Game Development Reference
In-Depth Information
ments it is impossible to enumerate the state and
action spaces. The problem of learning in large
spaces is addressed as generalization techniques
such as Vector Quantization (VQ) [5] or CMAC
(Garcia García, López-Bueno, Fernández and
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Q(s,a) = (1- α) Q(s, a) +α [r + γmax a' A Q(s',a')]
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This paper has introduced SIMBA, a WEB
based business simulator. In this chapter we
have described its model, software architecture
and main functionalities, together with the logic
and methodologies imbedded into it to make it
a responsive, proactive tool capable of respond-
ing to varying teaching demands. The ongoing
research in AI, providing multi-profile learning
agents to be installed within SIMBA make it a
powerful instrument.
SIMBA can be successfully used both for
teaching and research. In teaching, SIMBA pro-
vides several advantages over traditional business
teaching in different pedagogical areas, like the
learning objectives, the development of work
skills, and the teaching function.
From the point of view of research, SIMBA
offers a very successful framework for different
research areas. In this work attention is centered
in Artificial Intelligence, offering some insights
in the application of Data Mining approaches and
Automatic Decision Making using Multi-Agent
Systems. This approach provides an exciting re-
search field both for decision-making strategies
and management styles, and for the development of
efficient agents capable of a behavior resembling
a human, or team, player.
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