Techniques for Measuring the Inferential Strength of Forgetting Policies

• URL: http://arxiv.org/abs/2404.02454v2
• Date: Sun, 7 Apr 2024 19:47:33 GMT
• Title: Techniques for Measuring the Inferential Strength of Forgetting Policies
• Authors: Patrick Doherty, Andrzej Szalas,
• Abstract summary: This paper defines loss functions for measuring changes in inferential strength based on intuitions from model counting and probability theory. Although the focus is on forgetting, the results are much more general and should have wider application to other areas.
• Score: 0.3069335774032178
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The technique of forgetting in knowledge representation has been shown to be a powerful and useful knowledge engineering tool with widespread application. Yet, very little research has been done on how different policies of forgetting, or use of different forgetting operators, affects the inferential strength of the original theory. The goal of this paper is to define loss functions for measuring changes in inferential strength based on intuitions from model counting and probability theory. Properties of such loss measures are studied and a pragmatic knowledge engineering tool is proposed for computing loss measures using Problog. The paper includes a working methodology for studying and determining the strength of different forgetting policies, in addition to concrete examples showing how to apply the theoretical results using Problog. Although the focus is on forgetting, the results are much more general and should have wider application to other areas.

Related papers

• Teaching Software Metrology: The Science of Measurement for Software Engineering [10.23712090082156]
  This chapter reviews key concepts in the science of measurement and applies them to software engineering research. A series of exercises for applying important measurement concepts to the reader's research are included.
  arXiv  Detail & Related papers  (2024-06-20T16:57:23Z)
• Reduced-Rank Multi-objective Policy Learning and Optimization [57.978477569678844]
  In practice, causal researchers do not have a single outcome in mind a priori. In government-assisted social benefit programs, policymakers collect many outcomes to understand the multidimensional nature of poverty. We present a data-driven dimensionality-reduction methodology for multiple outcomes in the context of optimal policy learning.
  arXiv  Detail & Related papers  (2024-04-29T08:16:30Z)
• Understanding What Affects the Generalization Gap in Visual Reinforcement Learning: Theory and Empirical Evidence [53.51724434972605]
  This paper theoretically answers the key factors that contribute to the generalization gap when the testing environment has distractors. Our theories indicate that minimizing the representation distance between training and testing environments, which aligns with human intuition, is the most critical for the benefit of reducing the generalization gap.
  arXiv  Detail & Related papers  (2024-02-05T03:27:52Z)
• Towards Theoretical Understanding of Data-Driven Policy Refinement [0.0]
  This paper presents an approach for data-driven policy refinement in reinforcement learning, specifically designed for safety-critical applications. Our principal contribution lies in the mathematical formulation of this data-driven policy refinement concept. We present a series of theorems elucidating key theoretical properties of our approach, including convergence, robustness bounds, generalization error, and resilience to model mismatch.
  arXiv  Detail & Related papers  (2023-05-11T13:36:21Z)
• Lexicographic Multi-Objective Reinforcement Learning [65.90380946224869]
  We present a family of both action-value and policy gradient algorithms that can be used to solve such problems. We show how our algorithms can be used to impose safety constraints on the behaviour of an agent, and compare their performance in this context with that of other constrained reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2022-12-28T10:22:36Z)
• Individual Explanations in Machine Learning Models: A Case Study on Poverty Estimation [63.18666008322476]
  Machine learning methods are being increasingly applied in sensitive societal contexts. The present case study has two main objectives. First, to expose these challenges and how they affect the use of relevant and novel explanations methods. And second, to present a set of strategies that mitigate such challenges, as faced when implementing explanation methods in a relevant application domain.
  arXiv  Detail & Related papers  (2021-04-09T01:54:58Z)
• Metrics and continuity in reinforcement learning [34.10996560464196]
  We introduce a unified formalism for defining topologies through the lens of metrics. We establish a hierarchy amongst these metrics and demonstrate their theoretical implications on the Markov Decision Process. We complement our theoretical results with empirical evaluations showcasing the differences between the metrics considered.
  arXiv  Detail & Related papers  (2021-02-02T14:30:41Z)
• Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directions [6.68777133358979]
  We develop a taxonomy of explainability use-cases within public policy problems. We define the end-users of explanations and the specific goals explainability has to fulfill. We map existing work to these use-cases, identify gaps, and propose research directions to fill those gaps.
  arXiv  Detail & Related papers  (2020-10-27T15:37:00Z)
• Marginal likelihood computation for model selection and hypothesis testing: an extensive review [66.37504201165159]
  This article provides a comprehensive study of the state-of-the-art of the topic. We highlight limitations, benefits, connections and differences among the different techniques. Problems and possible solutions with the use of improper priors are also described.
  arXiv  Detail & Related papers  (2020-05-17T18:31:58Z)
• A Survey on Causal Inference [64.45536158710014]
  Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy and economics. Various causal effect estimation methods for observational data have sprung up.
  arXiv  Detail & Related papers  (2020-02-05T21:35:29Z)

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.