Comparing Multiagent Dynamic and Urgent Task Sharing Methods when the Cost of Unexecuted Task Is High

Abstract

During the COVID-19 pandemic, many hospitals faced a surge in patient numbers, leading to fatalities dueto delayed treatment, intensifying the importance of exploring dynamic task sharing. Using the pandemic as an illustration,the task involves treating patients and utilizing limited resources like beds and medical staff. It is crucialto commence this task within a specified timeframe to avoid adverse outcomes. The scheduling of tasks in advanceproves impossible due to the dynamic and asynchronous nature of task creation. The uneven distribution ofresources and tasks among agents underscores the essential need for transferring tasks from busy to less occupiedagents to maintain a well-balanced workload. However, decentralized patient reallocation is deemed necessary, giventhe independent operation of each hospital, emphasizing the pivotal role of negotiation and consensus among agentsin facilitating successful task transfers. In this context, we systematically developed, expanded, and evaluated sixgeneral-purpose decentralized algorithms tailored for emergency task share negotiation. The primary goal was tominimize the number of tasks not initiated within a specified time limit, particularly when the cost of non-executionwas significant. Our findings identified the CSRN (Continuous or Single Random Negotiation) algorithm as the mosteffective, characterized by its consistent negotiation with the same agent that had previously accepted a task transferbefore engaging with a randomly selected agent. Moreover, our observations indicated that negotiating with numerousagents for individual task transfers proved less effective overall. This distinctive comparative study not only providesvaluable insights for optimizing the sharing of patient treatment tasks but also holds relevance for analogous scenarioscharacterized by elevated costs associated with unexecuted tasks. In general, our study provides practical suggestionsfor negotiating strategies related to task transfers among autonomous agents in emergency scenarios characterized bydynamic and unevenly distributed workloads.