Playing two-player games using reinforcement learning and self-play can be challenging due to the complexity of two-player environments and the possible instability in the training process. We propose that a reinforcement learning algorithm can train more efficiently and achieve improved performance in a two-player game if it leverages the knowledge from the single-player version of the same game. This study examines the proposed idea in ten different Atari 2600 environments using the Atari 2600 RAM as the input state. We discuss the advantages of using transfer learning from a single-player training process over training in a two-player setting from scratch, and demonstrate our results in a few measures such as training time and average total reward. We also discuss a method of calculating RAM complexity and its relationship to performance.

Procedural Content Generation (PCG) is defined as the automatic creation of game content using algorithms. PCG has a long history in both the game industry and the academic world. It can increase player engagement and ease the work of game designers. While recent advances in deep learning approaches in PCG have enabled researchers and practitioners to create more sophisticated content, it is the arrival of Large Language Models (LLMs) that truly disrupted the trajectory of PCG advancement. This survey explores the differences between various algorithms used for PCG, including search-based methods, machine learning-based methods, other frequently used methods (e.g., noise functions), and the newcomer, LLMs. We also provide a detailed discussion on combined methods. Furthermore, we compare these methods based on the type of content they generate and the publication dates of their respective papers. Finally, we identify gaps in the existing academic work and suggest possible directions for future research.

The intersection between poetry and music provides an interesting case for computational creativity, yet remains relatively unexplored. This paper explores the integration of poetry and music through the lens of beat patterns, investigating whether a byte-based language model can generate words that fit specific beat patterns within the context of poetry. Drawing on earlier studies, we developed a method to train a byte-based transformer model, ByT5, to align poems with beat patterns. The results demonstrate a high level of beat alignment while maintaining semantic coherence. Future work will aim to improve the model's ability to create complete beat-aligned poems.

In response to the critical need for effective reconnaissance in disaster scenarios, this research article presents the design and implementation of a complete autonomous robot system using the Turtlebot3 with Robotic Operating System (ROS) Noetic. Upon deployment in closed, initially unknown environments, the system aims to generate a comprehensive map and identify any present 'victims' using AprilTags as stand-ins. We discuss our solution for search and rescue missions, while additionally exploring more advanced algorithms to improve search and rescue functionalities. We introduce a Cubature Kalman Filter to help reduce the mean squared error [m] for AprilTag localization and an information-theoretic exploration algorithm to expedite exploration in unknown environments. Just like turtles, our system takes it slow and steady, but when it's time to save the day, it moves at ninja-like speed! Despite Donatello's shell, he's no slowpoke - he zips through obstacles with the agility of a teenage mutant ninja turtle. So, hang on tight to your shells and get ready for a whirlwind of reconnaissance! Full pipeline code https://github.com/rzhao5659/MRProject/tree/main Exploration code https://github.com/rzhao5659/MRProject/tree/main

This paper presents ContrastWSD, a RoBERTa-based metaphor detection model that integrates the Metaphor Identification Procedure (MIP) and Word Sense Disambiguation (WSD) to extract and contrast the contextual meaning with the basic meaning of a word to determine whether it is used metaphorically in a sentence. By utilizing the word senses derived from a WSD model, our model enhances the metaphor detection process and outperforms other methods that rely solely on contextual embeddings or integrate only the basic definitions and other external knowledge. We evaluate our approach on various benchmark datasets and compare it with strong baselines, indicating the effectiveness in advancing metaphor detection.

The rapid advancement in machine learning has led to a surge in automatic data generation, making it increasingly challenging to differentiate between naturally or human-generated data and machine-generated data. Despite these advancements, the generation of creative data remains a challenge. This paper aims to investigate and comprehend the essence of creativity, both in general and within the context of natural language generation. We review various approaches to creative writing devices and tasks, with a specific focus on the generation of poetry. We aim to shed light on the challenges and opportunities in the field of creative data generation.

In recent years, Monte Carlo tree search (MCTS) has achieved widespread adoption within the game community. Its use in conjunction with deep reinforcement learning has produced success stories in many applications. While these approaches have been implemented in various games, from simple board games to more complicated video games such as StarCraft, the use of deep neural networks requires a substantial training period. In this work, we explore on-line adaptivity in MCTS without requiring pre-training. We present MCTS-TD, an adaptive MCTS algorithm improved with temporal difference learning. We demonstrate our new approach on the game miniXCOM, a simplified version of XCOM, a popular commercial franchise consisting of several turn-based tactical games, and show how adaptivity in MCTS-TD allows for improved performances against opponents.

Digital texts have become an increasingly important source of data for social studies. However, textual data from open platforms are vulnerable to manipulation (e.g., censorship and information inflation), often leading to bias in subsequent empirical analysis. This paper investigates the problem of data distortion in text classification when controlling type I error (a relevant textual message is classified as irrelevant) is the priority. The default classical classification paradigm that minimizes the overall classification error can yield an undesirably large type I error, and data distortion exacerbates this situation. As a solution, we propose the Neyman-Pearson (NP) classification paradigm which minimizes type II error under a user-specified type I error constraint. Theoretically, we show that while the classical oracle (i.e., optimal classifier) cannot be recovered under unknown data distortion even if one has the entire post-distortion population, the NP oracle is unaffected by data distortion and can be recovered under the same condition. Empirically, we illustrate the advantage of NP classification methods in a case study that classifies posts about strikes and corruption published on a leading Chinese blogging platform.

Video game virtual characters should interact with the player, each other, and the environment. However, the cost of scripting complex behaviors becomes a bottleneck in content creation. Our goal is to help game designers to more easily populate their open world with background characters that exhibit more believable behaviors. We use a cyclic scheduling model that generates dynamic schedules for the daily lives of virtual characters. The scheduler employs a tiered behavior architecture where behavior components are modular and reusable. This research validates the designer usability of an implementation of this model. We present the results of a user study that evaluates the scheduling system versus manual scripting based on three metrics of behavior creation: behavior completeness, behavior correctness and behavior implementation time. The results indicate that the behavior architecture produces more reliable behaviors and improves designer efficiency which will reduce the cost of generating more believable character behaviors.

As the video game industry grows, both developers and creative authors seek new ways to simplify the process of controlling story content using scripts. This paper describes a story model and its software implementation, ScriptEase II, designed to solve this game design bottleneck. ScriptEase II is the second generation of the ScriptEase system, whose goal was to enable game authors with no programming ability to generate scripting code from high-level game patterns. ScriptEase II differs from the original in two important ways. First, ScriptEase II uses game-dependent translators to generate scripts for any game engine. Second, ScriptEase II uses a drag-and-drop interface that simplifies the story component creation menus that grew cumbersome in the original ScriptEase. The feasibility of code generation has been validated using three different game engines and the advantages of the simple drag-and-drop interface have been validated by a user study.