Abstract
Multiagent application systems must deal with various changes in both the system and the system environment at runtime. Generally, such changes have undesirable negative effects on the system. To manage and control the system, it is important to observe and detect negative effects using an appropriate observation function of the system’s behavior. This paper focuses on the design of this function and proposes a new macroscopic measure with which to observe behavioral characteristics of a runtime multiagent system. The proposed measure is designed as the variance of fluctuation of a macroscopic activity factor of the whole system, based on theoretical analysis of the macroscopic behavioral model of a multiagent system. Experiments are conducted to investigate basic characteristics of the proposed measure, using a test bed system. The results of experiments show that the proposed measure reacts quickly and increases drastically in response to abnormal changes in the system. Hence, the proposed measure is considered a measure that can be used to detect undesirable changes in a multiagent system.
Highlights
A multiagent system (MAS) consists of agents who operate cooperatively as a team and handle problem-solving tasks according to their unique features relating to autonomy, reactivity, and social ability
Focusing on the problem above, this paper proposes a new macroscopic measure defined using the macroscopic activity factor of an MAS according to theoretical analysis of the behavioral model of the MAS
Against the background discussed above, this paper proposes a new macroscopic measure based on both the analysis of the theoretical behavioral model and experiments conducted using the test bed system of the MAS
Summary
A multiagent system (MAS) consists of agents who operate cooperatively as a team and handle problem-solving tasks according to their unique features relating to autonomy, reactivity, and social ability. It is difficult to presume the nondeterministic behavior of an MAS at runtime because the properties and functions of agents are affected directly or indirectly by various changes in the system’s runtime conditions and environment These changes occasionally have undesirable effects and can even cause catastrophic damage to the runtime system. It is necessary to develop effective methods and functions to maintain the system’s behavioral characteristics in response to such unusual situations Studies, such as those on resilient systems, have required a system to be proactive and able to anticipate, monitor and respond to environmental change or unexpected emerging threats. Kaindl et al [4]
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