An MML-based tool for evaluating the complexity of (stochastic) logic theories

Theory evaluation is a key problem in many areas: machine learning, scientific discovery, inverse engineering, decision making, software engineering, design, human sciences, etc. If we have a set of theories that are able to explain the same set of phenomena, we need a criterion to choose which one is best. There are, of course, many possible criteria. Model simplicity is one of the most common criteria in theory evaluation. The Minimum Message Length (MML) is a solid approach to evaluate theories relative to a given evidence or data. Theories can be expressed in specific or general (Turing-complete) languages. First-order logic, and logic programming in particular, is a Turingcomplete language. Evaluating the simplicity of a theory or program described in a Turing-complete language is much more difficult than just counting the number of lines or bits. It is, in fact, the problem of calculating its Kolmogorov complexity, which is uncomputable. Few works in the literature have been able to present accurate and effective approximations for a Turing-complete language. In this work, we present the first general MML coding scheme for logic programs. With this scheme, we can quantify the bits of information required to code (or send) a theory, a set of data or the same data given the theory. Moreover, we extend the expressiveness of the language to stochastic logic programs, which are not only able to model the truth value of any set of phenomena, but also their probability. As a result, we extend the coding scheme to stochastic logic programs. This opens up the applicability of model selection to many different problems which have a stochastic or probabilistic character, such as games, social phenomena, language processing, Markov processes, etc. As a realization of the above-mentioned schemes, we present a software tool which is able to code and evaluate a set of alternative (stochastic) theories (programs) against a set of examples. We illustrate the application of the tool to a variety of non-probabilistic and probabilistic scenarios.