Event-Driven Agents: Enhanced Perception for Multi-Agent Systems Using Complex Event Processing

Abstract

With the increase of existing sensor devices grows the data volume that is available to software systems to understand the physical world. The use of this sensor data in Multi-Agent Systems (MAS) could allow agents to improve their comprehension of the environment and provide additional information for their decision making. Unfortunately, conventional BDI agents cannot make sense of low-level sensor data directly due to their limited event comprehension capabilities: The agents react to single, isolated events rather than to multiple, related events and therefore are not able to efficiently detect complex higher-level situations from low-level sensor data. In this paper, we present Event-Driven Agents as a novel concept to enhance the perception of conventional BDI agents with Complex Event Processing. Their intended use is in environments in which percepts arrive with high speed and are too low-level to be efficiently interpreted by conventional agents directly. In a case study, we show how Event-Driven Agents can be used to address the bicycle rebalancing problem, which bike sharing systems face in their daily operations. Without an intelligent and timely intervention, bike stations of bike sharing systems tend to become empty or full quickly, which prevents the rental or return at these stations. We demonstrate how Event-Driven Agents, based on live data, can detect situations occurring in the bike sharing system in order to initiate appropriate rebalancing efforts.