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Towards Practical Normative Agents: A Framework and an Implementation for Norm-Aware Planning

  • Sofia Panagiotidi
  • Javier Vázquez-Salceda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7254)

Abstract

Nowadays there is an important increase in the adoption and use of distributed computational solutions which are growing both in size (from tens to hundreds or even thousands of components, computational entities or actors) and in complexity (from closed, static, pre-defined interactions to more open, dynamic ones stablished at run-time). In this scenario one way to tame such complexity is to add a social layer on top regulating or shaping the behaviour of the actors in the system. One of those social abstractions that has been explored in literature is the use of computational models of (social or organisational) norms. Most of these approaches see norms as a way to specify acceptable agent behaviour in some (distributed) context. In literature there is a lot of work on norm theories, models and specifications on how agents might take norms into account when reasoning but few practical implementations. In this paper we present a first step into the implementation of practical normative agents by describing a framework and an implementation of norm-oriented planning. In this framework norms can be either obligations or prohibitions which can be violated, and are accompanied by repair norms in case they are breached. Unlike most frameworks, our approach takes into consideration the operationalisation of norms during the plan generation phase. Norm operational semantics is expressed as an extension/on top of STRIPS semantics, acting as a form of temporal restrictions over the trajectories (plans) computed by the planner. In combination with the agent’s utility functions over the actions, the norm-aware planner computes the most profitable trajectory concluding to a state of the world where no pending obligations exist and any (obligation/prohibition) violation has been handled. An implementation of the framework in PDDL is described.

Keywords

Linear Temporal Logic Normative Reasoning Norm Violation Linear Temporal Logic Formula Hierarchical Task Network 
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 2012

Authors and Affiliations

  • Sofia Panagiotidi
    • 1
  • Javier Vázquez-Salceda
    • 1
  1. 1.Knowledge Engineering and Machine Learning GroupUniversitat Politècnica de Catalunya - BarcelonaTECH, Campus Nord UPC, Edifici K2MBarcelonaSpain

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