Benchmarking Mobile Device Control Agents across Diverse Android Configurations

แนวคิดหลัก

Developing autonomous agents for mobile device control can significantly enhance user interactions, but the lack of a commonly adopted benchmark makes it challenging to quantify scientific progress in this area. This work introduces B-MoCA, a novel benchmark designed specifically for evaluating mobile device control agents across diverse Android configurations.

บทคัดย่อ

The authors introduce B-MoCA, a benchmark designed to evaluate the performance of mobile device control agents on diverse Android device configurations. The key features of B-MoCA include:

1. It is based on the Android operating system, providing a realistic environment for evaluating agents.
2. It defines 60 common daily tasks that involve commonly used applications like Chrome and Calendar, ensuring relevance to everyday life.
3. It incorporates a randomization feature that changes various aspects of mobile devices, including user interface layouts, wallpapers, languages, and device types, to assess the generalization performance of agents.
4. It provides rule-based success detectors to reliably evaluate the agents' performance in completing the tasks.

The authors benchmark three types of agents: LLM agents, MLLM agents, and Vision-Language-UI (VLUI) agents. LLM agents and MLLM agents utilize foundation models like LLMs and MLLMs, respectively, while VLUI agents are trained from scratch using human expert demonstrations.

The experiments reveal that the agents exhibit fundamental skills in mobile device control, such as solving straightforward tasks or completing tasks in training environments. However, they struggle in more challenging scenarios, such as handling more difficult tasks or generalizing to unseen device configurations. The authors analyze the strengths and limitations of each agent type and discuss the effects of different design choices, such as the use of pre-trained encoders and training data diversity for VLUI agents.

The authors open-source the source code and relevant materials for B-MoCA, aiming to help future researchers identify challenges in building assistive agents and easily compare the efficacy of their methods over the prior work.

สถิติ

"Developing autonomous agents for mobile devices can significantly enhance user interactions by offering increased efficiency and accessibility."
"To create a realistic benchmark, we develop B-MoCA based on the Android operating system and define 60 common daily tasks."
"We benchmark diverse agents, including agents employing large language models (LLMs) or multi-modal LLMs (MLLMs) as well as agents trained from scratch using human expert demonstrations."

คำพูด

"While these agents demonstrate proficiency in executing straightforward tasks, their poor performance on complex tasks highlights significant opportunities for future research to enhance their effectiveness."
"Our source code is publicly available at https://b-moca.github.io."

ข้อมูลเชิงลึกที่สำคัญจาก

Benchmarking Mobile Device Control Agents across Diverse Configurations

by Juyong Lee,T... ที่ arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16660.pdf

สอบถามเพิ่มเติม

How can the benchmark be extended to include more diverse and complex tasks that better reflect real-world mobile device usage?

To enhance the benchmark and make it more reflective of real-world mobile device usage, several strategies can be implemented:

Incorporating Multi-Step Tasks: Introduce tasks that require multiple sequential steps to complete, mimicking real-world scenarios where users perform a series of actions to achieve a goal. This can include tasks like setting up a new device, customizing settings, or managing multiple applications simultaneously.

Integrating Text Input Tasks: Include tasks that involve text input, such as composing emails, sending messages, or conducting web searches. This will add complexity to the tasks and test the agents' ability to interact with text-based interfaces.

Adding Time-Sensitive Tasks: Introduce tasks that have time constraints, such as setting reminders, scheduling events, or responding to notifications within a specific timeframe. This will test the agents' efficiency and responsiveness in time-sensitive situations.

Simulating Real-World Scenarios: Design tasks that simulate common real-world scenarios, like making online purchases, navigating through complex applications, or troubleshooting device issues. This will provide a more realistic evaluation of the agents' capabilities in practical use cases.

Including User Preferences and Personalization: Incorporate tasks that involve personalization and user preferences, such as customizing themes, organizing apps, or setting up personalized shortcuts. This will test the agents' adaptability to individual user needs and preferences.

By expanding the benchmark to include these diverse and complex tasks, researchers can evaluate the agents' performance in a more realistic and challenging environment, leading to more robust and effective mobile device control agents.

How can the potential privacy and security concerns in developing mobile device control agents be addressed?

Developing mobile device control agents raises significant privacy and security concerns, as these agents interact with sensitive user data and have the potential to access and manipulate personal information. To address these concerns, the following measures can be implemented:

Data Encryption and Secure Communication: Ensure that all data exchanged between the agent and the device is encrypted to prevent unauthorized access. Implement secure communication protocols to protect sensitive information during interactions.

User Consent and Permissions: Obtain explicit consent from users before granting the agent access to device functionalities and data. Clearly communicate the scope of access and permissions required by the agent and allow users to customize and revoke permissions as needed.

Anonymization and Data Minimization: Minimize the collection and storage of user data to only what is necessary for the agent's functionality. Implement anonymization techniques to protect user identities and sensitive information.

Regular Security Audits and Updates: Conduct regular security audits to identify and address potential vulnerabilities in the agent's code and infrastructure. Keep the agent's software and security protocols up to date to mitigate risks of cyber threats and attacks.

Transparent Privacy Policies: Provide users with transparent privacy policies that clearly outline how their data is collected, used, and protected by the agent. Enable users to access and manage their data privacy settings easily.

By implementing these privacy and security measures, developers can build trust with users and ensure that mobile device control agents operate in a secure and privacy-conscious manner.

How can the insights from this work on mobile device control agents be applied to other domains, such as smart home or industrial automation, to enhance human-computer interaction?

The insights gained from the research on mobile device control agents can be applied to other domains, such as smart home or industrial automation, to enhance human-computer interaction in the following ways:

Adaptability and Generalization: The research on generalization abilities of mobile device control agents can be leveraged in smart home systems to ensure that agents can operate across diverse device configurations and environments. This adaptability is crucial for seamless interaction in complex smart home setups.

Multi-Modal Interaction: Techniques used for multi-modal interaction in mobile device control agents, such as combining text and visual inputs, can be applied in smart home systems to enable more intuitive and natural interactions with users. This can enhance user experience and efficiency in controlling smart home devices.

Privacy and Security: Insights on addressing privacy and security concerns in developing mobile device control agents can be extended to smart home systems to ensure the protection of sensitive user data and secure communication between devices. This is essential for maintaining user trust and data security in smart home environments.

Task Complexity and Real-World Scenarios: Designing tasks that reflect real-world scenarios and involve complex interactions can enhance human-computer interaction in smart home and industrial automation settings. Agents that can handle multi-step tasks, time-sensitive actions, and personalized preferences will provide more effective and user-friendly interactions.

By applying these insights to other domains, developers can create intelligent agents that improve human-computer interaction, streamline tasks, and enhance user satisfaction in various contexts beyond mobile devices.