Size and Logic

We show how to develop a multitude of rules of nonmonotonic logic from very simple and natural notions of size, using them as building blocks

Choosing Your Nonmonotonic Logic: A Shopper’s Guide

The paper presents an exhaustive menu of nonmonotonic logics. The options are individuated in terms of the principles they reject. I locate, e.g., cumulative logics and relevance logics on this menu. I highlight some frequently neglected options, and I argue that these neglected options are particularly attractive for inferentialists

Promoting Modular Nonmonotonic Logic Programs

Modularity in Logic Programming has gained much attention over the past years. To date, many formalisms have been proposed that feature various aspects of modularity. In this paper, we present our current work on Modular Nonmonotonic Logic Programs (MLPs), which are logic programs under answer set semantics with modules that have contextualized input provided by other modules. Moreover, they allow for (mutually) recursive module calls. We pinpoint issues that are present in such cyclic module systems and highlight how MLPs addresses them

David Makinson, "Bridges from Classical to Nonmonotonic Logic", King’s College Publications, London, 2005

Book Reviews: David Makinson, "Bridges from Classical to Nonmonotonic Logic", King’s College Publications, London, 2005, pp. 216, ISBN 1-904987-00-

Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME

We present a heuristic based algorithm to induce \textit{nonmonotonic} logic programs that will explain the behavior of XGBoost trained classifiers. We use the technique based on the LIME approach to locally select the most important features contributing to the classification decision. Then, in order to explain the model's global behavior, we propose the LIME-FOLD algorithm ---a heuristic-based inductive logic programming (ILP) algorithm capable of learning non-monotonic logic programs---that we apply to a transformed dataset produced by LIME. Our proposed approach is agnostic to the choice of the ILP algorithm. Our experiments with UCI standard benchmarks suggest a significant improvement in terms of classification evaluation metrics. Meanwhile, the number of induced rules dramatically decreases compared to ALEPH, a state-of-the-art ILP system