Abstract
In the multi-agent environment, a human expert engages inthe allocation of objectives among individual agents. How-ever, autonomous agents need to determine and allocate ob-jectives without external intervention from humans. There-fore, in this research, we attempt to solve initial goal distri-bution challenges in multi-agent settings by developing twogoal allocation algorithms. The primary objectives are to findcost-effective goal solution sets and distribute them evenlyamong available agents. We introduce two algorithms whenthe goals are structured in a hierarchical goal tree structure,and then test their efficiency across a variety of baseline al-location methods. Both algorithms were able to increase theperformance of agents in multi-agent settings by finding themost optimal distributions of goals and allowing agents to actindependently from human intervention.
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