Multi-Agent Task Control Using Request-Response Importance
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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, leading to inefficiencies in task completion when multiple agents need to cooperate.
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 efficient task execution even in unknown environments by adjusting the number of agents involved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple agents operate in cooperation 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 control apparatus implements feedback mechanisms where agents continuously report their task completion status and the system dynamically adjusts the number of agents based on observed progress. The calculation unit receives feedback from agents about their current task state and uses this information to determine whether additional agents are needed, creating a closed-loop control system that adapts to unknown environment requirements.
Solution Approach 2:
The system transitions from a static agent allocation model to a dynamic one where the number of agents participating in a task can change over time. The control apparatus allows agents to join or leave tasks based on real-time conditions, enabling the system to adapt the agent count dynamically rather than requiring predetermined fixed allocation.
2Reliability
If the number of agents is increased to improve task completion reliability, then task achievement possibility increases, but agent overload and inefficiency may occur
Solution Approach 1:
The control apparatus applies local quality by assigning different levels of agent participation to different tasks based on their specific requirements. Instead of uniformly allocating all agents to all tasks, the system evaluates each task's characteristics and determines the optimal number of agents needed for that specific task, ensuring that resources are concentrated where they are most needed without overloading the system.
Solution Approach 2:
The system changes the parameter of agent allocation from a fixed value to a variable that is continuously adjusted based on task progress and environmental conditions. The control apparatus monitors task state parameters and uses these to dynamically modify the number of agents assigned, allowing the system to optimize between reliability and efficiency by adjusting agent count as a variable parameter rather than a constant.
3Adaptability or versatility
If agents autonomously determine task importance without centralized control, then system flexibility increases, but coordinated task selection becomes difficult
Solution Approach 1:
The control apparatus acts as an intermediary between autonomous agents and the task environment. It receives information from agents about their capabilities and current state, calculates task importance based on this information, and provides coordinated task assignments back to agents. This intermediary role enables autonomous agents to work together effectively without direct peer-to-peer coordination complexity.
Data Source
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AI summary
A request response processing unit (130) 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 (140) 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 (150) selects the task to be performed by the agent according to the importance. A task execution unit (160) controls the agent so that it performs the selected task.