Abstract Interpretation for Generalized Heuristic Search in Model-Based Planning

• URL: http://arxiv.org/abs/2208.02938v1
• Date: Fri, 5 Aug 2022 00:22:11 GMT
• Title: Abstract Interpretation for Generalized Heuristic Search in Model-Based Planning
• Authors: Tan Zhi-Xuan, Joshua B. Tenenbaum, Vikash K. Mansinghka
• Abstract summary: Domain-general model-based planners often derive their generality by constructing searchs through the relaxation of symbolic world models. We illustrate how abstract interpretation can serve as a unifying framework for these abstractions, extending the reach of search to richer world models. Theses can also be integrated with learning, allowing agents to jumpstart planning in novel world models via abstraction-derived information.
• Score: 50.96320003643406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain-general model-based planners often derive their generality by constructing search heuristics through the relaxation or abstraction of symbolic world models. We illustrate how abstract interpretation can serve as a unifying framework for these abstraction-based heuristics, extending the reach of heuristic search to richer world models that make use of more complex datatypes and functions (e.g. sets, geometry), and even models with uncertainty and probabilistic effects. These heuristics can also be integrated with learning, allowing agents to jumpstart planning in novel world models via abstraction-derived information that is later refined by experience. This suggests that abstract interpretation can play a key role in building universal reasoning systems.

Related papers

• VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning [86.59849798539312]
  We present Neuro-Symbolic Predicates, a first-order abstraction language that combines the strengths of symbolic and neural knowledge representations. We show that our approach offers better sample complexity, stronger out-of-distribution generalization, and improved interpretability.
  arXiv  Detail & Related papers  (2024-10-30T16:11:05Z)
• Learning Discrete Concepts in Latent Hierarchical Models [73.01229236386148]
  Learning concepts from natural high-dimensional data holds potential in building human-aligned and interpretable machine learning models. We formalize concepts as discrete latent causal variables that are related via a hierarchical causal model. We substantiate our theoretical claims with synthetic data experiments.
  arXiv  Detail & Related papers  (2024-06-01T18:01:03Z)
• Neural Causal Abstractions [63.21695740637627]
  We develop a new family of causal abstractions by clustering variables and their domains. We show that such abstractions are learnable in practical settings through Neural Causal Models. Our experiments support the theory and illustrate how to scale causal inferences to high-dimensional settings involving image data.
  arXiv  Detail & Related papers  (2024-01-05T02:00:27Z)
• Emergence and Function of Abstract Representations in Self-Supervised Transformers [0.0]
  We study the inner workings of small-scale transformers trained to reconstruct partially masked visual scenes. We show that the network develops intermediate abstract representations, or abstractions, that encode all semantic features of the dataset. Using precise manipulation experiments, we demonstrate that abstractions are central to the network's decision-making process.
  arXiv  Detail & Related papers  (2023-12-08T20:47:15Z)
• A Recursive Bateson-Inspired Model for the Generation of Semantic Formal Concepts from Spatial Sensory Data [77.34726150561087]
  This paper presents a new symbolic-only method for the generation of hierarchical concept structures from complex sensory data. The approach is based on Bateson's notion of difference as the key to the genesis of an idea or a concept. The model is able to produce fairly rich yet human-readable conceptual representations without training.
  arXiv  Detail & Related papers  (2023-07-16T15:59:13Z)
• Systematic Visual Reasoning through Object-Centric Relational Abstraction [5.914610036560008]
  We introduce OCRA, a model that extracts explicit representations of both objects and abstract relations. It achieves strong systematic generalizations in tasks involving complex visual displays.
  arXiv  Detail & Related papers  (2023-06-04T22:47:17Z)
• Does Deep Learning Learn to Abstract? A Systematic Probing Framework [69.2366890742283]
  Abstraction is a desirable capability for deep learning models, which means to induce abstract concepts from concrete instances and flexibly apply them beyond the learning context. We introduce a systematic probing framework to explore the abstraction capability of deep learning models from a transferability perspective.
  arXiv  Detail & Related papers  (2023-02-23T12:50:02Z)
• FACT: Learning Governing Abstractions Behind Integer Sequences [7.895232155155041]
  We introduce a novel view on the learning of concepts admitting complete finitary descriptions. We lay down a set of benchmarking tasks aimed at conceptual understanding by machine learning models. To further aid research in knowledge representation and reasoning, we present FACT, the Finitary Abstraction Toolkit.
  arXiv  Detail & Related papers  (2022-09-20T08:20:03Z)
• A Self-Supervised Framework for Function Learning and Extrapolation [1.9374999427973014]
  We present a framework for how a learner may acquire representations that support generalization. We show the resulting representations outperform those from other models for unsupervised time series learning.
  arXiv  Detail & Related papers  (2021-06-14T12:41:03Z)

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.