On the Reliability of AI Planning Software in Real-Time Applications

We define the reliability of a real-time system incorporating AI planning programs as the probability that, for each problem-solving request issued from the environment, the embedded system can successfully plan and execute a response within a specified real-time deadline. A methodology is developed for evaluating the reliability of such systems taking into consideration the fact that, other than program bugs, the intrinsic characteristics of AI planning programs may also cause the embedded system to fail even after all software bugs are removed from the program. The utility of the methodology is demonstrated by applying it to the reliability evaluation of two AI planning algorithms embedded in a real-time multicriteria route finding system. >

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