Journal

In this paper we tackle the problem of automating the resolution of combinatorics math word problems. We introduce CoLa, a novel declarative language to express combinatorics math word problems, and CoSo, a solver based on lifted reasoning, which outperforms alternative frameworks.

We introduce second level algebraic model counting (2AMC) problems, a framework generalizing several probabilistic inference task. We present a novel Knowledge Compilation technique to address the increased complexity of a 2AMC task with respect to first-level AMC problems.

We model beliefs in argumentation problems with probabilistic logic programs and show that traditional probabilistic logic programming (PLP) systems cannot reason on this type of programs. We thus present smProblog, a novel PLP system based on ProbLog, where inference and learning over such probabilistic argumentation problems are possible.