Multi-Agent Task Control Using Help Requests in Unknown Environments
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In environments where tasks are unknown, existing technologies face challenges in determining the appropriate number of agents required to achieve task targets efficiently, leading to potential inefficiencies and failures in task completion.
Innovation Solution
A control apparatus and system that calculates request and response parameters based on observation information to determine task importance and select tasks for agents, allowing for dynamic task allocation and cooperation among multiple agents to achieve targets effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple agents are deployed to perform tasks in unknown environments, then the possibility of achieving task targets increases, but it becomes difficult to determine the appropriate number of agents required
Solution Approach 1:
The patent implements dynamic task allocation where agents can request help and other agents respond based on current task importance and agent availability. This dynamic mechanism allows the system to adapt the number of agents working on each task in real-time, resolving the contradiction by making the agent configuration flexible rather than fixed.
Solution Approach 2:
The patent employs feedback mechanisms where agents continuously evaluate task importance, monitor help requests from other agents, and adjust their behavior accordingly. This feedback loop enables the system to self-regulate the number of agents needed for each task, eliminating the need for predetermined agent allocation while maintaining high task achievement probability.
2Ease of operation
If agents operate independently without coordination, then individual agent autonomy is maintained, but task completion efficiency decreases when cooperation is needed
Solution Approach 1:
The patent enables agents to autonomously evaluate their own task importance and independently decide when to request help from other agents. This self-service approach maintains agent autonomy while introducing cooperative mechanisms, as each agent independently makes decisions about when coordination is necessary based on its own assessment of task criticality.
Solution Approach 2:
The patent introduces an implicit coordination mechanism where agents serve as intermediaries by responding to help requests from other agents. This intermediary role allows agents to maintain independence while participating in cooperative task completion, as they voluntarily assist others based on their own judgment of task importance and their current workload.
3Ease of manufacture
If a fixed number of agents are assigned to tasks, then resource allocation is simple, but the system cannot adapt to varying task requirements in unknown environments
Solution Approach 1:
The patent dynamically changes the parameter of agent allocation based on task importance evaluation. Instead of fixed resource allocation, the system adjusts the number of agents working on each task according to real-time assessments of task criticality and agent availability, enabling adaptation to varying requirements while maintaining relatively simple allocation rules based on importance thresholds.
Data Source
AI summary
A request response processing unit calculates, based on observation information about the agent, at least one other agent near the agent, and the task, a request parameter as to whether or not to request help, and a response parameter as to whether or not to respond to a request from the at least one other agent. An importance processing unit performs processing for calculating, based on at least the request parameter of the at least one other agent and the response parameter of the agent, importance of each of the tasks for the agent. A task selection unit selects the task to be performed by the agent according to the importance. A task execution unit controls the agent so that it performs the selected task.