Abstract
The ability to understand the world around us is crucial to successfully act within an environment. For such a task it is important to understand relations and context between objects and actions. The goal is to have an agent capable of understanding its environment and by using knowledge of how it works to take actions. The term environment is defined as physical places, virtual place and social situations an agent can encounter. Current agent architectures do not specify how knowledge should be handled from its environment or how to generate knowledge from experiences. This work focuses on using semantic networks to represent information an agent acquires from its environment, allowing communication in a more meaningful way by transferring information from the semantic network. An agent is able to have an abstraction of the environment to the extent that it is capable of interacting within it. Experiments with this method are done using a custom tool called Wiinik, this tool allows creating hierarchical agents using scripts as behavior rules. Semantic networks, although not standardized, have great potential to model any kind of knowledge, regardless of an agent’s capability. In this paper we describe how knowledge modeled as semantic networks is beneficial for agent decisions and facilitates the transfer of information between agents. Additionally, we describe the architecture of a software tool developed for social simulations and we discuss two case studies that were implemented using this software.
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Espinoza-Hernández, I., Flores, DL., Rodríguez-Díaz, A., Castañón-Puga, M., Gaxiola, C. (2010). Agent Communication Using Semantic Networks. In: Castillo, O., Kacprzyk, J., Pedrycz, W. (eds) Soft Computing for Intelligent Control and Mobile Robotics. Studies in Computational Intelligence, vol 318. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15534-5_20
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DOI: https://doi.org/10.1007/978-3-642-15534-5_20
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