KRR

A presentation on my research about Lifted Reasoning for solving Combinatorics Math Word Problems.

We show that ProbLog is an instance of a form of Probabilistic Abstract Argumentation (PAA) that builds upon Assumption-Based Argumentation (ABA). The connections pave the way towards equipping ProbLog with alternative semantics, inherited from PAA and ABA.

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.

A presentation on my research about Lifted Reasoning for solving combinatorics math word problems.

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 analyze different neural models to solve probability math word problems in two ways. First, to predict directly the answer in an end-to-end fashion. Second, to map the text to a formal representation used by a probabilistic programming system to compute the answer.

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.

Answer Set Programming (ASP) is a logic programming language widely used in non-monotonic automated reasoning. Thanks to its …