Review Of LM-based Agent (LMA) Architecture For Gaming And Issues

3 main points
✔️ We note that while language-centric or multimodal large-scale models (LMs) are rapidly evolving, there is a lack of systematic reviews of their capabilities and potential. In particular, we focus on gameplay scenarios, surveying their current status and identifying outstanding issues.
✔️ There are several key challenges to LMA (Language Model Agent) gameplay. These include resolving illusions, error correction, generalization, and enhancing interpretability.
✔️ There are a variety of ways to address challengessuch as resol ving illusions, error correction, generalization, and enhanced interpretability , but effective feedback mechanisms and strategic implementation are critical. This will improve the quality and efficiency of LMA gameplay, resulting in a smoother play experience.

A Survey on Game Playing Agents and Large Models: Methods, Applications, and Challenges
written by Xinrun Xu、Yuxin Wang、Chaoyi Xu、Ziluo Ding、Jiechuan Jiang、Zhiming Ding、Börje F. Karlsson
(Submitted on 15 Mar 2024)
Comments: 13 pages, 3 figures
Subjects: Artificial Intelligence (cs.AI)

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The images used in this article are from the paper, the introductory slides, or were created based on them.

Summary

This paper points out that while language-centric or multimodal large-scale models (LMs) are rapidly evolving, a systematic review of their capabilities and potential is lacking. In particular, we focus on gameplay scenarios, surveying their current state and identifying outstanding issues. We review existing game LM-based agent (LMA) architectures and summarize commonalities and challenges.

Introduction

The development of large-scale models (LMs) combines language models with multimodal models and has led to significant advances in the fields of natural language processing and computer vision. This has yielded remarkable results in a variety of areas, including text generation, image understanding, and robotics. The application of LM is of particular interest in gameplay scenarios, and its use in complex environments, such as the popular game Minecraft, is being studied. This is because digital games provide complex challenges that require advanced reasoning and cognitive abilities and are important in the context of artificial intelligence research; game agents using LMs may exhibit more interesting generalization abilities than traditionally trained agents. However, there are still many open challenges in this area. To address these challenges, we examine how LM-based game agents perform at the perception, inference, and action stages, followed by an analysis of common challenges and future research directions.

Review

Perception

Multiple inputs, including visual, semantic, and audio, are important to recognize and reflect the information that arises in a game and to select actions. In games, it is common to solve puzzles or find hidden information through text or audio. Digital games offer a richer experience through multimodal information integration, but the existing literature has not focused much on how to integrate audio data into models. This is an issue that needs to be addressed in the future.

In addition, recognition based on semantic information has relied on text items and natural language instructions, but this has its limitations. Therefore, methods for processing richer semantic information are needed. For vision, there are a variety of approaches, such as incorporating game-relevant information into the model or pre-training with image and action data. A combination of these approaches will enable the development of more effective game agents.

Inference

The language model (LM) may serve as a core element of an intelligent agent's cognitive framework, including autonomy, reactivity, spontaneity, and sociability. However, different stages of gameplay have specific requirements. In the beginning stages of the game, the agent needs to absorb important common sense and background knowledge about the game. Then, as the game progresses, the agent's role extends to integrating past game events, managing knowledge, and performing cognitive functions such as information learning, reasoning, decision making, and reflection. The agent also continuously updates or improves its knowledge in preparation for future activities.

Action

This section discusses ways in which language models (LMs) exhibit human-like behavior. Specifically, we focus on the execution of specific behaviors, communication with humans or other agents, and how to ensure consistency in these behaviors.

The LM performs specific actions in the game. This includes text-based interaction and decision-making, manipulation of the game environment through APIs and predefined actions, and manipulation through direct control.LMs also communicate with humans and other agents. This includes text-based dialogue and decision-making, interaction via game-specific APIs, and direct manipulation via input devices (e.g., mouse and keyboard).In addition, the LM must maintain consistency in behavior and communication. This means taking into account past actions and situations, as well as changing behavior as the game progresses.

These elements allow the LM to play a variety of roles in the game and address different aspects of gameplay.

There are several key challenges to LMA (Language Model Agent) gameplay. These include resolving illusions, error correction, generalization, and enhanced interpretability.

First, with respect to resolving illusions, the LMA may output information that is different from the original information. This may include errors and inconsistencies. To address this issue, structured reasoning, situational awareness, interactive approaches, and specific prompting and feedback mechanisms can be used.Second, with regard to error correction, it is important to identify ways to identify and correct errors that LMAs may commit. Iterative feedback and iterative replanning based on environmental feedback can help correct errors and increase accuracy.In addition, generalization capability refers to the ability of LMAs to apply what they learn in one situation to other situations and perform tasks in new environments. This allows LMAs to continuously adapt and solve new problems.

There are many ways to address these challenges, but effective feedback mechanisms and strategic implementation are critical. This will improve the quality and efficiency of LMA gameplay, resulting in a smoother playing experience.

Conclusion

Focusing on the combination of agents and Language Models (LMs), this paper surveys the literature on digital game play, detailing the challenges faced by LMAs and their solutions, as well as providing directions for future research. Particular emphasis was placed on improving multimodal perception and performance in real-time game environments. This will hopefully lead to a more engaging and realistic gaming experience.

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Categories related to this article

• Machine Learning
• Language Processing
• Large Language Models