Random Logic Programs: Linear Model
This work addresses statistical modeling for random logic programs, which is incremental as it builds on existing methods for generating and analyzing such programs.
The paper tackles the problem of randomly generating logic programs with low rule density by proposing a linear model, and shows mathematically that the average number of answer sets converges to a constant as atoms increase, with experiments indicating normal distribution of answer set sizes.
This paper proposes a model, the linear model, for randomly generating logic programs with low density of rules and investigates statistical properties of such random logic programs. It is mathematically shown that the average number of answer sets for a random program converges to a constant when the number of atoms approaches infinity. Several experimental results are also reported, which justify the suitability of the linear model. It is also experimentally shown that, under this model, the size distribution of answer sets for random programs tends to a normal distribution when the number of atoms is sufficiently large.