аналитика - Game AI - # Large Language Model-Based Game Agents

A Comprehensive Survey on Large Language Model-Based Game Agents: Architectures, Methodologies, and Future Directions

Q: How can LLMGAs be further grounded in real-world experiences to develop more human-like intelligence beyond just leveraging pre-trained knowledge?

To develop more human-like intelligence, LLMGAs can be further grounded in real-world experiences by incorporating mechanisms for embodied cognition. This involves allowing agents to interact with and learn from their environments, similar to how humans acquire knowledge through hands-on experience. One approach is to enable LLMGAs to learn from in-context feedback, where they receive evaluations and adjust their strategies based on the outcomes of their actions. This feedback loop helps agents refine their decision-making processes and adapt to different situations. Additionally, LLMGAs can benefit from supervised fine-tuning on real-world data to enhance their performance in specific tasks or domains. By training LLMs on high-quality experience data from real-world scenarios, agents can acquire domain-specific knowledge and improve their capabilities in practical applications. This fine-tuning process allows LLMGAs to leverage real-world experiences to develop more nuanced and contextually relevant responses. Furthermore, incorporating reinforcement learning techniques can enable LLMGAs to learn through trial and error in simulated or real-world environments. By providing rewards or penalties based on the outcomes of their actions, agents can iteratively improve their decision-making skills and adapt to dynamic and complex situations. This reinforcement learning approach allows LLMGAs to explore different strategies and learn from their successes and failures, leading to more robust and adaptive intelligence.

Q: What are the potential ethical and safety concerns in developing highly capable LLMGAs, and how can they be addressed?

The development of highly capable LLMGAs raises several ethical and safety concerns, including issues related to bias, privacy, misuse, and accountability. One major concern is the potential for LLMGAs to perpetuate or amplify existing biases present in the training data, leading to discriminatory or harmful outcomes. To address this, developers can implement bias detection and mitigation techniques, such as diverse training data, fairness constraints, and bias audits, to ensure that LLMGAs make fair and unbiased decisions. Privacy is another significant concern, as LLMGAs may have access to sensitive or personal information during their interactions with users. To safeguard privacy, developers can implement privacy-preserving techniques such as data anonymization, encryption, and access controls to protect user data and prevent unauthorized access. Misuse of highly capable LLMGAs for malicious purposes, such as generating fake news, deepfakes, or engaging in harmful behaviors, poses a serious risk. Developers and policymakers can address this by implementing strict regulations, ethical guidelines, and oversight mechanisms to monitor and control the use of LLMGAs. Ensuring accountability and transparency in the decision-making processes of LLMGAs is essential to address concerns about the lack of explainability and control. By designing LLMGAs with built-in mechanisms for explaining their decisions, providing transparency in their operations, and enabling human oversight and intervention, developers can enhance accountability and trust in the technology.

Q: How can the modular architecture of LLMGAs be extended to support multi-agent cooperation and competition in complex game environments, potentially leading to insights for achieving artificial general intelligence?

The modular architecture of LLMGAs can be extended to support multi-agent cooperation and competition in complex game environments by incorporating specialized modules for communication, coordination, and strategic planning among agents. By integrating modules for understanding and generating natural language dialogues, agents can communicate effectively with each other to coordinate actions, share information, and negotiate strategies in cooperative or competitive settings. Additionally, modules for theory of mind reasoning can enable agents to infer the intentions and beliefs of other agents, facilitating better cooperation and competition in dynamic and uncertain environments. By modeling the mental states of other agents and predicting their behaviors, LLMGAs can make more informed decisions and adapt their strategies based on the actions of their counterparts. Furthermore, reinforcement learning techniques can be used to train LLMGAs to collaborate and compete with each other in complex game environments. By incentivizing cooperative behaviors and strategic decision-making through rewards and penalties, agents can learn to work together towards common goals or outperform each other in competitive scenarios. This approach can provide insights into how artificial general intelligence may emerge from the interactions and dynamics of multiple intelligent agents in complex environments.

Основные понятия

This survey provides a comprehensive overview of the conceptual architecture, methodologies, and future research directions for large language model-based game agents (LLMGAs) across diverse game genres, including adventure, communication, competition, cooperation, simulation, and crafting & exploration.

Аннотация

This survey paper offers a comprehensive review of the literature on LLMGAs, providing a unified reference framework and a taxonomy of six game categories to enhance understanding and facilitate the development and assessment of various LLMGAs.

The paper first introduces the conceptual architecture of LLMGAs, which consists of six essential functional components: perception, memory, thinking, role-playing, action, and learning. It then surveys existing representative LLMGAs documented in the literature with respect to methodologies and adaptation agility across the six game genres.

For adventure games, the survey discusses text-based adventure games that rely on LLMs' commonsense knowledge and video adventure games that leverage multimodal LLMs for perception. For communication games, the paper examines LLMGAs in Werewolf, Avalon, and diplomatic games, highlighting their ability to infer others' intentions and generate consistent dialogues.

In the competition games category, the survey covers LLMGAs playing StarCraft II, Pokémon battles, chess, and poker, demonstrating their reasoning, planning, and strategic decision-making capabilities. For cooperative games, the paper discusses decentralized and centralized cooperation structures, where LLMGAs collaborate to accomplish tasks in Overcooked and Minecraft.

The survey also covers simulation games, including human & social simulation, civilization simulation, and embodied simulation, showcasing LLMGAs' ability to exhibit human-like behaviors, manage civilizations, and accomplish embodied tasks through planning.

Finally, the paper discusses LLMGAs in crafting & exploration games, such as Minecraft and Crafter, highlighting their challenges in understanding complex crafting recipes and navigating open-world environments.

Throughout the survey, the paper identifies key technical challenges, supporting game environments, and optimization strategies employed in the development of various LLMGAs. In the concluding section, the paper presents an outlook of future research and development directions in this burgeoning field.

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Статистика

"The development of game agents holds a critical role in advancing towards Artificial General Intelligence (AGI)."
"LLMs demonstrate astonishing capabilities of generalizing knowledge from huge text corpus data and displaying conversational intelligence in natural language with human-level NLU performance."
"Digital games are recognized as ideal environments for cultivating AI agents due to their complexity, diversity, controllability, safety and reproducibility."
"LLMGAs capable of employing cognitive abilities to gain fundamental insights into gameplay, potentially aligns more closely with the pursuit of AGI."

Цитаты

"Intelligence emerges in the interaction of an agent with an environment and as a result of sensorimotor activity."
"The emergence of multimodal LLMs (MLLMs), such as GPT-4V and Gemini, marks another milestone, enabling LLMs to perceive and understand visual input."
"Unlike traditional Reinforcement Learning (RL)-based agents that make decisions with the goal of maximizing expected rewards through behavior-level policy learning, constructing LLM-based game agents (LLMGAs) capable of employing cognitive abilities to gain fundamental insights into gameplay, potentially aligns more closely with the pursuit of AGI."

Ключевые выводы из

A Survey on Large Language Model-Based Game Agents

by Sihao Hu,Tia... в arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.02039.pdf

A Survey on Large Language Model-Based Game Agents

Дополнительные вопросы

How can LLMGAs be further grounded in real-world experiences to develop more human-like intelligence beyond just leveraging pre-trained knowledge?

To develop more human-like intelligence, LLMGAs can be further grounded in real-world experiences by incorporating mechanisms for embodied cognition. This involves allowing agents to interact with and learn from their environments, similar to how humans acquire knowledge through hands-on experience. One approach is to enable LLMGAs to learn from in-context feedback, where they receive evaluations and adjust their strategies based on the outcomes of their actions. This feedback loop helps agents refine their decision-making processes and adapt to different situations.
Additionally, LLMGAs can benefit from supervised fine-tuning on real-world data to enhance their performance in specific tasks or domains. By training LLMs on high-quality experience data from real-world scenarios, agents can acquire domain-specific knowledge and improve their capabilities in practical applications. This fine-tuning process allows LLMGAs to leverage real-world experiences to develop more nuanced and contextually relevant responses.
Furthermore, incorporating reinforcement learning techniques can enable LLMGAs to learn through trial and error in simulated or real-world environments. By providing rewards or penalties based on the outcomes of their actions, agents can iteratively improve their decision-making skills and adapt to dynamic and complex situations. This reinforcement learning approach allows LLMGAs to explore different strategies and learn from their successes and failures, leading to more robust and adaptive intelligence.

What are the potential ethical and safety concerns in developing highly capable LLMGAs, and how can they be addressed?

The development of highly capable LLMGAs raises several ethical and safety concerns, including issues related to bias, privacy, misuse, and accountability. One major concern is the potential for LLMGAs to perpetuate or amplify existing biases present in the training data, leading to discriminatory or harmful outcomes. To address this, developers can implement bias detection and mitigation techniques, such as diverse training data, fairness constraints, and bias audits, to ensure that LLMGAs make fair and unbiased decisions.
Privacy is another significant concern, as LLMGAs may have access to sensitive or personal information during their interactions with users. To safeguard privacy, developers can implement privacy-preserving techniques such as data anonymization, encryption, and access controls to protect user data and prevent unauthorized access.
Misuse of highly capable LLMGAs for malicious purposes, such as generating fake news, deepfakes, or engaging in harmful behaviors, poses a serious risk. Developers and policymakers can address this by implementing strict regulations, ethical guidelines, and oversight mechanisms to monitor and control the use of LLMGAs.
Ensuring accountability and transparency in the decision-making processes of LLMGAs is essential to address concerns about the lack of explainability and control. By designing LLMGAs with built-in mechanisms for explaining their decisions, providing transparency in their operations, and enabling human oversight and intervention, developers can enhance accountability and trust in the technology.

How can the modular architecture of LLMGAs be extended to support multi-agent cooperation and competition in complex game environments, potentially leading to insights for achieving artificial general intelligence?

The modular architecture of LLMGAs can be extended to support multi-agent cooperation and competition in complex game environments by incorporating specialized modules for communication, coordination, and strategic planning among agents. By integrating modules for understanding and generating natural language dialogues, agents can communicate effectively with each other to coordinate actions, share information, and negotiate strategies in cooperative or competitive settings.
Additionally, modules for theory of mind reasoning can enable agents to infer the intentions and beliefs of other agents, facilitating better cooperation and competition in dynamic and uncertain environments. By modeling the mental states of other agents and predicting their behaviors, LLMGAs can make more informed decisions and adapt their strategies based on the actions of their counterparts.
Furthermore, reinforcement learning techniques can be used to train LLMGAs to collaborate and compete with each other in complex game environments. By incentivizing cooperative behaviors and strategic decision-making through rewards and penalties, agents can learn to work together towards common goals or outperform each other in competitive scenarios. This approach can provide insights into how artificial general intelligence may emerge from the interactions and dynamics of multiple intelligent agents in complex environments.