Prolog-based agnostic explanation module for structured pattern classification

• URL: http://arxiv.org/abs/2112.12641v1
• Date: Thu, 23 Dec 2021 15:28:40 GMT
• Title: Prolog-based agnostic explanation module for structured pattern classification
• Abstract summary: This paper presents a Prolog-based reasoning module to generate counterfactual explanations. It can also resolve what-if queries using the ground-truth labels instead of the predicted ones. Overall, our approach comprises four well-defined stages that can be applied to any structured pattern classification problem.
• Score: 2.0824228840987447
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper presents a Prolog-based reasoning module to generate counterfactual explanations given the predictions computed by a black-box classifier. The proposed symbolic reasoning module can also resolve what-if queries using the ground-truth labels instead of the predicted ones. Overall, our approach comprises four well-defined stages that can be applied to any structured pattern classification problem. Firstly, we pre-process the given dataset by imputing missing values and normalizing the numerical features. Secondly, we transform numerical features into symbolic ones using fuzzy clustering such that extracted fuzzy clusters are mapped to an ordered set of predefined symbols. Thirdly, we encode instances as a Prolog rule using the nominal values, the predefined symbols, the decision classes, and the confidence values. Fourthly, we compute the overall confidence of each Prolog rule using fuzzy-rough set theory to handle the uncertainty caused by transforming numerical quantities into symbols. This step comes with an additional theoretical contribution to a new similarity function to compare the previously defined Prolog rules involving confidence values. Finally, we implement a chatbot as a proxy between human beings and the Prolog-based reasoning module to resolve natural language queries and generate counterfactual explanations. During the numerical simulations using synthetic datasets, we study the performance of our system when using different fuzzy operators and similarity functions. Towards the end, we illustrate how our reasoning module works using different use cases.

Related papers

• Moment-Structured Block Encodings of Periodic Finite-Difference Operators [0.0]
  Block encoding is the standard technique for accessing matrix data in quantum linear-algebra algorithms.<n>Explicit construction of block encodings with provably optimal subnormalization exists for only a handful of operators.<n>We develop a framework to block encode translation-invariant finite-difference operators on a periodic grid.
  arXiv  Detail & Related papers  (2026-07-13T14:17:57Z)
• Probing Structural Mathematical Reasoning in Language Models with Algebraic Trapdoors [0.0]
  We introduce a benchmark suite for evaluating structural mathematical reasoning in language models.<n>Each instance presents a finitely generated subgroup as a list of integer matrices.<n>We report empirical results across five representative reasoning traces from two state-of-the-art models.
  arXiv  Detail & Related papers  (2026-05-05T23:16:30Z)
• Modeling Next-Token Prediction as Left-Nested Intuitionistic Implication [0.0]
  We introduce the emphArrow Language Model, a neural architecture derived from an intuitionistic-logic interpretation of next-token prediction.<n>We show that a neural architecture equivalent to multiplicative RNNs arises naturally from a proof-theoretic interpretation of next-token prediction as nested intuitionistic implication.
  arXiv  Detail & Related papers  (2026-01-07T04:10:26Z)
• Parseval Convolution Operators and Neural Networks [16.78532039510369]
  We first identify the Parseval convolution operators as the class of energy-preserving filterbanks. We then present a constructive approach for the design/specification of such filterbanks via the chaining of elementary Parseval modules. We demonstrate the usage of those tools with the design of a CNN-based algorithm for the iterative reconstruction of biomedical images.
  arXiv  Detail & Related papers  (2024-08-19T13:31:16Z)
• Causal Interpretation of Self-Attention in Pre-Trained Transformers [4.419843514606336]
  We propose a causal interpretation of self-attention in the Transformer neural network architecture. We use self-attention as a mechanism that estimates a structural equation model for a given input sequence of symbols. We demonstrate this method by providing causal explanations for the outcomes of Transformers in two tasks: sentiment classification (NLP) and recommendation.
  arXiv  Detail & Related papers  (2023-10-31T09:27:12Z)
• Tractable Bounding of Counterfactual Queries by Knowledge Compilation [51.47174989680976]
  We discuss the problem of bounding partially identifiable queries, such as counterfactuals, in Pearlian structural causal models. A recently proposed iterated EM scheme yields an inner approximation of those bounds by sampling the initialisation parameters. We show how a single symbolic knowledge compilation allows us to obtain the circuit structure with symbolic parameters to be replaced by their actual values.
  arXiv  Detail & Related papers  (2023-10-05T07:10:40Z)
• Evaluating Step-by-Step Reasoning through Symbolic Verification [20.156768135017007]
  Pre-trained language models (LMs) have shown remarkable reasoning performance for in-context learning. LMLP enjoys more than $25%$ higher accuracy than chain-of-thoughts (CoT) on length generalization benchmarks even with smaller model sizes.
  arXiv  Detail & Related papers  (2022-12-16T19:30:01Z)
• Learning to Select Prototypical Parts for Interpretable Sequential Data Modeling [7.376829794171344]
  We propose a Self-Explaining Selective Model (SESM) that uses a linear combination of prototypical concepts to explain its own predictions. For better interpretability, we design multiple constraints including diversity, stability, and locality as training objectives.
  arXiv  Detail & Related papers  (2022-12-07T01:42:47Z)
• Hierarchical Phrase-based Sequence-to-Sequence Learning [94.10257313923478]
  We describe a neural transducer that maintains the flexibility of standard sequence-to-sequence (seq2seq) models while incorporating hierarchical phrases as a source of inductive bias during training and as explicit constraints during inference. Our approach trains two models: a discriminative derivation based on a bracketing grammar whose tree hierarchically aligns source and target phrases, and a neural seq2seq model that learns to translate the aligned phrases one-by-one.
  arXiv  Detail & Related papers  (2022-11-15T05:22:40Z)
• Data-driven Numerical Invariant Synthesis with Automatic Generation of Attributes [0.0]
  We propose a data-driven algorithm for numerical invariant synthesis and verification. The algorithm is based on the ICE-DT schema for learning decision trees from samples of positive and negative states.
  arXiv  Detail & Related papers  (2022-05-30T09:18:37Z)
• Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI [60.142926537264714]
  We introduce the methodology of Faithfulness-through-Counterfactuals. It generates a counterfactual hypothesis based on the logical predicates expressed in the explanation. It then evaluates if the model's prediction on the counterfactual is consistent with that expressed logic.
  arXiv  Detail & Related papers  (2022-05-25T03:40:59Z)
• Structured Reordering for Modeling Latent Alignments in Sequence Transduction [86.94309120789396]
  We present an efficient dynamic programming algorithm performing exact marginal inference of separable permutations. The resulting seq2seq model exhibits better systematic generalization than standard models on synthetic problems and NLP tasks.
  arXiv  Detail & Related papers  (2021-06-06T21:53:54Z)
• Tractable Inference in Credal Sentential Decision Diagrams [116.6516175350871]
  Probabilistic sentential decision diagrams are logic circuits where the inputs of disjunctive gates are annotated by probability values. We develop the credal sentential decision diagrams, a generalisation of their probabilistic counterpart that allows for replacing the local probabilities with credal sets of mass functions. For a first empirical validation, we consider a simple application based on noisy seven-segment display images.
  arXiv  Detail & Related papers  (2020-08-19T16:04:34Z)
• LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network [111.24773949467567]
  We propose LogicalFactChecker, a neural network approach capable of leveraging logical operations for fact checking. It achieves the state-of-the-art performance on TABFACT, a large-scale, benchmark dataset.
  arXiv  Detail & Related papers  (2020-04-28T17:04:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.