Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach

• URL: http://arxiv.org/abs/2411.08463v1
• Date: Wed, 13 Nov 2024 09:33:33 GMT
• Title: Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach
• Authors: Fadi Al Machot, Martin Thomas Horsch, Habib Ullah,
• Abstract summary: We encode domain-specific constraints, rules, and logical reasoning directly into the model's learning process. The proposed approach is flexible and applicable to both regression and classification tasks. The design allows for the automation of the loss function by simply updating the ASP rules.
• Score: 0.7420433640907689
• License:
• Abstract: This paper presents a hybrid methodology that enhances the training process of deep learning (DL) models by embedding domain expert knowledge using ontologies and answer set programming (ASP). By integrating these symbolic AI methods, we encode domain-specific constraints, rules, and logical reasoning directly into the model's learning process, thereby improving both performance and trustworthiness. The proposed approach is flexible and applicable to both regression and classification tasks, demonstrating generalizability across various fields such as healthcare, autonomous systems, engineering, and battery manufacturing applications. Unlike other state-of-the-art methods, the strength of our approach lies in its scalability across different domains. The design allows for the automation of the loss function by simply updating the ASP rules, making the system highly scalable and user-friendly. This facilitates seamless adaptation to new domains without significant redesign, offering a practical solution for integrating expert knowledge into DL models in industrial settings such as battery manufacturing.

Related papers

• Adaptive Meta-Domain Transfer Learning (AMDTL): A Novel Approach for Knowledge Transfer in AI [0.0]
  AMDTL aims to address the main challenges of transfer learning, such as domain misalignment, negative transfer, and catastrophic forgetting. The framework integrates a meta-learner trained on a diverse distribution of tasks, adversarial training techniques for aligning domain feature distributions, and dynamic feature regulation mechanisms. Experimental results on benchmark datasets demonstrate that AMDTL outperforms existing transfer learning methodologies in terms of accuracy, adaptation efficiency, and robustness.
  arXiv  Detail & Related papers  (2024-09-10T18:11:48Z)
• Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Models [79.28821338925947]
  Domain-Class Incremental Learning is a realistic but challenging continual learning scenario. To handle these diverse tasks, pre-trained Vision-Language Models (VLMs) are introduced for their strong generalizability. This incurs a new problem: the knowledge encoded in the pre-trained VLMs may be disturbed when adapting to new tasks, compromising their inherent zero-shot ability. Existing methods tackle it by tuning VLMs with knowledge distillation on extra datasets, which demands heavy overhead. We propose the Distribution-aware Interference-free Knowledge Integration (DIKI) framework, retaining pre-trained knowledge of
  arXiv  Detail & Related papers  (2024-07-07T12:19:37Z)
• Semantic Objective Functions: A distribution-aware method for adding logical constraints in deep learning [4.854297874710511]
  Constrained Learning and Knowledge Distillation techniques have shown promising results. We propose a loss-based method that embeds knowledge-enforces logical constraints into a machine learning model. We evaluate our method on a variety of learning tasks, including classification tasks with logic constraints.
  arXiv  Detail & Related papers  (2024-05-03T19:21:47Z)
• Solution-oriented Agent-based Models Generation with Verifier-assisted Iterative In-context Learning [10.67134969207797]
  Agent-based models (ABMs) stand as an essential paradigm for proposing and validating hypothetical solutions or policies. Large language models (LLMs) encapsulating cross-domain knowledge and programming proficiency could potentially alleviate the difficulty of this process. We present SAGE, a general solution-oriented ABM generation framework designed for automatic modeling and generating solutions for targeted problems.
  arXiv  Detail & Related papers  (2024-02-04T07:59:06Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation [62.738582127114704]
  Adar is a framework for understanding and improving the generalization of both imitation-learning-based (IL-based) and reinforcement-learning-based solvers (RL-based)
  arXiv  Detail & Related papers  (2023-10-22T03:15:36Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)
• Learning Off-Policy with Online Planning [18.63424441772675]
  We investigate a novel instantiation of H-step lookahead with a learned model and a terminal value function. We show the flexibility of LOOP to incorporate safety constraints during deployment with a set of navigation environments.
  arXiv  Detail & Related papers  (2020-08-23T16:18:44Z)
• Delta Schema Network in Model-based Reinforcement Learning [125.99533416395765]
  This work is devoted to unresolved problems of Artificial General Intelligence - the inefficiency of transfer learning. We are expanding the schema networks method which allows to extract the logical relationships between objects and actions from the environment data. We present algorithms for training a Delta Network (DSN), predicting future states of the environment and planning actions that will lead to positive reward.
  arXiv  Detail & Related papers  (2020-06-17T15:58:25Z)
• Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning [75.56839075060819]
  Traditional robotic approaches rely on an accurate model of the environment, a detailed description of how to perform the task, and a robust perception system to keep track of the current state. reinforcement learning approaches can operate directly from raw sensory inputs with only a reward signal to describe the task, but are extremely sample-inefficient and brittle. In this work, we combine the strengths of model-based methods with the flexibility of learning-based methods to obtain a general method that is able to overcome inaccuracies in the robotics perception/actuation pipeline.
  arXiv  Detail & Related papers  (2020-05-21T19:47:05Z)

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.