On the impact of fuzzy-logic based BDI agent model for cyber–physical systems

• Published in: Expert systems with applications Vol. 238; p. 122265
• Main Authors: Karaduman, Burak, Tezel, Baris Tekin, Challenger, Moharram
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.03.2024
• Subjects: BDI agent model; Cyber–physical systems; Fuzzy logic; Software agents; Fuzzy logic; Cyber–physical systems; Software agents; BDI agent model
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2023.122265

Cyber–Physical Systems (CPS) interconnect embedded computing technologies into the physical world, forming a complex, multi-disciplinary, physically-effective system. However, interacting with the physical world brings unpredictability, as real-world events are uncertain and dynamic by its nature. Consequently, CPS must be capable of reasoning about these unpredictable situations and adapt their behaviour accordingly. Therefore, it is essential to enhance the intelligence of CPS to overcome this challenge effectively, leading to the development of smart CPS. As an approach, a reasoning mechanism can support the system to reason about its state and actions to handle unpredictable changes. In this study, we employ intelligent Belief-Desire-Intention (BDI) agents to achieve this objective. Nevertheless, traditional logic approaches based on crisp numbers, which are used in typical BDI agents, may not effectively handle uncertainty. Hence, the reasoning mechanism can be extended with fuzzy logic to deal with run-time uncertainties. Thus, the target system’s suitable inputs, outputs and reasoning phases are fuzzified to indicate the impact of the fuzzy logic on the CPS. To this end, an architecture is provided to deploy BDI agents integrated with a fuzzy reasoning model for handling imprecise information and improving process control. The approach is validated and evaluated on a complex case study with a heterogeneous structure controlled by multiple agents, namely the “smart production system”. The multiple modular and end-to-end experiments conducted reveal that, on the whole, the fuzzy BDI agent outperforms the classical one by up to three times, with only requiring approximately 10% more computation time. •Combining fuzzy logic and BDI reasoning agents to mitigate uncertainty in CPS.•Extending the reference architecture proposed for the BDI agents with Fuzzy logic.•Validating distributed fuzzy-BDI agents using a complex and heterogeneous case study.