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

Cumulative Scoring-Based Induction of Default Theories

Significant research has been conducted in recent years to extend Inductive Logic Programming (ILP) methods to induce a more expressive class of logic programs such as answer set programs. The methods proposed perform an exhaustive search for the correct hypothesis. Thus, they are sound but not scalable to real-life datasets. Lack of scalability and inability to deal with noisy data in real-life datasets restricts their applicability. In contrast, top-down ILP algorithms such as FOIL, can easily guide the search using heuristics and tolerate noise. They also scale up very well, due to the greedy nature of search for best hypothesis. However, in some cases despite having ample positive and negative examples, heuristics fail to direct the search in the correct direction. In this paper, we introduce the FOLD 2.0 algorithm - an enhanced version of our recently developed algorithm called FOLD. Our original FOLD algorithm automates the inductive learning of default theories. The enhancements presented here preserve the greedy nature of hypothesis search during clause specialization. These enhancements also avoid being stuck in local optima - a major pitfall of FOIL-like algorithms. Experiments that we report in this paper, suggest a significant improvement in terms of accuracy and expressiveness of the class of induced hypotheses. To the best of our knowledge, our FOLD 2.0 algorithm is the first heuristic based, scalable, and noise-resilient ILP system to induce answer set programs

Counterfactual Explanation Generation with s(CASP)

Machine learning models that automate decision-making are increasingly being used in consequential areas such as loan approvals, pretrial bail, hiring, and many more. Unfortunately, most of these models are black-boxes, i.e., they are unable to reveal how they reach these prediction decisions. A need for transparency demands justification for such predictions. An affected individual might desire explanations to understand why a decision was made. Ethical and legal considerations may further require informing the individual of changes in the input attribute that could be made to produce a desirable outcome. This paper focuses on the latter problem of automatically generating counterfactual explanations. Our approach utilizes answer set programming and the s(CASP) goal-directed ASP system. Answer Set Programming (ASP) is a well-known knowledge representation and reasoning paradigm. s(CASP) is a goal-directed ASP system that executes answer-set programs top-down without grounding them. The query-driven nature of s(CASP) allows us to provide justifications as proof trees, which makes it possible to analyze the generated counterfactual explanations. We show how counterfactual explanations are computed and justified by imagining multiple possible worlds where some or all factual assumptions are untrue and, more importantly, how we can navigate between these worlds. We also show how our algorithm can be used to find the Craig Interpolant for a class of answer set programs for a failing query.Comment: 18 Page

Knowledge-driven Natural Language Understanding of English Text and its Applications

Understanding the meaning of a text is a fundamental challenge of natural language understanding (NLU) research. An ideal NLU system should process a language in a way that is not exclusive to a single task or a dataset. Keeping this in mind, we have introduced a novel knowledge driven semantic representation approach for English text. By leveraging the VerbNet lexicon, we are able to map syntax tree of the text to its commonsense meaning represented using basic knowledge primitives. The general purpose knowledge represented from our approach can be used to build any reasoning based NLU system that can also provide justification. We applied this approach to construct two NLU applications that we present here: SQuARE (Semantic-based Question Answering and Reasoning Engine) and StaCACK (Stateful Conversational Agent using Commonsense Knowledge). Both these systems work by "truly understanding" the natural language text they process and both provide natural language explanations for their responses while maintaining high accuracy.Comment: Preprint. Accepted by the 35th AAAI Conference (AAAI-21) Main Track