Software Policy Network Selection for UI Task Automation
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Solution Overview
Problem
Existing client devices require multiple user inputs and resource consumption to perform repetitive tasks, especially for users with limited dexterity, due to the lack of efficient automation in selecting and controlling software clients.
Innovation Solution
Implementing a holistic reinforcement learning framework with a multi-agent system that selects appropriate software policy networks and generates sequences of actions to automatically complete tasks, reducing the need for user input and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated assistant functionality is extended to variety of software applications, then task automation capability is improved, but device complexity increases
Solution Approach 1:
The system is divided into multiple independent software policy networks, each specialized for controlling a specific software application. The holistic policy network coordinates these specialized networks, allowing automation across multiple applications without requiring a single monolithic complex system. This segmentation enables modular scalability where each software client can be independently trained and maintained.
Solution Approach 2:
The holistic policy network serves as a universal coordinator that can select and switch between different software policy networks based on the user's intent. This multi-functional architecture allows a single system to automate tasks across diverse software applications (music player, restaurant ordering, lighting control) while maintaining a unified control framework.
2Ease of operation
If user inputs are reduced for users with limited dexterity, then ease of operation is improved, but automation complexity increases
Solution Approach 1:
The software policy networks are trained using reinforcement learning to autonomously determine the sequence of actions needed to complete tasks. Once trained, these networks self-service by automatically executing tasks based on user intent without requiring manual intervention or complex real-time decision-making logic, thereby reducing operational complexity during actual use.
Solution Approach 2:
The system performs preliminary training actions during the reinforcement learning phase, where software policy networks learn optimal action sequences through simulated interactions with software applications. This preliminary learning phase prepares the networks to automatically execute tasks with minimal runtime complexity, trading offline training complexity for online operational simplicity.
3Productivity
If reinforcement learning framework is implemented to select software policy networks, then productivity is improved, but use of energy increases
Solution Approach 1:
Computational workload is segmented across multiple specialized software policy networks, each optimized for specific applications. The holistic policy network makes lightweight selection decisions by routing to pre-trained specialized networks, distributing computational energy consumption efficiently rather than requiring one network to handle all possible tasks.
Solution Approach 2:
The system dynamically changes operational parameters by selecting different software policy networks based on the current task requirements. This allows the system to optimize resource usage by activating only the necessary specialized networks for each task, rather than maintaining constant high-level processing for all potential applications.
Data Source
AI summary
Techniques are disclosed that enable automating user interface input by generating a sequence of actions to perform a task utilizing a multi-agent reinforcement learning framework. Various implementations process an intent associated with received user interface input using a holistic reinforcement policy network to select a software reinforcement learning policy network. The sequence of actions can be generated by processing the intent, as well as a sequence of software client state data, using the selected software reinforcement learning policy network. The sequence of actions are utilized to control the software client corresponding to the selected software reinforcement learning policy network.