Reviewerly/exHarmony huggingface dataset:
From the history of semiotics in the USSR: Georgy Kh. Shingarov

Abstract Georgy Kh. Shingarov is one of the few philosophers of the Soviet period who studied the conditioned reflex as a sign system. The originality of his theory, created in accordance with the provisions of dialectical materialism, is shown. He considered the signal in the model of the conditioned reflex as a sign, explaining its formation by the process of dialectical ‘sublation’, i.e. the transformation of a material unconditioned stimulus into an idealized object, devoid of the properties of materiality, but which is the result of the manifestation of matter. The socio-political conditions that determined the features of this theory are described.
Investigating Reinforcement Learning for Dynamic Difficulty Adjustment

Dynamic Difficulty Adjustment (DDA) is a technique to automatically adjust various game factors, such as items, maps, or opponent behavior, to provide players with a challenging and engaging experience. The goal is to maintain a balance ensuring an optimal level of enjoyment. In this work, we propose a reinforcement learning agent in a fighting game to create an opponent that matches the player's skill level. We propose a reward function that leads the player to have similar relative skill to his opponent and maintain a balanced match. Additionally, we introduce a penalty given to the agent during training to constrain its win rate. Therefore, creating an opponent that is not too wear nor too strong. We also explore regularization techniques to improve the agent's performance and adaptability. We show that regularization improves over the baseline in generalizing its behavior to handle opponents not encountered during training.
RPG Creature Design: Cross-System Analysis and Conversion

The Role-Playing Game (RPG) genre has achieved tremendous popularity, which is evidenced by its significant sales and high profits. One of the key reasons behind its success is the ability to model a vast array of entities, including creatures and characters, within RPG systems. However, it is worth noting that only a limited number of literary works delve into the automated conversion of entity designs across different RPG systems. This paper showcases a series of research endeavors aimed at automating RPG content, particularly focusing on creatures, involving constructing a creature database for three RPG systems, emphasizing data linkage. Three system conversion approaches are introduced, two leveraging Machine Learning (ML) techniques, and one relying on manual modeling. To assess the effectiveness of the conversions, error metrics were employed alongside feedback obtained from 14 players. Results suggest that despite ML methods yielding lower numeric errors, the creature sheets created using manual modeling were better accepted by human evaluators.
A Dynamic Difficulty Adjustment Algorithm With Generic Player Behavior Classification Unity Plugin In Single Player Games

With the game market growing year by year, game developers find themselves in an extremely competitive scenario. To draw players attention towards their game and to engage them even more during gameplay, one alternative is to apply a Dynamic Difficulty Adjustment algorithm. But the problem of the DDA approach is usually not the algorithm itself, but the player classification step. Therefore, we created a generic Unity Plugin that, allied with a Python API, will be able to classify players, using unsupervised and supervised Machine Learning techniques, based on game telemetry. We also implemented our own simple DDA algorithm, to test how it would work allied with the online classification process. The results show that the DDA version outperforms the standard one in the Video-Game category (CEGE Framework). The resultant classification was 63% completely accurate and 100% partially accurate. Moreover, no other work was able to create a generic plugin that simplified the use of ML in the game development context, allowing to test 28 different algorithm combinations.
A Semantic Segmentation System for generating context-based tile-maps

Training a Generative Adversarial Network (GAN) involves a two-step process: training the generator network and training the discriminator network. The generator tries to generate realistic data, while the discriminator aims to distinguish between real and generated data. In this work we propose a semantic segmentation system that uses regular images for generating semantic maps through Tensor Flow framework. These maps are associated with a discrete set of tiles, which can be used for training generation of game style tile-maps. Besides the data-set creation, our solution also allows the creation of tile-maps based on image samples.
Unveiling the Key Features Influencing Game Agents with Different Levels of Robustness

Training agents using Deep Reinforcement Learning methods is rapidly progressing in several fields and techniques like domain randomization have been demonstrated to improve the generalization ability of these agents. However, due to the black-box nature of the models, it is not easy to understand why an action was selected from a given input. Although prior research on Explainable Artificial Intelligence presents efforts to bridge this gap, is unclear what particular input features that contribute to a model's generalizability. This work examines the main aspects that affect the behavior of game agents with varying robustness levels. By comparing specialized and generalized agents, we investigate what are the main differences and similarities present in these models when they select an action. To achieve this goal, we trained two agents with different robustness levels and applied Explainable Artificial Intelligence methods to highlight the key features on the input screen. We employed a mixed methods analysis, which provided important quantitative results on the agents' performance as well as qualitative insights about their behavior. We are able to show that the visualization of generalized agents tends to be more interpretable since they concentrate on the game objects, whereas specialized agents are more spread along the whole input screen. This result constitutes an important step to understanding the behavior of game agents trained using Deep Reinforcement Learning with different training procedures.
Towards Scalable Cloud Gaming Systems: Decoupling Physics from the Game Engine

By leveraging cloud computing resources, cloud gaming, also known as Games-as-a-Service (GaaS), has emerged as a new computer game delivery paradigm that promises gaming anywhere, anytime, on any device. In this regard, game engine architectures are one of the main objects of study when adapting digital games to cloud gaming. However, most of the proposed cloud gaming architectures cannot take full advantage of cloud computing resources to offer system scalability due to a monolith design. In this paper, we evaluate the impacts of a distributed architecture in terms of system performance by employing the service-oriented architecture paradigm to offload the game physics calculations as a decoupled system. As a differential, our work contributes to the discussion by implementing a proof-of-concept to compare the game engine performance between the monolith and distributed approaches by utilizing a modern public cloud provider, which as far as we know, has not yet been done in the literature. As a result, the distributed approach had a better performance in computation-intensive physics calculations scenarios. In this case, the communication overhead is outweighed by performance gains, as the physics engine was deployed on a compute-optimized VM. This further indicates the benefits of decoupling game engine systems, not only better game performance but also taking a step further for a scalable cloud gaming system, which can scale better both vertically and horizontally.
Hybrid Minimax-MCTS and Difficulty Adjustment for General Game Playing

Board games are a great source of entertainment for all ages, as they create a competitive and engaging environment, as well as stimulating learning and strategic thinking. It is common for digital versions of board games, as any other type of digital games, to offer the option to select the difficulty of the game. This is usually done by customizing the search parameters of the AI algorithm. However, this approach cannot be extended to General Game Playing agents, as different games might require different parametrization for each difficulty level. In this paper, we present a general approach to implement an artificial intelligence opponent with difficulty levels for zero-sum games, together with a propose of a Minimax-MCTS hybrid algorithm, which combines the minimax search process with GGP aspects of MCTS. This approach was tested in our mobile application LoBoGames, an extensible board games platform, that is intended to have an broad catalog of games, with an emphasis on accessibility: the platform is friendly to visually-impaired users, and is compatible with more than 92% of Android devices. The tests in this work indicate that both the hybrid Minimax-MCTS and the new difficulty adjustment system are promising GGP approaches that could be expanded in future work.
How to improve the quality of GAN-based map generators

Procedural Content Generation algorithms aim to create unique and variable dungeon maps, ensuring that players encounter infinite maps in the game. This capability is essential to prevent repetitive environments, keeping players engaged and providing them with new challenges and discoveries. Machine learning techniques, such as Generative Adversarial Networks (GANs), have proven effective in generating data, although they may have specific limitations. This paper proposes a GAN-based approach for generating dungeon maps and introduces three optimizations to enhance the training process. Our approach achieves remarkable results in producing valid and varied maps compared to existing methods. We demonstrate that our approach outperforms other approaches by generating more valid maps with increased variability.
Evolutionary Tabletop Game Design: A Case Study in the Risk Game

Creating and evaluating games manually is an arduous and laborious task. Procedural content generation can aid by creating game artifacts, but usually not an entire game. Evolutionary game design, which combines evolutionary algorithms with automated playtesting, has been used to create novel board games with simple equipment; however, the original approach does not include complex tabletop games with dice, cards, and maps. This work proposes an extension of the approach for tabletop games, evaluating the process by generating variants of Risk, a military strategy game where players must conquer map territories to win. We achieved this using a genetic algorithm to evolve the chosen parameters, as well as a rules-based agent to test the games and a variety of quality criteria to evaluate the new variations generated. Our results show the creation of new variations of the original game with smaller maps, resulting in shorter matches. Also, the variants produce more balanced matches, maintaining the usual drama. We also identified limitations in the process, where, in many cases, where the objective function was correctly pursued, but the generated games were nearly trivial. This work paves the way towards promising research regarding the use of evolutionary game design beyond classic board games.
DDA-MAPEKit: A Framework for Dynamic Difficulty Adjustment Based on MAPE-K Loop

Dynamic Difficulty Adjustment (DDA) has emerged as a prominent solution to address the demand for adaptive gameplay in digital games. However, various research challenges within the realm of DDA still require attention. This paper introduces an approach that addresses some of these challenges by merging the knowledge of self-adaptive systems with the specific requirements of adaptive gameplay. We present DDA-MAPEKit, a framework developed for Unity Engine, a solution that implements this approach. It was constructed based on the modular MAPE-K loop, enabling the integration of multiple DDA strategies. The aim is to provide customized treatment for each game mechanics by constructing a separate MAPE-K loop for each one of them. To examine the feasibility of the proposed model, a proof of concept is conducted through the application of DDA-MAPEKit in an exergame designed for telerehabilitation purposes. The results were promising. By comparing and analyzing the data gathered during simulations with and without DDA, it was observed that the inclusion of the DDA mechanism created with DDA-MAPEKit led to the adaptation of the variables that depict the complexity of the game mechanics according to the player's performance. Hence, the effectiveness and feasibility of this mechanism are demonstrated by these findings, paving the way for further research.
Scale-Invariant Reinforcement Learning in Real-Time Strategy Games

Real-time strategy games present a significant challenge for artificial game-playing agents by combining several fundamental AI problems. Despite the difficulties, attempts to create autonomous agents using Deep Reinforcement Learning have been successful, with bots like AlphaStar beating even expert human players. Many RTS games include several distinct world maps with different dimensions, which may affect the agent's observation and the representation of game states. However, most current architectures suffer from fixed input sizes or require extensive and complex training. In this paper, we overcome these limitations by combining Grid-Wise Control with Spatial Pyramid Pooling (SPP). Specifically, we employ the encoder-decoder framework provided by the GridNet architecture and enhance the critic component of PPO by adding an SPP layer to it. The new layer generates a standardized representation of any game state regardless of the initial observation dimensions, allowing the agent to act on any map. Our evaluation demonstrates that our proposed method improves the models' flexibility and provides a more effective and efficient solution for training autonomous agents in multiple RTS game scenarios.
Deceptive Topographic Path Planning

Deception is essential to model real-world agent behaviors in entertainment and serious games. This paper approaches the challenge of planning deceptive routes on topographic terrains where topographic path costs and other deceptive path factors are analyzed. The work explores alternative deceptive strategies tuned to explore the terrain costs to determine how deceptive the path search nodes are and to return paths with low topographic costs. In addition to the A* algorithm, the Theta* algorithm is explored in the search for smoother deceptive routes, which better represent routes used by terrestrial agents in real-world adversarial situations. It also analyzes deceptive topographic paths computed according to the Last Deceptive Point (LDP) and Last Topographic Deceptive Point (LDPT) notions, where examples of returned paths are presented with the help of terrain relief scenarios. Experimental results with the proposed methods are statistically analyzed according to various path search metrics, showing that all deceptive strategies with A* compute topographic paths with higher numbers of deceptive nodes on reduced execution times. Moreover, the strategies with Theta* and LDPT return paths that show a useful combination of topographic path quality and number of deceptive nodes.
An Experimental Evaluation of Anomaly Detection in Time Series

Anomaly detection in time series data has been studied for decades in both statistics and computer science. Various algorithms have been proposed for different scenarios, such as fraud detection, environmental monitoring, manufacturing, and healthcare. However, there is a lack of comparative evaluation of these state-of-the-art approaches, especially in the same test environment and with the same benchmark, making it difficult for users to select an appropriate method for real-world applications. In this paper, we present a taxonomy of anomaly detection methods based on the main features, i.e., data dimension, processing technique, and anomaly type and six inner classes. We perform systematic intra- and inter-class comparisons of seventeen state-of-the-art algorithms on real and synthetic datasets with a point metric commonly used in classification problems and a range metric specifically designed for subsequence anomalies in time series data. We analyze the properties of these algorithms and test them in terms of effectiveness, efficiency, and robustness to anomaly rates, data sizes, number of dimensions, anomaly patterns, and threshold settings. We also test their performance in different use cases. Finally, we provide a practical guide for detecting anomalies in time series and discussions.
SmartLite: A DBMS-Based Serving System for DNN Inference in Resource-Constrained Environments

Many IoT applications require the use of multiple deep neural networks (DNNs) to perform various tasks on low-cost edge devices with limited computation resources. However, existing DNN model serving platforms, such as TensorFlow Serving and TorchServe, are resource-intensive and require high-performance GPUs that are often not available on low-cost edge devices. In this paper, we propose SmartLite, a lightweight DBMS that addresses these challenges by storing the parameters and structural information of neural networks as database tables and implementing neural network operators inside the DBMS engine. SmartLite quantizes model parameters as binarized values, applies neural pruning techniques to compress the models, and transforms tensor manipulations into value lookup operations of the DBMS to reduce computation overhead. Experimental results show that SmartLite requires 98% less memory while achieving about a 134% performance speedup compared to Torch-Serve. Our proposed solution addresses the challenges of running multiple DNN models on low-cost edge devices and provides a significant contribution to the field of IoT applications.
RAGraph: A Region-Aware Framework for Geo-Distributed Graph Processing

In many global businesses of multinational enterprises, graph-structure data is usually geographically distributed in different regions to support low-latency services. Geo-distributed graph processing suffers from the Wide Area Networks (WANs) with scarce and heterogeneous bandwidth, thus essentially differs from traditional distributed graph processing. In this paper, we propose RAGraph, a Region-Aware framework for geo-distributed graph processing. At the core of RAGraph, we design a region-aware graph processing framework that allows advancing inefficient global updates locally and enables sensible coordination-free message interactions. RAGraph also contains an adaptive hierarchical message interaction engine to switch interaction modes adaptively based on network heterogeneity and fluctuation, and a discrepancy-aware message filtering strategy to filter important messages. Finally, the experiments show that RAGraph can achieve 1.69X - 40.53X speedup and 20.9% - 97% WAN cost reduction compared with state-of-the-art systems.
Missing Value Imputation for Multi-Attribute Sensor Data Streams via Message Propagation

Sensor data streams occur widely in various real-time applications in the context of the Internet of Things (IoT). However, sensor data streams feature missing values due to factors such as sensor failures, communication errors, or depleted batteries. Missing values can compromise the quality of real-time analytics tasks and downstream applications. Existing imputation methods either make strong assumptions about streams or have low efficiency. In this study, we aim to accurately and efficiently impute missing values in data streams that satisfy only general characteristics in order to benefit real-time applications more widely. First, we propose a message propagation imputation network (MPIN) that is able to recover the missing values of data instances in a time window. We give a theoretical analysis of why MPIN is effective. Second, we present a continuous imputation framework that consists of data update and model update mechanisms to enable MPIN to perform continuous imputation both effectively and efficiently. Extensive experiments on multiple real datasets show that MPIN can outperform the existing data imputers by wide margins and that the continuous imputation framework is efficient and accurate.
Confidence Intervals for Private Query Processing

Whenever randomness is involved in query processing, confidence intervals are commonly returned to the user to indicate the statistical significance of the query answer. However, this problem has not been explicitly addressed under differential privacy, which must use randomness by definition. For some classical mechanisms whose noise distribution does not depend on the input, such as the Laplace and the Gaussian mechanism, deriving confidence intervals is easy. But the problem becomes nontrivial for queries whose global sensitivity is large or unbounded, for which these classical mechanisms cannot be applied. There are three main techniques in the literature for dealing with such queries: the exponential mechanism, the sparse vector technique, and the smooth sensitivity. In this paper, for each of the three techniques we design mechanisms to produce confidence intervals that are (1) differentially private; (2) correct, i.e., the interval contains the true query answer with the specified confidence level; and (3) have a utility guarantee matching that of the original mechanism, up to constant factors. Then we show how to apply our techniques to a variety of problems ranging from simple statistics (e.g., mean, median, maximum) to graph pattern counting and conjunctive queries.
Demystifying Graph Sparsification Algorithms in Graph Properties Preservation

Graph sparsification is a technique that approximates a given graph by a sparse graph with a subset of vertices and/or edges. The goal of an effective sparsification algorithm is to maintain specific graph properties relevant to the downstream task while minimizing the graph's size. Graph algorithms often suffer from long execution time due to the irregularity and the large real-world graph size. Graph sparsification can be applied to greatly reduce the run time of graph algorithms by substituting the full graph with a much smaller sparsified graph, without significantly degrading the output quality. However, the interaction between numerous sparsifiers and graph properties is not widely explored, and the potential of graph sparsification is not fully understood. In this work, we cover 16 widely-used graph metrics, 12 representative graph sparsification algorithms, and 14 real-world input graphs spanning various categories, exhibiting diverse characteristics, sizes, and densities. We developed a framework to extensively assess the performance of these sparsification algorithms against graph metrics, and provide insights to the results. Our study shows that there is no one sparsifier that performs the best in preserving all graph properties, e.g. sparsifiers that preserve distance-related graph properties (eccentricity) struggle to perform well on Graph Neural Networks (GNN). This paper presents a comprehensive experimental study evaluating the performance of sparsification algorithms in preserving essential graph metrics. The insights inform future research in incorporating matching graph sparsification to graph algorithms to maximize benefits while minimizing quality degradation. Furthermore, we provide a framework to facilitate the future evaluation of evolving sparsification algorithms, graph metrics, and ever-growing graph data.