120 research outputs found
Abduction-Based Explanations for Machine Learning Models
The growing range of applications of Machine Learning (ML) in a multitude of
settings motivates the ability of computing small explanations for predictions
made. Small explanations are generally accepted as easier for human decision
makers to understand. Most earlier work on computing explanations is based on
heuristic approaches, providing no guarantees of quality, in terms of how close
such solutions are from cardinality- or subset-minimal explanations. This paper
develops a constraint-agnostic solution for computing explanations for any ML
model. The proposed solution exploits abductive reasoning, and imposes the
requirement that the ML model can be represented as sets of constraints using
some target constraint reasoning system for which the decision problem can be
answered with some oracle. The experimental results, obtained on well-known
datasets, validate the scalability of the proposed approach as well as the
quality of the computed solutions
Extracting Boolean rules from CA patterns
A multiobjective genetic algorithm (GA) is introduced to identify both the neighborhood and the rule set in the form of a parsimonious Boolean expression for both one- and two-dimensional cellular automata (CA). Simulation results illustrate that the new algorithm performs well even when the patterns are corrupted by static and dynamic nois
Three Modern Roles for Logic in AI
We consider three modern roles for logic in artificial intelligence, which
are based on the theory of tractable Boolean circuits: (1) logic as a basis for
computation, (2) logic for learning from a combination of data and knowledge,
and (3) logic for reasoning about the behavior of machine learning systems.Comment: To be published in PODS 202
Logic-Based Explainability in Machine Learning
The last decade witnessed an ever-increasing stream of successes in Machine
Learning (ML). These successes offer clear evidence that ML is bound to become
pervasive in a wide range of practical uses, including many that directly
affect humans. Unfortunately, the operation of the most successful ML models is
incomprehensible for human decision makers. As a result, the use of ML models,
especially in high-risk and safety-critical settings is not without concern. In
recent years, there have been efforts on devising approaches for explaining ML
models. Most of these efforts have focused on so-called model-agnostic
approaches. However, all model-agnostic and related approaches offer no
guarantees of rigor, hence being referred to as non-formal. For example, such
non-formal explanations can be consistent with different predictions, which
renders them useless in practice. This paper overviews the ongoing research
efforts on computing rigorous model-based explanations of ML models; these
being referred to as formal explanations. These efforts encompass a variety of
topics, that include the actual definitions of explanations, the
characterization of the complexity of computing explanations, the currently
best logical encodings for reasoning about different ML models, and also how to
make explanations interpretable for human decision makers, among others
A Survey of Methods for Converting Unstructured Data to CSG Models
The goal of this document is to survey existing methods for recovering CSG
representations from unstructured data such as 3D point-clouds or polygon
meshes. We review and discuss related topics such as the segmentation and
fitting of the input data. We cover techniques from solid modeling and CAD for
polyhedron to CSG and B-rep to CSG conversion. We look at approaches coming
from program synthesis, evolutionary techniques (such as genetic programming or
genetic algorithm), and deep learning methods. Finally, we conclude with a
discussion of techniques for the generation of computer programs representing
solids (not just CSG models) and higher-level representations (such as, for
example, the ones based on sketch and extrusion or feature based operations).Comment: 29 page
Analysis of non-coherent fault trees using ternary decision diagrams
Risk and safety assessments performed on potentially hazardous industrial systems commonly utilise Fault Tree Analysis (FTA) to forecast the probability of system failure. The type of logic for the top event is usually limited to AND and OR gates which leads to a coherent fault tree structure. In non-coherent fault trees components’ working states as well as components’ failures contribute to the failure of the system. The qualitative and quantitative analyses of non-coherent fault trees can introduce further difficulties over and above those seen in the coherent case. It is shown that the Binary Decision Diagram (BDD) method can be used for this type of assessment. The BDD approach can improve the accuracy and efficiency of the quantitative analysis of non-coherent fault trees. This article demonstrates the value of the Ternary Decision Diagram method (TDD) for the qualitative analysis of non-coherent fault trees. Such analysis can be used to provide information to a decision making process for future actions of an autonomous system and therefore it must be performed in real time. In these circumstances fast processing and small storage requirements are very important. The TDD method provides a fast processing capability and small storage is achieved when a single structure is used for both qualitative and quantitative analyses. The efficiency of the TDD method is discussed and compared to the performance of the established methods for analysis of non-coherent fault trees
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