A Praxical Solution of the Symbol Grounding Problem
This article is the second step in our research into the Symbol Grounding Problem (SGP). In a previous work, we defined the main condition that must be satisfied by any strategy in order to provide a valid solution to the SGP, namely the zero semantic commitment condition (Z condition). We then showed that all the main strategies proposed so far fail to satisfy the Z condition, although they provide several important lessons to be followed by any new proposal. Here, we develop a new solution of the SGP. It is called praxical in order to stress the key role played by the interactions between the agents and their environment. It is based on a new theory of meaning—Action-based Semantics (AbS)—and on a new kind of artificial agents, called two-machine artificial agents (AM²). Thanks to their architecture, AM2s implement AbS, and this allows them to ground their symbols semantically and to develop some fairly advanced semantic abilities, including the development of semantically grounded communication and the elaboration of representations, while still respecting the Z condition.
KeywordsAction-based semantics Artificial evolution Communication Hebb’s rule Local selection Symbol Grounding Problem Two-machine artificial agents Zero semantic commitment condition
A first draft of this paper was the topic of a seminar given by one of us (Mariarosaria) at the Department of Philosophy at University of Padua and we are grateful to the participants for their helpful discussions. We would also like to thank Massimiliano Carrara, Roberto Cordeschi and Jeff Sanders for their suggestions and comments on several versions of this paper. We are very grateful to the members of our research group on the philosophy of information, the IEG, at the University of Oxford for their useful comments; in particular, we would like to acknowledge the help and the very valuable feedback by Sebastian Sequoiah-Grayson and Matteo Turilli. Filippo Menczer very kindly provided many suggestions about the use of ELSA and Local Selection in solving the Symbol Grounding Problem. None of the people listed above is responsible for any remaining mistake.
- Cointe P. (Ed.) (1999). Meta-Level architectures and reflection, second international conference on reflection. Saint-Malo, France: Springer-Verlag.Google Scholar
- Donahoe J. W., et al. (Eds.) (1997). Neural network models of cognition: Biobehavioral foundations. Amsterdam: Elsevier Science Press.Google Scholar
- Floridi, L., et al. (2004). The method of abstraction. In M. Negrotti (Ed.), Yearbook of the artificial, dedicated to “models in contemporary sciences” (pp. 177–220). Berna: P. Lang.Google Scholar
- Harnad, S. (1990). The symbol grounding problem. Physica, D, 335–346.Google Scholar
- Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. New York: John Wiley & Sons.Google Scholar
- Menczer, F., et al. (2001). Evolving heterogeneous neural agents by local selection. In M. Patel, et al. (Eds.), Advances in the evolutionary synthesis of intelligent agents (pp. 337–366). Cambridge, MA: MIT Press.Google Scholar
- Mosses, P. D. (1992). Action semantics. Cambridge University Press.Google Scholar
- Pinker, S. (1994). The language instinct. New York: William Morrow.Google Scholar
- Smith, J. M., et al. (1999). The origins of life. Oxford University Press.Google Scholar
- Varshavskaya, P. (2002). Behavior-based early language development on a humanoid robot. In C. G. Prince, et al. (Eds.), Second international workshop on epigenetic robotics: Modelling cognitive development in robotic systems (pp. 149–158). Edinburgh, Scotland.Google Scholar