Modular Nonmonotonic Logic Programming Revisited

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

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33013052 ◽

2019 ◽

Vol 33 ◽

pp. 3052-3059 ◽

Cited By ~ 3

Author(s):

Farhad Shakerin ◽

Gopal Gupta

Keyword(s):

Logic Programming ◽

Inductive Logic Programming ◽

State Of The Art ◽

Inductive Logic ◽

Nonmonotonic Logic ◽

Global Behavior ◽

Logic Programs ◽

Tree Models ◽

Boosted Tree ◽

Classification Evaluation

We present a heuristic based algorithm to induce 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 nonmonotonic 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.

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Inductive equivalence in clausal logic and nonmonotonic logic programming

Machine Learning ◽

10.1007/s10994-010-5189-4 ◽

2010 ◽

Vol 83 (1) ◽

pp. 1-29

Author(s):

Chiaki Sakama ◽

Katsumi Inoue

Keyword(s):

Logic Programming ◽

Nonmonotonic Logic

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Preferences and Nonmonotonic Reasoning

AI Magazine ◽

10.1609/aimag.v29i4.2179 ◽

2008 ◽

Vol 29 (4) ◽

pp. 69 ◽

Cited By ~ 24

Author(s):

Gerhard Brewka ◽

Ilkka Niemela ◽

Miroslaw Truszczynski

Keyword(s):

Logic Programming ◽

Nonmonotonic Reasoning ◽

Nonmonotonic Logic ◽

Preference Information ◽

The World ◽

Extended Logic Programming ◽

Answer Set Semantics ◽

Nonmonotonic Logics ◽

Answer Set

We give an overview of the multifaceted relationship between nonmonotonic logics and preferences. We discuss how the nonmonotonicity of reasoning itself is closely tied to preferences reasoners have on models of the world or, as we often say here, possible belief sets. Selecting extended logic programming with the answer-set semantics as a "generic" nonmonotonic logic, we show how that logic defines preferred belief sets and how preferred belief sets allow us to represent and interpret normative statements. Conflicts among program rules (more generally, defaults) give rise to alternative preferred belief sets. We discuss how such conflicts can be resolved based on implicit specificity or on explicit rankings of defaults. Finally, we comment on formalisms which explicitly represent preferences on properties of belief sets. Such formalisms either build preference information directly into rules and modify the semantics of the logic appropriately, or specify preferences on belief sets independently of the mechanism to define them.

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HOW TO COMBINE NONMONOTONIC LOGIC AND RAPID PROTOTYPING TO HELP MAINTAIN SOFTWARE

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s021819409500006x ◽

1995 ◽

Vol 05 (01) ◽

pp. 89-118 ◽

Cited By ~ 10

Author(s):

LUQI ◽

DANIEL E. COOKE

Keyword(s):

Rapid Prototyping ◽

Logic Programming ◽

Software Maintenance ◽

Nonmonotonic Logic ◽

Research Progress ◽

Software Systems ◽

Practical Implementation ◽

Simple Extension ◽

Logic Programs ◽

Software Environment

This paper explores the possibility of automated support for detecting inconsistencies in software systems and requirements. The inconsistencies are introduced when the environment of the software system changes. We refer to the software environment as its context. We review the recent research progress on nonmonotonic logics, pointing out the significance of these results to software maintenance. We explain how a practical implementation of such logics can be obtained via a simple extension to logic programming in the form of an answer procedure that realizes the Extended Logic Semantics [7] for nonmonotonic logic programs that have a unique answer set (which is a large and useful class of logic programs). We augment the existing automated capabilities of the Computer-Aided Prototyping System (CAPS) for rapid prototyping via the extension to logic programming to provide an improved automated capability for detecting certain kinds of inconsistencies created by implicit requirements changes. We illustrate the significance of this capability via an example prototype for a problem originally suggested by Lehman.

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