An Organizational Semiotics Perspective for Norm-Based Agent Design

The EDA Model
  • Joaquim Filipe
  • Kecheng Liu
Part of the Multiagent Systems, Artificial Societies, and Simulated Organizations book series (MASA, volume 2)


Semiotics is the formal doctrine of signs. Organizational Semiotics is a particular branch of Semiotics, concerned with understanding organizations as information systems. Rejecting the position of a totally objective reality, we adopt as our philosophical stance a radical relativistic model. In this model both human agents and artificial agents have a constructed knowledge about reality, which requires the agent active participation, and all knowledge is connected to a knowing agent. The EDA (Epistemic-Deontic-Axiological) model, here proposed, enables the representation of agent informational states and simultaneously defines the conceptual communication framework. Agents use their knowledge (epistemic level) and take into account their obligations and authorizations (deontic level), which they may choose to accept or to violate, to decide what to do next, i.e. to define their goals. In the process they use individual preferences defined in their system of values (axiological level). Organizational concepts and activities, such as power relationships, roles, or contracts, are defined by norms in terms of the basic EDA components. Using an EDA model, it is possible to define an explicit representation of the institutional roles the agent can play, where a role is defined as a set of services plus a set of policies. A service is represented by a procedural abstraction, whereas a policy is represented by a deontic statement, either specifying an obligation-to-do or an authorization-to-do. The application of the EDA model has been tested in academic case studies. In another paper also presented at this workshop (Filipe, 2000) we show how some of the ideas presented in this paper can be implemented.


Multiagent System Artificial Agent Deontic Logic Default Theory Behavioral Norm 
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 Science+Business Media New York 2001

Authors and Affiliations

  • Joaquim Filipe
    • 1
  • Kecheng Liu
    • 2
  1. 1.Escola Superior de Tecnologia do Instituto Politécnico de SetúbalSetúbalPortugal
  2. 2.School of ComputingStaffordshire UniversityStaffordUK

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