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The main reason why model-based reasoning is researched since the 1990s is to create different layers for modeling and control of a system. This allows to solve more complex tasks and existing programs can be reused for different problems. The model layer is used to monitor a system and to evaluate
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as well are controlled by software. The software is implemented as a normal computer program which consists of if-then-statements, for-loops and subroutines. The task for the programmer is to find an algorithm which is able to control the robot, so that it can do a task. In the history of robotics
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There are many other forms of models that may be used. Models might be quantitative (for instance, based on mathematical equations) or qualitative (for instance, based on cause/effect models.) They may include representation of uncertainty. They might represent behavior over time. They might
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of the physical world. With this approach, the main focus of application development is developing the model. Then at run time, an "engine" combines this model knowledge with observed data to derive conclusions such as a diagnosis or a prediction.
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representative a reactive architecture can overcome the issue. Such a system doesn't need a symbolic model but the actions are connected direct to sensor signals which are grounded in reality.
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represent "normal" behavior, or might only represent abnormal behavior, as in the case of the examples above. Model types and usage for model-based reasoning are discussed in.
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as a logical formalization for describing a system. From a more practical perspective, a declarative model means, that the system is simulated with a
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as input value and determines the output signal. Sometimes, a game engine is described as a prediction engine for simulating the world.
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have argued, that symbolic models are separated from underlying physical systems and they fail to control robots. According to
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if the actions are correct, while the control layer determines the actions and brings the system into a goal state.
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like Prolog and Golog. From a mathematical point of view, a declarative model has much in common with the
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Niederlinski, A (2001). "An expert system shell for uncertain rule-and model based reasoning".
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In 1990, criticism was formulated on model-based reasoning. Pioneers of
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Representing actions and state constraints in model-based diagnosis
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Watson, David P and
Scheidt, David H (2005). "Autonomous systems".
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Model-based programming using golog and the situation calculus
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Model Based
Reasoning for Fault Detection and Diagnosis
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Brooks, Rodney A (1990). "Elephants don't play chess".
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