this approach by demonstrating a process by which we utilize temporal information to identify the decision boundaries related to particular clusters. Additionally, we demonstrate further robustness by applying the same techniques toMiniDungeons, another popular domain for player modeling research. Finally, we also propose approaches for determining mean play-style examples suitable for describing general play-style behaviors and for determining the correct number of represented play-styles.

In any game, play-style is a concept that describes the technique and strategy employed by a player to achieve a goal. Being able to identify the play-style of a player is desirable as it can enlighten players on which approaches work better or worse in different scenarios, as well as inform developers of the value of design decisions. In this paper, we propose a novel approach to play-style identification based on an unsupervised LSTM-autoencoder clustering approach for multi-dimensional trajectory-based data of variable length. We evaluate our approach on two domains and show that not only is our model capable of identifying these play-styles from entire trajectories but it is also capable of this during gameplay from partial trajectories. Additionally, it is demonstrated through state frequency analysis that the properties of each of the play-styles can be identified and compared. Through these processes, we can extract useful information which describes the different behaviours or play-styles present within a domain useful to both players and developers.

Action prediction in video games is the process of extracting useful information in order to predict the future actions of a player. Long-range dependencies and the dynamic nature of video games make it difficult for most algorithms to accurately predict the future actions of players. We propose a novel machine learning approach to improving future action prediction from video game trajectories. This method requires having first clustered player trajectories based on behaviour similarities. Our model consists of a set of LSTM based prediction modules each trained on a subset of data based upon a respective cluster. The effectiveness of our model is analysed on both a synthetic and natural dataset. We find that our future action prediction approach of leveraging multiple models trained on individual data subsets results in greater accuracy over a single model on a complete dataset.

A class K of algebras has the finite embeddability property (FEP) if every finite partial subalgebra of some member of K can be embedded into some finite member of K⁠. We prove the FEP for varieties of decreasing residuated lattice-ordered algebras using a construction based on the canonical extension. This construction produces a (generally) different finite member of the class from alternative FEP constructions for similar classes of algebras. Additionally, the constructed algebra is internally compact, in contrast to other FEP constructions. We give a description of the σ- and π-extensions of operations that do not rely on the notions of closed and open elements and we use this to obtain a syntactic description of a class of inequalities s≤t that are preserved by the construction.

Abstract The logics considered here are the propositional Linear Logic and propositional Intuitionistic Linear Logic extended by a knotted structural rule: . It is proved that the class of algebraic models for such a logic has the finite embeddability property, meaning that every finite partial subalgebra of an algebra in the class can be embedded into a finite full algebra in the class. It follows that each such logic has the finite model property with respect to its algebraic semantics and hence that the logic is decidable.