Real/Simulated World Dichotomy
While one of the goals of artificial intelligence is to design an intelligent
agent that is fully functional in our world, the real world poses so many
challenges that it is difficult to address them all in a single system.
For example, agents that operate in the
real world must be able to deal with imperfect sensing, and be able to react quickly to sensor data since the
world is often dynamic and unpredictable. The agent must contend with the difficult issue of motor control which does
not conform well to discrete operator application. Furthermore, the dynamic
nature of the world and imperfect motor control means that an agent that
operates in the real world must be able to constantly
replan .
By designing an agent to operate in a simulated world, the researcher can
focus more on cognitive functions such as planning and learning while carefully controlling
the environment in a scientifically deliberate way. The hope is that research
obtained by experimentation in simplified simulated environments will provide
information about aspects of operation in the real world.
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