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Compiling AI Engineering Models for Probabilistic Inference

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7006)

Abstract

In engineering domains, AI decision making is often confronted with problems that lie at the intersection of logic-based and probabilistic reasoning. A typical example is the plan assessment problem studied in this paper, which comprises the identification of possible faults and the computation of remaining success probabilities based on a system model. In addition, AI solutions to such problems need to be tailored towards the needs of engineers. This is being addressed by the recently developed high-level, expressive modeling formalism called probabilistic hierarchical constraint automata (PHCA).

This work introduces a translation from PHCA models to statistical relational models, which enables a wide array of probabilistic reasoning solutions to be leveraged, e.g., by grounding to problem-specific Bayesian networks. We illustrate this approach for the plan assessment problem, and compare it to an alternative logic-based approach that translates the PHCA models to lower-level logic models and computes solutions by enumerating most likely hypotheses. Experimental results on realistic problem instances demonstrate that the probabilistic reasoning approach is a promising alternative to the logic-based approach.

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  • DOI: 10.1007/978-3-642-24455-1_18
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Maier, P., Jain, D., Sachenbacher, M. (2011). Compiling AI Engineering Models for Probabilistic Inference. In: Bach, J., Edelkamp, S. (eds) KI 2011: Advances in Artificial Intelligence. KI 2011. Lecture Notes in Computer Science(), vol 7006. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24455-1_18

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  • DOI: https://doi.org/10.1007/978-3-642-24455-1_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24454-4

  • Online ISBN: 978-3-642-24455-1

  • eBook Packages: Computer ScienceComputer Science (R0)