An agent-based approach for building complex software systems

By Nicholas R. Jennings. CACM, 2001

1,390 citations recorded by the source corpus.

uilding high-quality, industrialstrength software is difficult. Indeed, it has been argued that developing such software in domains like telecommunications, industrial control, and business process management represents one of the most complex construction tasks humans undertake. Against this background, a wide range of software engineering paradigms have been devised. Each successive development either claims to make the engineering process easier or promises to extend the complexity of applications that can feasibly be built. Although evidence is emerging to support these claims, researchers continue to strive for more effective techniques. To this end, this article will argue that analyzing, designing, and implementing complex software systems as a collection of interacting, autonomous agents (that is, as a multiagent system [4]) affords software engineers a number of significant advantages over contemporary methods. This is not to say that agent-oriented software engineering represents a silver bullet [2]—there is no evidence to suggest it will represent an order of magnitude improvement in productivity. However, the increasing number of deployed applications [4, 8] bears testament to the potential advantages that accrue from such an approach. In seeking to demonstrate the efficacy of agent-oriented techniques, the most compelling argument would be to quantitatively show how their adoption improved the development process in a range of projects. However, such data is simply not available (as it is not for approaches like patterns, application frameworks, and componentware). Given this situation, the best that can be achieved is a qualitative justification of why agent-oriented approaches are well suited to engineering complex, distributed software systems.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/035e4a6d0995f494f7e011c487621e6e5974e2a4

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Citations in the corpus

Argumentation-based negotiation (2003, background): “This is because the multi-agent paradigm oﬀers a powerful set of metaphors, concepts and techniques for conceptualising, designing, implementing and verifying complex distributed systems (Jennings, 2001).”
Developing intelligent agent systems - a practical guide (2004, background): “Jennings and Wooldridge (1998b)).”
Multi-Agent Systems (2017, neutral): “multi-agent systems (MAS) in which the individual components act in an autonomous and flexible manner in order to achieve their objectives [16].”
Trust in multi-agent systems (2004, methodology): “Given this , agent based computing has been advocated as the natural computation model for such systems ( Jennings , 2001).”
Agent-based systems for manufacturing (2006, background): “Figure 2: Generic scheme of a multi-agent system [41].”
Agent-Client Interaction in a Web-based E-Commerce System (2002, neutral): “This limits much of the usefulness of agents as it ties them too closely into the architecture; the mobility, autonomy and wide distribution of agents – three of their defining characteristics [14, 16] – are all inhibited by this configuration.”
Middleware for Internet of Things: A Survey (2016, positive): “This survey indicates that there have been significant advances in addressing many challenges for middleware in an IoT environment, with the following open challenges remaining.”
Secure Tropos: a Security-Oriented Extension of the Tropos Methodology (2007, background): “AOSE introduces an alternative approach in analysing and designing complex distributed computerised systems [24, 43, 21], according to which a complex computerised system is viewed as a multiagent system [43], in which a collection of autonomous software agents (subsystems) interact with each other ”
The Semantic Grid: Past, Present, and Future (2005, neutral): “model [17]—agents are producers, consumers, and bro-”
Brain Meets Brawn: Why Grid and Agents Need Each Other (2004, background): “In contrast, those working on agents have focused on “brains,” i.e., on the development of concepts, methodologies, and algorithms for autonomous problem solvers that can act flexibly in uncertain and dynamic environments in order to achieve their aims and objectives [ 21 ].”