Probabilistic logic programming formalisms permit the definition of potentially very complex probability distributions. This complexity can often make learning hard, even when structure is fixed and learning reduces to parameter estimation. In this paper an approximate Bayesian computation (ABC) method is presented which computes approximations to the posterior distribution over PRISM parameters. The key to ABC approaches is that the likelihood function need not be computed, instead a 'distance' between the observed data and synthetic data generated by candidate parameter values is used to drive the learning. This makes ABC highly appropriate for PRISM programs which can have an intractable likelihood function, but from which synthetic data can be readily generated. The algorithm is experimentally shown to work well on an easy problem but further work is required to produce acceptable results on harder ones.
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