Learning to design without prior data: Discovering generalizable design strategies using deep learning and tree search

• URL: http://arxiv.org/abs/2211.15068v1
• Date: Mon, 28 Nov 2022 05:00:58 GMT
• Title: Learning to design without prior data: Discovering generalizable design strategies using deep learning and tree search
• Authors: Ayush Raina, Jonathan Cagan, Christopher McComb
• Abstract summary: Building an AI agent that can design on its own has been a goal since the 1980s. Deep learning has shown the ability to learn from large-scale data, enabling significant advances in data-driven design. This paper presents a framework to self-learn high-performing and generalizable problem-solving behavior in an arbitrary problem space.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Building an AI agent that can design on its own has been a goal since the 1980s. Recently, deep learning has shown the ability to learn from large-scale data, enabling significant advances in data-driven design. However, learning over prior data limits us only to solve problems that have been solved before and biases data-driven learning towards existing solutions. The ultimate goal for a design agent is the ability to learn generalizable design behavior in a problem space without having seen it before. We introduce a self-learning agent framework in this work that achieves this goal. This framework integrates a deep policy network with a novel tree search algorithm, where the tree search explores the problem space, and the deep policy network leverages self-generated experience to guide the search further. This framework first demonstrates an ability to discover high-performing generative strategies without any prior data, and second, it illustrates a zero-shot generalization of generative strategies across various unseen boundary conditions. This work evaluates the effectiveness and versatility of the framework by solving multiple versions of two engineering design problems without retraining. Overall, this paper presents a methodology to self-learn high-performing and generalizable problem-solving behavior in an arbitrary problem space, circumventing the needs for expert data, existing solutions, and problem-specific learning.

Related papers

• Contractual Reinforcement Learning: Pulling Arms with Invisible Hands [68.77645200579181]
  We propose a theoretical framework for aligning economic interests of different stakeholders in the online learning problems through contract design. For the planning problem, we design an efficient dynamic programming algorithm to determine the optimal contracts against the far-sighted agent. For the learning problem, we introduce a generic design of no-regret learning algorithms to untangle the challenges from robust design of contracts to the balance of exploration and exploitation.
  arXiv  Detail & Related papers  (2024-07-01T16:53:00Z)
• Coding for Intelligence from the Perspective of Category [66.14012258680992]
  Coding targets compressing and reconstructing data, and intelligence. Recent trends demonstrate the potential homogeneity of these two fields. We propose a novel problem of Coding for Intelligence from the category theory view.
  arXiv  Detail & Related papers  (2024-07-01T07:05:44Z)
• A Survey on State-of-the-art Deep Learning Applications and Challenges [0.0]
  Building a deep learning model is challenging due to the algorithm's complexity and the dynamic nature of real-world problems. This study aims to comprehensively review the state-of-the-art deep learning models in computer vision, natural language processing, time series analysis and pervasive computing.
  arXiv  Detail & Related papers  (2024-03-26T10:10:53Z)
• When Meta-Learning Meets Online and Continual Learning: A Survey [39.53836535326121]
  meta-learning is a data-driven approach to optimize the learning algorithm. Continual learning and online learning, both of which involve incrementally updating a model with streaming data. This paper organizes various problem settings using consistent terminology and formal descriptions.
  arXiv  Detail & Related papers  (2023-11-09T09:49:50Z)
• A Survey on Knowledge Editing of Neural Networks [43.813073385305806]
  Even the largest artificial neural networks make mistakes, and once-correct predictions can become invalid as the world progresses in time. Knowledge editing is emerging as a novel area of research that aims to enable reliable, data-efficient, and fast changes to a pre-trained target model. We first introduce the problem of editing neural networks, formalize it in a common framework and differentiate it from more notorious branches of research such as continuous learning.
  arXiv  Detail & Related papers  (2023-10-30T16:29:47Z)
• Graph Reinforcement Learning for Network Control via Bi-Level Optimization [37.00510744883984]
  We argue that data-driven strategies can automate this process and learn efficient algorithms without compromising optimality. We present network control problems through the lens of reinforcement learning and propose a graph network-based framework to handle a broad class of problems.
  arXiv  Detail & Related papers  (2023-05-16T03:20:22Z)
• Hierarchically Structured Task-Agnostic Continual Learning [0.0]
  We take a task-agnostic view of continual learning and develop a hierarchical information-theoretic optimality principle. We propose a neural network layer, called the Mixture-of-Variational-Experts layer, that alleviates forgetting by creating a set of information processing paths. Our approach can operate in a task-agnostic way, i.e., it does not require task-specific knowledge, as is the case with many existing continual learning algorithms.
  arXiv  Detail & Related papers  (2022-11-14T19:53:15Z)
• A neural anisotropic view of underspecification in deep learning [60.119023683371736]
  We show that the way neural networks handle the underspecification of problems is highly dependent on the data representation. Our results highlight that understanding the architectural inductive bias in deep learning is fundamental to address the fairness, robustness, and generalization of these systems.
  arXiv  Detail & Related papers  (2021-04-29T14:31:09Z)
• Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges [50.22269760171131]
  The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. This text is concerned with exposing pre-defined regularities through unified geometric principles. It provides a common mathematical framework to study the most successful neural network architectures, such as CNNs, RNNs, GNNs, and Transformers.
  arXiv  Detail & Related papers  (2021-04-27T21:09:51Z)
• A Design Space Study for LISTA and Beyond [79.76740811464597]
  In recent years, great success has been witnessed in building problem-specific deep networks from unrolling iterative algorithms. This paper revisits the role of unrolling as a design approach for deep networks, to what extent its resulting special architecture is superior, and can we find better? Using LISTA for sparse recovery as a representative example, we conduct the first thorough design space study for the unrolled models.
  arXiv  Detail & Related papers  (2021-04-08T23:01:52Z)
• A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions [48.76705090826339]
  Neural Architecture Search (NAS) is a revolutionary algorithm, and the related research work is complicated and rich. We provide a new perspective: beginning with an overview of the characteristics of the earliest NAS algorithms, summarizing the problems in these early NAS algorithms. Besides, we conduct a detailed and comprehensive analysis, comparison, and summary of these works.
  arXiv  Detail & Related papers  (2020-06-01T13:08:03Z)

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.