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U­nifying Logical and Statistical AI with Markov Logic


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shapes and orbital paths, illustration

Credit: Guzel Khuzhina

 

For many years, the two dominant paradigms in artificial intelligence (AI) have been logical AI and statistical AI. Logical AI uses first-order logic and related representations to capture complex relationships and knowledge about the world. However, logic-based approaches are often too brittle to handle the uncertainty and noise present in many applications. Statistical AI uses probabilistic representations such as probabilistic graphical models to capture uncertainty. However, graphical models only represent distributions over propositional universes and must be customized to handle relational domains. As a result, expressing complex concepts and relationships in graphical models is often difficult and labor-intensive.

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To handle the complexity and uncertainty present in most real-world problems, we need AI that is both logical and statistical, integrating first-order logic and graphical models. One or the other by itself cannot provide the minimum functionality needed to support the full range of AI applications. Further, the two need to be fully integrated, and are not simply provided alongside each other. Most applications require simultaneously the expressiveness of first-order logic and the robustness of probability, not just one or the other. Unfortunately, the split between logical and statistical AI runs very deep. It dates to the earliest days of the field, and continues to be highly visible today. It takes a different form in each subfield of AI, but it is omnipresent. Table 1 shows examples of this. In each case, both the logical and the statistical approaches contribute something important. This justifies the abundant research on each of them, but also implies that ultimately a combination of the two is required.


 

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