A New Biological Inspired Resource Allocation Algorithm for Distributed Multi Agent Systems with Limited Knowledge
Abstract
Resource allocation in scenarios involving uncertainty, limited information, and potential threats can pose significant challenges and difficulties. Swarm intelligence, characterized by the cooperative dynamics of individuals, could enhance community resilience. This is particularly true for scenarios that involve the allocation of finite resources. This study examines a new biologically inspired distributed resource allocation algorithm, drawing inspiration from snapping shrimp colonies. Operating within environments with uncertainty and resource limitations, snapping shrimp colonies exhibit distributed resource allocation when they allocate a limited number of defenders to protect the nest. The hypothesis of the paper is if inspiration is drawn from the snapping shrimp colonies, then the distributed resource allocation can be improved. The result is a new Snapping Shrimp Resource Allocation Algorithm (SSRAA). The proposed algorithm is assessed on an Agent-Based Model of a wildfire response. Wildfire response requires distributed agents to make resource allocation decisions with limited situational knowledge. The proposed algorithm outperforms a current wildfire response strategy for 86% of the scenarios examined, saving an average of an additional 3% of the forest from burning. This is especially promising because the proposed algorithm does not require global knowledge, central coordination, or special technology to implement. Our goal is that future use of this algorithm will result in a long-term, smarter, and more sustainable world by providing an approach for distributed resource allocation for agents with limited knowledge.
A New Biological Inspired Resource Allocation Algorithm for Distributed Multi Agent Systems with Limited Knowledge
Resource allocation in scenarios involving uncertainty, limited information, and potential threats can pose significant challenges and difficulties. Swarm intelligence, characterized by the cooperative dynamics of individuals, could enhance community resilience. This is particularly true for scenarios that involve the allocation of finite resources. This study examines a new biologically inspired distributed resource allocation algorithm, drawing inspiration from snapping shrimp colonies. Operating within environments with uncertainty and resource limitations, snapping shrimp colonies exhibit distributed resource allocation when they allocate a limited number of defenders to protect the nest. The hypothesis of the paper is if inspiration is drawn from the snapping shrimp colonies, then the distributed resource allocation can be improved. The result is a new Snapping Shrimp Resource Allocation Algorithm (SSRAA). The proposed algorithm is assessed on an Agent-Based Model of a wildfire response. Wildfire response requires distributed agents to make resource allocation decisions with limited situational knowledge. The proposed algorithm outperforms a current wildfire response strategy for 86% of the scenarios examined, saving an average of an additional 3% of the forest from burning. This is especially promising because the proposed algorithm does not require global knowledge, central coordination, or special technology to implement. Our goal is that future use of this algorithm will result in a long-term, smarter, and more sustainable world by providing an approach for distributed resource allocation for agents with limited knowledge.