Multi-Agent Dispatch via Dual Cost Function Optimization
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Solution Overview
Problem
Traditional methods for dispatching agents to tasks are sub-optimal and limited by the number of agents and tasks, leading to inefficient assignment and clustering in multi-agent systems.
Innovation Solution
A computer-implemented method that uses a primary and secondary cost function to optimize agent-task assignments through a series of searches and graph representations, ensuring that agents are dispatched to tasks in a Pareto optimal manner, regardless of the number of agents and tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional heuristic and metaheuristic techniques are used for agent-task assignment, then the method is simpler to implement, but the solution quality deteriorates to sub-optimal results
Solution Approach 1:
The patent replaces traditional heuristic and metaheuristic algorithms with a formal optimization approach using cost functions and graph-based search methods. This substitution transforms the assignment problem from an approximate mechanical process into a systematic optimization process that guarantees optimal solutions through mathematical rigor rather than iterative approximation.
Solution Approach 2:
The patent introduces multiple cost function parameters (primary cost function for task completion quality, secondary cost function for load balancing) that can be adjusted to optimize different aspects of the assignment problem. By changing these parameters, the system can adapt to different optimization goals while maintaining mathematical optimality, resolving the contradiction between implementation simplicity and solution quality.
2Device complexity
If traditional methods are used for agent-task assignment, then the computational complexity is lower, but the applicability deteriorates due to restrictions on the number of agents and tasks
Solution Approach 1:
The patent employs dynamic programming and graph-based search algorithms that adapt to the size of the agent-task problem instance. The computational complexity scales gracefully with the number of agents and tasks through efficient state-space exploration and pruning techniques, allowing the method to handle large-scale problems that traditional fixed-complexity algorithms cannot solve.
Solution Approach 2:
The patent segments the complex assignment problem into manageable components through graph representation, where agents and tasks are nodes and assignments are edges. This segmentation allows the optimization algorithm to process the problem in discrete steps through systematic search and exploration, making large-scale problems tractable while maintaining optimality.
3Manufacturing precision
If exact optimization methods are used for agent-task assignment, then the solution quality improves to optimal results, but the computational time increases
Solution Approach 1:
The patent performs preliminary actions by generating an initial feasible assignment before applying optimization searches. This preliminary assignment provides a baseline solution and pruning criterion for the subsequent optimal search, reducing the effective search space and computational time required to reach the optimal solution while maintaining solution quality.
Solution Approach 2:
The patent uses feedback from cost function evaluations to guide the search process. By continuously evaluating the primary and secondary cost functions during the search and using these evaluations to prune suboptimal paths, the algorithm efficiently converges to the optimal solution without exhaustively exploring all possible assignments, thereby reducing computational time.
4Adaptability or versatility
If the number of agents and tasks increases, then the system handles more complex scenarios, but traditional methods deteriorate in finding exact solutions
Solution Approach 1:
The patent employs dynamic optimization algorithms that adapt their search strategies based on the size of the agent-task instance. The graph-based search and pruning techniques dynamically adjust to handle larger problem sizes while maintaining exact solution capability, unlike traditional methods that fail to find exact solutions as problem size increases.
Solution Approach 2:
The patent transforms the agent-task assignment problem into a graph theoretical framework, adding a structural dimension to the problem representation. This dimensional transformation allows the application of graph algorithms that can efficiently handle large-scale assignments while guaranteeing optimality, overcoming the limitations of traditional flat-space optimization methods.
Data Source
AI summary
A computer-implemented method for dispatching a plurality of agents to a plurality of tasks, the method comprising: identifying, at least one agent-task pair from a plurality of possible agent-task pairs based on a primary cost function of a first assignment of agents to tasks to be used to generate a trimmed plurality of possible agent-task pairs; generating a second assignment of agents to tasks with the primary cost function that is at least as optimal as the primary cost function of the first assignment; generating a third assignment of agents with a secondary cost function that is at least as optimal as the secondary cost function of the second assignment, such that the primary cost function of the third assignment is at least as optimal as the primary cost function of the second assignment; and dispatching the plurality of agents to the plurality of tasks based on the third assignment.