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On building meaning: a biologically-inspired experiment on symbol-based communication
Angelo Loula1, Ricardo Gudwin, Sidarta Ribeiro
1Department of Exact Sciences, State University of Feira de Santana, Feira de Santana, Brazil. angelocl@ecomp.uefs.br
Advances in Experimental Medicine and Biology
|December 19, 2009
Summary
This study demonstrates how synthetic experiments with artificial creatures can reveal the emergence of self-organized symbolic communication. Applying Peircean philosophy and neuroethology constraints, researchers observed evolved referential alarm calls.
Area of Science:
- Artificial Life
- Computational Ethology
- Philosophy of Sign
Background:
- Understanding natural phenomena requires well-constrained synthetic experiments.
- Synthetic ethology provides a framework for studying animal behavior in artificial environments.
- The evolution of communication, particularly symbolic communication, is a complex phenomenon.
Purpose of the Study:
- To describe a general approach for conducting synthetic ethological experiments.
- To computationally model the emergence of self-organized symbols in artificial creatures.
- To investigate the role of theoretical and empirical constraints in shaping communication.
Main Methods:
- Developed a computational experiment simulating a community of artificial creatures.
- Applied theoretical constraints from Peircean philosophy of sign.
- Incorporated empirical constraints from neuroethology to design artificial creatures and environmental elements.
Main Results:
- Simulated complex intra- and inter-specific interactions among artificial creatures.
- Observed the evolution of meaning from random alarm calls to optimal referential alarm calls.
- Demonstrated that the chosen constraints were necessary and sufficient for producing symbolic communication.
Conclusions:
- Synthetic experiments guided by appropriate constraints enhance the understanding of natural phenomena.
- The study successfully modeled the emergence of self-organized symbolic communication.
- Peircean and neuroethological constraints are effective in simulating the evolution of meaning in artificial systems.
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