Agent Planning with World Knowledge Model

Shuofei Qiao, Runnan Fang, Ningyu Zhang, Yuqi Zhu, Xiang Chen, Shumin Deng, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen·May 23, 2024

Summary

This paper introduces a novel World Knowledge Model (WKM) to enhance agent planning in AI by combining global task knowledge and dynamic state knowledge from large language models. WKM improves upon existing LLMs by reducing trial-and-error and hallucinatory actions through self-synthesis of expert and sampled trajectories. Experiments on three complex simulated datasets (ALFWorld, WebShop, and ScienceWorld) demonstrate WKM's superiority over baselines, showcasing its ability to guide strong agent models and generalize to unseen tasks. The study contributes to more effective agent planning in interactive environments, with potential for further development in multi-modal and adaptable world modeling.

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of compensating for the lack of world knowledge in agent models by proposing a World Knowledge Model (WKM) . This problem is not entirely new, as the paper acknowledges that determining what a language model knows and doesn't know has been an ongoing challenge that remains unresolved . The paper also highlights that world knowledge extends beyond textual representations, indicating the need to explore multi-modal world knowledge models as an important future task .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis related to the development and utilization of a World Knowledge Model (WKM) to compensate for the lack of world knowledge in agent models. The primary goal is to explore how a language model's understanding of the world can be enhanced through the integration of a world knowledge model, enabling more informed decision-making and planning by the agent .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes several new ideas, methods, and models in the field of agent planning with a World Knowledge Model (WKM) . These include:

  1. Unified World Knowledge Model: The paper suggests building a unified world knowledge model to enhance agent planning capabilities . This model aims to compensate for the lack of world knowledge in agent models.

  2. Predicting the World like a World Model: The paper introduces the concept of learning to predict the world similar to a world model, which can aid in improving agent planning strategies .

  3. Multi-Modal Agent Planning: Exploring multi-modal world knowledge models is highlighted as an important future task to enhance the agent planning process .

  4. Automated Generation of State-Aware Guidelines: The paper introduces Autoguide, a system for automated generation and selection of state-aware guidelines for large language model agents, which can assist in improving agent planning efficiency .

  5. Adaptable Modular Knowledge Agents: The paper presents AMOR, a method for building adaptable modular knowledge agents through process feedback, which can enhance the adaptability and performance of agents .

  6. Large Language Model Based Multi-Agents: The paper discusses the progress and challenges in using large language model-based multi-agents for agent planning tasks .

  7. Reasoning with Language Model for Planning: The paper emphasizes the importance of reasoning with language models for effective planning with world models, highlighting the interconnectedness of language understanding and agent planning .

These proposed ideas, methods, and models aim to advance the capabilities of agents in planning tasks by leveraging world knowledge, language models, and innovative approaches to enhance decision-making and problem-solving processes . The Agent Planning with World Knowledge Model paper introduces several characteristics and advantages compared to previous methods:

  1. Mitigation of Blind Trial-and-Error: The World Knowledge Model (WKM) is designed to mitigate blind trial-and-error and reduce hallucinatory actions during agent planning tasks .

  2. Improved Planning Efficiency: The WKM outperforms previous methods in terms of the number of planning steps required for each dataset, showcasing its ability to enhance planning efficiency .

  3. Enhanced Performance: In comparison to prompt-based baselines like REACT and Reflexion, the WKM demonstrates superior performance across various datasets, surpassing models like GPT-4 and fine-tuning-based baselines like NAT and ETO on tasks such as ALFWorld and WebShop .

  4. Generalization Ability: The WKM exhibits a strong generalization ability, maintaining its advantage over other methods, especially on unseen tasks, highlighting its effectiveness in handling diverse scenarios .

  5. Task and State Knowledge Integration: The WKM integrates task and state knowledge effectively, with the task knowledge playing a more significant role in enhancing agent performance compared to state knowledge, emphasizing the importance of global prior knowledge for agent planning .

  6. Efficiency Over Fine-Tuning: The paper suggests that integrating world knowledge directly into agent models, as done by the WKM, is more effective than further fine-tuning strategies like SFT or DPO on negative examples, indicating the efficiency of the WKM approach .

  7. Impact on Trajectory Generation: The WKM is responsible for generating instance-level task knowledge and maintaining implicit action constraints, leading to improved trajectory generation by agents, showcasing the effectiveness of the WKM in enhancing agent planning processes .

These characteristics and advantages of the World Knowledge Model presented in the paper demonstrate its potential to significantly enhance agent planning capabilities and performance compared to existing methods, emphasizing the importance of leveraging world knowledge for more effective decision-making and problem-solving in agent tasks .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers and notable researchers in the field of large language models and agent planning have been identified:

  • Noteworthy researchers in this field include Thomas M. Moerland, Joost Broekens, Aske Plaat, Catholijn M. Jonker, Shishir G. Patil, Tianjun Zhang, Xin Wang, Joseph E. Gonzalez, Chen Qian, Xin Cong, Cheng Yang, and many others .
  • The key to the solution mentioned in the paper revolves around building a unified world knowledge model, learning to predict the world like a world model, and applying these concepts to multi-modal agent planning .

How were the experiments in the paper designed?

The experiments in the paper were designed by evaluating the method on three real-world simulated planning datasets: ALFWorld, WebShop, and ScienceWorld. These datasets include both seen and unseen tasks to assess the agent's generalization ability . The reward structure varied across the datasets, with ALFWorld having binary rewards (0 or 1), while WebShop and ScienceWorld provided dense rewards ranging from 0 to 1 to measure task completion levels. The evaluation metric used for all datasets was the average reward . The paper compared the proposed method with state-of-the-art open-source models and baselines, such as Mistral-7B, Gemma-7B, and Llama-3-8B, as well as prompt-based baselines like REACT and Reflexion. Additionally, strong baselines like NAT and ETO were included, which introduced rejected trajectories into the training process to learn from experience .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is comprised of three real-world simulated planning datasets: ALFWorld, WebShop, and ScienceWorld . These datasets are utilized to assess the agent's generalization ability and performance across different tasks. The code for the state-of-the-art open-source models and baselines used in the evaluation, such as Mistral-7B, Gemma-7B, and Llama-3-8B, is open source . The study compares the proposed method with various prompt-based and fine-tuning-based baselines, all of which are detailed in the research .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that need to be verified. The study evaluates the proposed method on real-world simulated planning datasets, including ALFWorld, WebShop, and ScienceWorld, to assess the agent's generalization ability . The models are compared with state-of-the-art open-source models and prompt-based baselines, demonstrating the effectiveness of the World Knowledge Model (WKM) . Additionally, the analysis shows that world knowledge can reduce blind trial-and-error and minimize hallucinatory actions during planning tasks, supporting the hypothesis that incorporating world knowledge enhances agent performance . The results, as depicted in Table 1, highlight the superior performance of WKM compared to other baselines across different datasets, reinforcing the validity of the scientific hypotheses tested in the study .


What are the contributions of this paper?

The paper makes several contributions, including:

  • Model-based reinforcement learning for Atari
  • Integrating formal language and natural language for controllable LLM-based agents
  • Few-shot subgoal planning with language models
  • Decoupled weight decay regularization
  • Plug-and-play compositional reasoning with large language models
  • Synergizing reasoning and acting in language models
  • From robotic process automation to agentic process automation
  • Learning agents with unified data, modular design, and open-source LLMs
  • Enabling generalized agent abilities for LLMs
  • Designing unified data and training pipeline for effective agent learning
  • Agents: An open-source framework for autonomous language agents
  • Knowledge-augmented planning for LLM-based agents

What work can be continued in depth?

To further advance the research in this area, several potential future directions can be explored based on the existing work:

  1. Building a unified world knowledge model to enhance the agent's understanding of the environment .
  2. Developing mechanisms for the agent to learn and predict the world dynamics akin to a world model .
  3. Exploring the application of multi-modal approaches in agent planning to incorporate diverse types of information beyond textual data .
  4. Addressing the challenge of dynamically updating the world knowledge model with real-time changes and feedback from the agent .
  5. Mitigating the additional inference overhead introduced by generating world knowledge to improve computational efficiency .

Introduction
Background
Evolution of AI planning in interactive environments
Limitations of existing LLMs in agent planning
Objective
To develop a novel WKM for improved agent performance
Reduce trial-and-error and hallucinations
Enhance generalization to unseen tasks
Method
Data Collection
Global Task Knowledge
Integration of large language models for task understanding
Extraction of expert and diverse trajectories
Dynamic State Knowledge
Leveraging LLMs for state representation and reasoning
Real-time adaptation to changing environment
Data Preprocessing
Cleaning and filtering of LLM-generated data
Ensuring accuracy and relevance for agent planning
WKM Architecture
Self-synthesis of expert trajectories
Sampling-based trajectory generation
Integration with agent planning algorithms
Experiments and Evaluation
Dataset Description
ALFWorld
WebShop
ScienceWorld
Complexity and diversity of tasks
Baselines
Comparison with existing LLMs and planning algorithms
Performance metrics (e.g., success rate, efficiency)
Results and Analysis
Superiority of WKM over baselines
Effectiveness in guiding strong agent models
Generalization to unseen tasks
Contributions
Advancements in world modeling for AI agents
Potential for multi-modal and adaptable planning
Applications in interactive environments
Future Directions
Multi-modal integration (vision, language, etc.)
Scalability to larger and more complex domains
Real-world deployment and validation
Conclusion
Summary of key findings and implications
Limitations and future research directions
WKM's potential to transform AI agent planning.
Basic info
papers
computation and language
computer vision and pattern recognition
machine learning
artificial intelligence
multiagent systems
Advanced features
Insights
What are the three simulated datasets used to evaluate WKM's performance?
What advantage does WKM demonstrate over baselines in the experiments?
How does WKM differ from existing LLMs in terms of agent planning?
What is the primary purpose of the World Knowledge Model (WKM) introduced in the paper?

Agent Planning with World Knowledge Model

Shuofei Qiao, Runnan Fang, Ningyu Zhang, Yuqi Zhu, Xiang Chen, Shumin Deng, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen·May 23, 2024

Summary

This paper introduces a novel World Knowledge Model (WKM) to enhance agent planning in AI by combining global task knowledge and dynamic state knowledge from large language models. WKM improves upon existing LLMs by reducing trial-and-error and hallucinatory actions through self-synthesis of expert and sampled trajectories. Experiments on three complex simulated datasets (ALFWorld, WebShop, and ScienceWorld) demonstrate WKM's superiority over baselines, showcasing its ability to guide strong agent models and generalize to unseen tasks. The study contributes to more effective agent planning in interactive environments, with potential for further development in multi-modal and adaptable world modeling.
Mind map
ScienceWorld
WebShop
ALFWorld
Extraction of expert and diverse trajectories
Integration of large language models for task understanding
Generalization to unseen tasks
Effectiveness in guiding strong agent models
Superiority of WKM over baselines
Performance metrics (e.g., success rate, efficiency)
Comparison with existing LLMs and planning algorithms
Complexity and diversity of tasks
Integration with agent planning algorithms
Sampling-based trajectory generation
Self-synthesis of expert trajectories
Ensuring accuracy and relevance for agent planning
Cleaning and filtering of LLM-generated data
Real-time adaptation to changing environment
Leveraging LLMs for state representation and reasoning
Global Task Knowledge
Enhance generalization to unseen tasks
Reduce trial-and-error and hallucinations
To develop a novel WKM for improved agent performance
Limitations of existing LLMs in agent planning
Evolution of AI planning in interactive environments
WKM's potential to transform AI agent planning.
Limitations and future research directions
Summary of key findings and implications
Real-world deployment and validation
Scalability to larger and more complex domains
Multi-modal integration (vision, language, etc.)
Applications in interactive environments
Potential for multi-modal and adaptable planning
Advancements in world modeling for AI agents
Results and Analysis
Baselines
Dataset Description
WKM Architecture
Data Preprocessing
Dynamic State Knowledge
Data Collection
Objective
Background
Conclusion
Future Directions
Contributions
Experiments and Evaluation
Method
Introduction
Outline
Introduction
Background
Evolution of AI planning in interactive environments
Limitations of existing LLMs in agent planning
Objective
To develop a novel WKM for improved agent performance
Reduce trial-and-error and hallucinations
Enhance generalization to unseen tasks
Method
Data Collection
Global Task Knowledge
Integration of large language models for task understanding
Extraction of expert and diverse trajectories
Dynamic State Knowledge
Leveraging LLMs for state representation and reasoning
Real-time adaptation to changing environment
Data Preprocessing
Cleaning and filtering of LLM-generated data
Ensuring accuracy and relevance for agent planning
WKM Architecture
Self-synthesis of expert trajectories
Sampling-based trajectory generation
Integration with agent planning algorithms
Experiments and Evaluation
Dataset Description
ALFWorld
WebShop
ScienceWorld
Complexity and diversity of tasks
Baselines
Comparison with existing LLMs and planning algorithms
Performance metrics (e.g., success rate, efficiency)
Results and Analysis
Superiority of WKM over baselines
Effectiveness in guiding strong agent models
Generalization to unseen tasks
Contributions
Advancements in world modeling for AI agents
Potential for multi-modal and adaptable planning
Applications in interactive environments
Future Directions
Multi-modal integration (vision, language, etc.)
Scalability to larger and more complex domains
Real-world deployment and validation
Conclusion
Summary of key findings and implications
Limitations and future research directions
WKM's potential to transform AI agent planning.

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of compensating for the lack of world knowledge in agent models by proposing a World Knowledge Model (WKM) . This problem is not entirely new, as the paper acknowledges that determining what a language model knows and doesn't know has been an ongoing challenge that remains unresolved . The paper also highlights that world knowledge extends beyond textual representations, indicating the need to explore multi-modal world knowledge models as an important future task .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis related to the development and utilization of a World Knowledge Model (WKM) to compensate for the lack of world knowledge in agent models. The primary goal is to explore how a language model's understanding of the world can be enhanced through the integration of a world knowledge model, enabling more informed decision-making and planning by the agent .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes several new ideas, methods, and models in the field of agent planning with a World Knowledge Model (WKM) . These include:

  1. Unified World Knowledge Model: The paper suggests building a unified world knowledge model to enhance agent planning capabilities . This model aims to compensate for the lack of world knowledge in agent models.

  2. Predicting the World like a World Model: The paper introduces the concept of learning to predict the world similar to a world model, which can aid in improving agent planning strategies .

  3. Multi-Modal Agent Planning: Exploring multi-modal world knowledge models is highlighted as an important future task to enhance the agent planning process .

  4. Automated Generation of State-Aware Guidelines: The paper introduces Autoguide, a system for automated generation and selection of state-aware guidelines for large language model agents, which can assist in improving agent planning efficiency .

  5. Adaptable Modular Knowledge Agents: The paper presents AMOR, a method for building adaptable modular knowledge agents through process feedback, which can enhance the adaptability and performance of agents .

  6. Large Language Model Based Multi-Agents: The paper discusses the progress and challenges in using large language model-based multi-agents for agent planning tasks .

  7. Reasoning with Language Model for Planning: The paper emphasizes the importance of reasoning with language models for effective planning with world models, highlighting the interconnectedness of language understanding and agent planning .

These proposed ideas, methods, and models aim to advance the capabilities of agents in planning tasks by leveraging world knowledge, language models, and innovative approaches to enhance decision-making and problem-solving processes . The Agent Planning with World Knowledge Model paper introduces several characteristics and advantages compared to previous methods:

  1. Mitigation of Blind Trial-and-Error: The World Knowledge Model (WKM) is designed to mitigate blind trial-and-error and reduce hallucinatory actions during agent planning tasks .

  2. Improved Planning Efficiency: The WKM outperforms previous methods in terms of the number of planning steps required for each dataset, showcasing its ability to enhance planning efficiency .

  3. Enhanced Performance: In comparison to prompt-based baselines like REACT and Reflexion, the WKM demonstrates superior performance across various datasets, surpassing models like GPT-4 and fine-tuning-based baselines like NAT and ETO on tasks such as ALFWorld and WebShop .

  4. Generalization Ability: The WKM exhibits a strong generalization ability, maintaining its advantage over other methods, especially on unseen tasks, highlighting its effectiveness in handling diverse scenarios .

  5. Task and State Knowledge Integration: The WKM integrates task and state knowledge effectively, with the task knowledge playing a more significant role in enhancing agent performance compared to state knowledge, emphasizing the importance of global prior knowledge for agent planning .

  6. Efficiency Over Fine-Tuning: The paper suggests that integrating world knowledge directly into agent models, as done by the WKM, is more effective than further fine-tuning strategies like SFT or DPO on negative examples, indicating the efficiency of the WKM approach .

  7. Impact on Trajectory Generation: The WKM is responsible for generating instance-level task knowledge and maintaining implicit action constraints, leading to improved trajectory generation by agents, showcasing the effectiveness of the WKM in enhancing agent planning processes .

These characteristics and advantages of the World Knowledge Model presented in the paper demonstrate its potential to significantly enhance agent planning capabilities and performance compared to existing methods, emphasizing the importance of leveraging world knowledge for more effective decision-making and problem-solving in agent tasks .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers and notable researchers in the field of large language models and agent planning have been identified:

  • Noteworthy researchers in this field include Thomas M. Moerland, Joost Broekens, Aske Plaat, Catholijn M. Jonker, Shishir G. Patil, Tianjun Zhang, Xin Wang, Joseph E. Gonzalez, Chen Qian, Xin Cong, Cheng Yang, and many others .
  • The key to the solution mentioned in the paper revolves around building a unified world knowledge model, learning to predict the world like a world model, and applying these concepts to multi-modal agent planning .

How were the experiments in the paper designed?

The experiments in the paper were designed by evaluating the method on three real-world simulated planning datasets: ALFWorld, WebShop, and ScienceWorld. These datasets include both seen and unseen tasks to assess the agent's generalization ability . The reward structure varied across the datasets, with ALFWorld having binary rewards (0 or 1), while WebShop and ScienceWorld provided dense rewards ranging from 0 to 1 to measure task completion levels. The evaluation metric used for all datasets was the average reward . The paper compared the proposed method with state-of-the-art open-source models and baselines, such as Mistral-7B, Gemma-7B, and Llama-3-8B, as well as prompt-based baselines like REACT and Reflexion. Additionally, strong baselines like NAT and ETO were included, which introduced rejected trajectories into the training process to learn from experience .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is comprised of three real-world simulated planning datasets: ALFWorld, WebShop, and ScienceWorld . These datasets are utilized to assess the agent's generalization ability and performance across different tasks. The code for the state-of-the-art open-source models and baselines used in the evaluation, such as Mistral-7B, Gemma-7B, and Llama-3-8B, is open source . The study compares the proposed method with various prompt-based and fine-tuning-based baselines, all of which are detailed in the research .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that need to be verified. The study evaluates the proposed method on real-world simulated planning datasets, including ALFWorld, WebShop, and ScienceWorld, to assess the agent's generalization ability . The models are compared with state-of-the-art open-source models and prompt-based baselines, demonstrating the effectiveness of the World Knowledge Model (WKM) . Additionally, the analysis shows that world knowledge can reduce blind trial-and-error and minimize hallucinatory actions during planning tasks, supporting the hypothesis that incorporating world knowledge enhances agent performance . The results, as depicted in Table 1, highlight the superior performance of WKM compared to other baselines across different datasets, reinforcing the validity of the scientific hypotheses tested in the study .


What are the contributions of this paper?

The paper makes several contributions, including:

  • Model-based reinforcement learning for Atari
  • Integrating formal language and natural language for controllable LLM-based agents
  • Few-shot subgoal planning with language models
  • Decoupled weight decay regularization
  • Plug-and-play compositional reasoning with large language models
  • Synergizing reasoning and acting in language models
  • From robotic process automation to agentic process automation
  • Learning agents with unified data, modular design, and open-source LLMs
  • Enabling generalized agent abilities for LLMs
  • Designing unified data and training pipeline for effective agent learning
  • Agents: An open-source framework for autonomous language agents
  • Knowledge-augmented planning for LLM-based agents

What work can be continued in depth?

To further advance the research in this area, several potential future directions can be explored based on the existing work:

  1. Building a unified world knowledge model to enhance the agent's understanding of the environment .
  2. Developing mechanisms for the agent to learn and predict the world dynamics akin to a world model .
  3. Exploring the application of multi-modal approaches in agent planning to incorporate diverse types of information beyond textual data .
  4. Addressing the challenge of dynamically updating the world knowledge model with real-time changes and feedback from the agent .
  5. Mitigating the additional inference overhead introduced by generating world knowledge to improve computational efficiency .
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