ADAM: An Embodied Causal Agent in Open-World Environments (2410.22194v1)

Abstract: In open-world environments like Minecraft, existing agents face challenges in continuously learning structured knowledge, particularly causality. These challenges stem from the opacity inherent in black-box models and an excessive reliance on prior knowledge during training, which impair their interpretability and generalization capability. To this end, we introduce ADAM, An emboDied causal Agent in Minecraft, that can autonomously navigate the open world, perceive multimodal contexts, learn causal world knowledge, and tackle complex tasks through lifelong learning. ADAM is empowered by four key components: 1) an interaction module, enabling the agent to execute actions while documenting the interaction processes; 2) a causal model module, tasked with constructing an ever-growing causal graph from scratch, which enhances interpretability and diminishes reliance on prior knowledge; 3) a controller module, comprising a planner, an actor, and a memory pool, which uses the learned causal graph to accomplish tasks; 4) a perception module, powered by multimodal LLMs, which enables ADAM to perceive like a human player. Extensive experiments show that ADAM constructs an almost perfect causal graph from scratch, enabling efficient task decomposition and execution with strong interpretability. Notably, in our modified Minecraft games where no prior knowledge is available, ADAM maintains its performance and shows remarkable robustness and generalization capability. ADAM pioneers a novel paradigm that integrates causal methods and embodied agents in a synergistic manner. Our project page is at https://opencausalab.github.io/ADAM.

• The paper introduces ADAM, an embodied causal agent that integrates causal discovery methods to autonomously plan and understand tasks in Minecraft.
• It details a modular architecture with interaction, causal modeling, control, and perception components to enable iterative learning and decision-making.
• Experimental results show 2.2x to 4.6x speedups over state-of-the-art methods, demonstrating robust performance in diverse open-world scenarios.

An Embodied Causal Agent in Open-World Environments: A Technical Overview

The paper under review presents a detailed exploration of an embodied agent, referred to as "ADAM" (An Embodied causal Agent in Minecraft), designed to autonomously navigate and learn in the open-world environment of Minecraft. This research aims to tackle the interpretability and generalization challenges inherent in using existing black-box AI models for open-world gameplay. By integrating causal discovery (CD) methodologies, the agent engages in an iterative process of knowledge acquisition and task execution without relying on prior game-specific knowledge, contributing to its robust generalization capabilities.

Core Contributions

The authors outline four primary modules of the ADAM framework:

1. Interaction Module: This module enables the agent to perform actions within the environment and compile interaction data. This data serves as a foundational component for subsequent causal discovery processes.
2. Causal Model Module: Central to the agent's architecture, this module constructs a causal graph via two distinct CD methods—LLM-based CD for causal assumption generation, and intervention-based CD for assumption refinement. These methodologies collectively enhance interpretability by reducing reliance on antecedent knowledge.
3. Controller Module: Encompassing a planner, actor, and memory pool, this component utilizes the causal graph to decompose tasks into actionable steps, facilitating memory-dependent decision making.
4. Perception Module: Equipped with multimodal LLMs (MLLMs), this module processes environmental data to enable human-like perception and interaction dynamics.

Numerical Results

The paper presents extensive experimental data underscoring the agent’s effectiveness. Notably, in tasks involving the acquisition of diamonds within modified configurations of Minecraft, ADAM showcases a significant performance advantage over existing state-of-the-art (SOTA) methods. Specifically, it achieves a 2.2$\times$ speedup in standard conditions and maintains efficiency in modified conditions with a 4.6$\times$ speedup in less straightforward tasks, demonstrating higher success rates compared to traditional methods.

Implications and Future Directions

The implications of this research extend both practically and theoretically. Practically, this architecture holds promise for enhancing AI robustness in uncertain and dynamic environments beyond gaming scenarios—such as autonomous robotics, where interpretability and adaptability are critical. Theoretically, the integration of causal inference and embodied agents sets a novel precedent for future AI system architectures, emphasizing interpretability without sacrificing performance. Furthermore, the lifelong learning paradigm offers a pathway for continuous adaptation and knowledge refinement, crucial for real-world applications.

Future research directions could investigate the scalability of ADAM’s framework to other complex open-world contexts beyond Minecraft. Additionally, exploring the potential of combining ADAM’s CD approaches with reinforcement learning (RL) methodologies might yield insights into optimal performance balancing between prescriptive task planning and empirical learning adjustments.

Conclusion

This paper makes a substantial contribution to the field of AI research in open-world environments. By developing an agent that autonomously constructs a causal understanding of dynamic gameplay scenarios, the authors not only address significant limitations in existing models but also pave the way for further exploration into robust, interpretable AI systems. The framework promises adaptability to varying environments, delineating a path for future advancements in both autonomous systems and AI-driven computational models.