Hybrid Probabilistic Programs: Algorithms and Complexity
This work addresses computational complexity issues in logic programming for AI researchers, but it is incremental as it builds on existing HPP frameworks.
The paper tackles the problem of classifying Hybrid Probabilistic Programs (HPPs) into three classes and develops algorithms for computing ground consequences, entailment, and consistency, providing a fine characterization of polynomial algorithms and intractability for these problems.
Hybrid Probabilistic Programs (HPPs) are logic programs that allow the programmer to explicitly encode his knowledge of the dependencies between events being described in the program. In this paper, we classify HPPs into three classes called HPP_1,HPP_2 and HPP_r,r>= 3. For these classes, we provide three types of results for HPPs. First, we develop algorithms to compute the set of all ground consequences of an HPP. Then we provide algorithms and complexity results for the problems of entailment ("Given an HPP P and a query Q as input, is Q a logical consequence of P?") and consistency ("Given an HPP P as input, is P consistent?"). Our results provide a fine characterization of when polynomial algorithms exist for the above problems, and when these problems become intractable.