PLP

Declarative Modelling and Reasoning for Combinatorial Problem Solving and Argumentation under Uncertainty
Declarative Modelling and Reasoning for Combinatorial Problem Solving and Argumentation under Uncertainty

The slides for my PhD public defence.

Mar 8, 2023

Understanding ProbLog as Probabilistic Argumentation

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.

Jan 1, 2023

Reasoning on Arguments and Beliefs with Probabilistic Logic Programs
Reasoning on Arguments and Beliefs with Probabilistic Logic Programs

A talk for the XAI seminars by the CLArg Group (Imperial College)

Dec 8, 2022

Stable Model Semantics in ProbLog and its Applications in Argumentation
Stable Model Semantics in ProbLog and its Applications in Argumentation

A presentation on my research about Probabilistic Logic Programming and Argumentation

Feb 21, 2022

Efficient Knowledge Compilation Beyond Weighted Model Counting

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.

Jan 1, 2022

smProbLog: Stable Model Semantics in ProbLog for Probabilistic Argumentation
smProbLog: Stable Model Semantics in ProbLog for Probabilistic Argumentation

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.

Jan 1, 2021

Mapping Probability Word Problems to Executable Representations

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.

Jan 1, 2021