Multiagent opportunism refers to the ability of agents operating in a multiagent system (MAS) to recognize and respond to potential opportunities for mutual assistance in achieving individual goals. Two major potential obstacles in operationalizing multiagent opportunistic assistance in real-world systems are (i) low amounts of knowledge shared between the agents, and (ii) limited ability of the agents to re-plan dynamically. We have previously shown that even under these limiting conditions, systems of agents can benefit from multiagent opportunism. In this work we discuss how multi-agent systems can exploit shared knowledge for opportunistic predictive encoding using an approach based on an abstract plan representation called partial order plan graphs (POPGs). Further, we present several approaches for increasing system-level performance by improving the efficiency of the plans containing predictively encoded opportunities, as well as the results of an empirical analysis of their impact on the system performance.
['James H. Lawton', 'Carmel Domshlak']
Multi-agent opportunistic planning and plan execution