Where is Knowledge in Robotics? Some Methodological Issues on Symbolic and Connectionist Perspectives of AI

  • J. Mira
  • A. E. Delgado
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 116)


In this chapter we consider a number of methodological issues to which little importance is normally attributed in robotics but which we consider essential to the development of integrated methods of soft and hard Computing and to the understanding of the artificial intelligence (AI) purpose and fundamentals.

The basic conjecture in this chapter is that knowledge always remains at the knowledge level and in the external observer’s domain. To the robot only pass the formai model underlying these models of knowledge. Consequently, there are neither essential differences between symbolic and connectionist techniques nor between soft and hard Computing. They are different inferences and problem-solving-methods (PSMs) that belong to a library and that are selected to be used in a sequential or concurrent manner according to the suitability for decomposing the task under consideration, until we arrive to the level of inferential primitives solved in terms of data and relations specifie of the application domain.

The distinctive characteristics of hard and soft Computing methods are related to the balance between knowledge and data available in advance, the granularity of the model or the necessity and capacity of learning in real time. Nevertheless, in ail these cases the knowledge (the meaning of the entities and relations of the model) always is outside the robot, at the knowledge level, the “house” of models.

In many publications, the robotic programs are described without including âny distinction between levels and domains of description of a calculus. As a resuit, it is generally difficult to determine what the robot actually performs, which knowledge has been represented, and which is artificially injected during the human interpretation of the robots behavior.

In order to make clear this methodological issues we consider the taxonomy of levels introduced by Marr [1] and Newell [2] (Knowledge, Symbols, and Hardware) put on the top of the two domains of description of a calculus (the domain proper of each level and that of the observer external to the computation of the level). Then, we describe the usual approach to modeling and reduction of models from the knowledge to the symbol level and finally we illustrate the analogies and differences between different models and reduction processes including the opera-tional stage, either symbolic, connectionist, probabilistic or fuzzy. In ail the cases our conviction is that most of the work must be made by modeling tasks and PSMs at the knowledge level, where it is crystal clear that soft and hard Computing are complementary and ready to be integrated.


Natural Language Knowledge Level Physical Level External Observer Automaton Theory 
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 2003

Authors and Affiliations

  • J. Mira
    • 1
  • A. E. Delgado
    • 1
  1. 1.Dpto. de Inteligencia Artificial, Facultad de Ciencias y ETSI InformáticaUNEDSpain

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