Action and Agency in Norm-Governed Multi-agent Systems

  • Marek Sergot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4995)


There is growing interest in the idea that, in some cases, interactions among multiple, independently acting agents in a multi-agent system can be regulated and managed by norms (or ‘social laws’) which, if respected, allow the agents to co-exist in a shared environment. We present a formal (modal-logical) language for describing and analysing such systems. We distinguish between system norms, which express a system designer’s view of what system behaviours are deemed to be legal, permitted, desirable, and so on, and agent-specific norms which constrain and guide an individual agent’s behaviours and which are supposed to be incorporated, in one way or another, in the agent’s implementation. The language provides constructs for expressing properties of states and transitions in a transition system, and modalities of the kind found in logics of action/agency for expressing that an agent brings it about that, or is responsible for, its being the case that A. The novel feature is that an agent, or group of agents, brings it about that a transition has a certain property rather than bringing it about that a certain state of affairs obtains, as is usually the case. The aim of the paper is to motivate the technical development and illustrate the use of the formal language by means of a simple example in which there there are both physical and normative constraints on agents’ behaviours. We discuss some relationships between system norms and agent-specific norms, and identify several different categories of non-compliant behaviour that can be expressed and analysed using the formal language. The final part of the paper presents some transcripts of output from a model-checker for the language.


Transition System Individual Norm Multiagent System Male Agent Label Transition System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Marek Sergot
    • 1
  1. 1.Department of ComputingImperial College LondonLondonUK

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