Compiling AI engineering models for probabilistic inference

  • Authors:

  • Paul Maier;Dominik Jain;Martin Sachenbacher

  • Affiliations:

  • Department of Informatics, Technische Universität München, Germany;Department of Informatics, Technische Universität München, Germany;Department of Informatics, Technische Universität München, Germany

  • Venue:
  • KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
  • Year:
  • 2011

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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.