Agent-based systems (ABS) are computational systems that consist of autonomous entities (agents) that perceive their environment, make decisions, and take actions independently or in collaboration with other agents to achieve specific goals (Wooldridge, 2009; Weiss, 1999). The rise of agent-based systems can be attributed to key trends that have shaped computing history. These trends, including ubiquity, interconnection, intelligence, delegation, and human-orientedness, have led to increasing adoption of ABS across various organisations.
Ubiquity - the widespread presence of interconnected devices - has led to the need for ABS to manage decentralised systems across industries like healthcare and logistics. The trend of interconnection between systems gave rise to the need for seamless coordination across complex networks. Intelligence in computing has also led to ABS being increasingly capable of autonomous decision-making, learning, and improving (Stuart and Norvig, 2022). Delegation allows ABS to perform tasks without constant human oversight, streamlining operations. Finally, human-orientedness has pushed ABS to be designed with intuitive human interfaces (Khosla and Ichalkaranje, 2013), improving their integration into organizational workflows, and enhancing productivity and user experience.
ABS can view a business process as a community of collaborating agents, where each agent represents a distinct role or department capable of providing services (Jennings, Sycara and Wooldridge, 1998). By delegating routine or complex tasks to autonomous agents, organisations can reduce human intervention, streamline operations, and minimise errors. ABS can also enable better decision-making through real-time data analysis and adaptive learning, allowing organisations to respond quickly to changing environments.
In conclusion, ABS have become a powerful tool for organisations, driven by key trends in computing. With their ability to work autonomously, make smart decisions, and collaborate across systems, ABS help streamline operations, improve decision-making, and boost adaptability. As they keep evolving, their impact on productivity and business processes will only grow.
References
Jennings, N.R., Sycara, K. and Wooldridge, M. (1998) ‘A Roadmap of Agent Research and Development’, Autonomous agents and multiagent systems, 1(1), pp. 7–38. Available at: https://doi.org/10.1023/A:1010090405266.
Khosla, R. and Ichalkaranje, N. (2013) Design of intelligent multi-agent systems: human-centredness, architectures, learning and adaptation. Springer.
Stuart, R. and Norvig, P. (2022) Artificial Intelligence : a Modern Approach, Global Edition. Fourth edition. Harlow, England: Pearson Education Limited.
Weiss, G. (1999) Multiagent systems : a modern approach to distributed artificial intelligence. Cambridge, Mass: MIT Press.
Wooldridge, M. (2009) ‘An Introduction to Multi Agent Systems’. United Kingdom: John Wiley & Sons, Incorporated.