WebPilot: A Versatile and Autonomous Multi-Agent System for Web Task Execution with Strategic Exploration (2408.15978v1)

Abstract: LLM-based autonomous agents often fail to execute complex web tasks that require dynamic interaction due to the inherent uncertainty and complexity of these environments. Existing LLM-based web agents typically rely on rigid, expert-designed policies specific to certain states and actions, which lack the flexibility and generalizability needed to adapt to unseen tasks. In contrast, humans excel by exploring unknowns, continuously adapting strategies, and resolving ambiguities through exploration. To emulate human-like adaptability, web agents need strategic exploration and complex decision-making. Monte Carlo Tree Search (MCTS) is well-suited for this, but classical MCTS struggles with vast action spaces, unpredictable state transitions, and incomplete information in web tasks. In light of this, we develop WebPilot, a multi-agent system with a dual optimization strategy that improves MCTS to better handle complex web environments. Specifically, the Global Optimization phase involves generating a high-level plan by breaking down tasks into manageable subtasks and continuously refining this plan, thereby focusing the search process and mitigating the challenges posed by vast action spaces in classical MCTS. Subsequently, the Local Optimization phase executes each subtask using a tailored MCTS designed for complex environments, effectively addressing uncertainties and managing incomplete information. Experimental results on WebArena and MiniWoB++ demonstrate the effectiveness of WebPilot. Notably, on WebArena, WebPilot achieves SOTA performance with GPT-4, achieving a 93% relative increase in success rate over the concurrent tree search-based method. WebPilot marks a significant advancement in general autonomous agent capabilities, paving the way for more advanced and reliable decision-making in practical environments.

• The paper introduces WebPilot, a multi-agent system utilizing a dual-phase global and local optimization strategy with strategic exploration for robust web task execution.
• WebPilot achieved a 37.2% success rate on the WebArena benchmark, showing a 93% relative improvement over comparable methods, particularly excelling in complex web environments.
• This work advances autonomous agents' ability to handle dynamic web interactions through adaptive strategies, pointing towards future integration of visual reasoning for enhanced versatility.

An Overview of "WebPilot: A Versatile and Autonomous Multi-Agent System for Web Task Execution with Strategic Exploration"

The paper under scrutiny presents "WebPilot," an autonomous multi-agent system developed to address the limitations of current LLM-based web agents in executing complex web tasks. This paper is a detailed account of the conceptualization, design, and empirical validation of WebPilot, which leverages a dual-phase optimization strategy to enhance flexibility and decision-making when interacting with challenging web environments.

Problem Definition and Motivation

Current LLM-based systems encounter significant challenges when dealing with the complexity and dynamic nature of web environments. These systems often rely on rigid, expert-designed policies, resulting in a lack of flexibility. To counter this, WebPilot employs a strategic exploration paradigm akin to human cognitive processes, allowing for adaptive strategy development through Monte Carlo Tree Search (MCTS)-inspired techniques.

Methodology

WebPilot's framework is built on a dual optimization strategy comprising Global Optimization and Local Optimization phases:

1. Global Optimization: This phase involves the high-level decomposition of tasks into manageable subtasks through Hierarchical Task Decomposition (HTD). A Planner, Controller, and Extractor are employed to ensure dynamic adaptability. The system leverages initial knowledge, using reflective analysis to adapt strategies continuously. Reflective Task Adjustment (RTA) further refines these strategies in light of new observations, enhancing the agent's ability to navigate complex web environments.
2. Local Optimization: WebPilot refines decisions at the subtask level using an adapted version of MCTS. This process, facilitated by the Explorer, Verifier, Appraiser, and Controller, involves Goal-Oriented Selection (GOS) for efficient pathfinding, Reflection-Enhanced Node Expansion (RENE) for strategic refining of actions, and a novel Granular Bifaceted Self-Reward Mechanism that assesses both immediate action effectiveness and potential outcomes (Dynamic Evaluation and Simulation).

Empirical Evaluation

The effectiveness of WebPilot is empirically demonstrated through experimental evaluation on benchmark environments like WebArena and MiniWoB++. In WebArena, WebPilot achieves a 37.2% success rate, a 93% relative improvement over the concurrent tree search-based method, particularly excelling in the GitLab domain. These results underline the superiority of WebPilot's adaptive, multi-agent approach, particularly in real-world, complex, and dynamic web environments. Even when equipped with GPT-3.5, WebPilot remains competitive, highlighting the robustness of its framework.

Critical Analysis and Implications

WebPilot signifies a substantial advancement in the ability of autonomous agents to conduct complex web interactions by adeptly balancing exploration and exploitation. Its strategic decomposition of tasks through high-level planning and real-time iterative refinement mimics human adaptability, making it more versatile than traditional methods.

The implications of WebPilot are broad, impacting both theoretical advancements in AI and practical applications in autonomous web navigation. Particularly, WebPilot's ability to dynamically adapt to unseen tasks suggests a promising trajectory toward more generalizable AI systems.

However, the paper notes several limitations, notably the reliance on text-based observations without visual input, which can hamper performance in tasks where visual context is critical. Future developments might focus on integrating visual reasoning capabilities for a more holistic approach to web task execution, further enhancing the applicability and efficiency of AI systems like WebPilot.

In summary, this paper provides a comprehensive framework for developing more adaptable, efficient autonomous agents using strategic exploration techniques, thus paving the way for advancements in both theory and application within the fields of AI and web interactivity.