Neurosymbolic artificial intelligence (NeSy ai) is a rapidly evolving field that seeks to combine the perceptual capabilities of neural networks with the logical reasoning strengths of symbolic systems. This hybrid approach is designed to tackle complex tasks that require both pattern recognition and deductive reasoning. NeSy systems aim to create more robust and generalizable ai models by integrating neural and symbolic components. Despite limited data, these models are better equipped to handle uncertainty, make informed decisions, and function effectively. The field represents a significant advancement in ai, aiming to overcome the limitations of either purely neural or purely symbolic approaches.
One of the main challenges facing the development of NeSy ai is the complexity involved in learning from data when combining neural and symbolic components. In particular, integrating the neural network learning signals with the symbolic logic component is a difficult task. Traditional learning methods in NeSy systems typically rely on exact probabilistic logic inference, which is computationally expensive and needs to be better scaled to more complex or larger systems. This limitation has hampered the widespread application of NeSy systems, as the computational demands make them impractical for many real-world problems where scalability and efficiency are critical.
There are several methods that attempt to address this learning challenge in NeSy systems, each with its limitations. For example, knowledge compilation techniques provide accurate propagation of learning signals, but need better scalability, making them impractical for larger systems. Approximation methods, such as k-best solutions or the A-NeSI framework, offer alternative approaches by simplifying the inference process. However, these methods often introduce bias or require extensive optimization and hyperparameter tuning, resulting in long training times and reduced applicability to complex tasks. Furthermore, these approaches typically need stronger guarantees of the accuracy of their approximations, raising concerns about the reliability of their results.
Researchers at KU Leuven have developed a new method known as EXPLAIN, AGREE, LEARN (EXAL)This method is specifically designed to improve the scalability and efficiency of learning in NeSy systems. The EXAL framework introduces a sampling-based objective that enables more efficient learning while providing strong theoretical guarantees on the approximation error. These guarantees are crucial to ensure that the system's predictions remain reliable even as task complexity increases. By optimizing a surrogate objective that approximates the likelihood of the data, EXAL addresses scalability issues that plague other methods.
The EXAL method involves three key steps:
In the first step, the EXPLAIN algorithm generates samples of possible explanations for the observed data. These explanations represent different logical mappings that could satisfy the requirements of the symbolic component. For example, in a self-driving car scenario, EXPLAIN could generate multiple explanations for why the car should brake, such as detecting a pedestrian or a red light. The second step, AGREE, involves reweighting these explanations based on their likelihood according to the neural network’s predictions. This step ensures that more weight is given to the most plausible explanations, which improves the learning process. Finally, in the LEARN step, these weighted explanations are used to update the neural network parameters through a traditional gradient descent approach. This process allows the network to learn more effectively from the data without requiring exact probabilistic inference.
The performance of the EXAL method has been validated by extensive experiments on two important NeSy tasks:
- Addition of MNIST
- Pathfinding in Warcraft
On the MNIST addition task, which involves adding sequences of digits represented by images, EXAL achieved a test accuracy of 96.40% for two-digit sequences and 93.81% for four-digit sequences. Notably, EXAL outperformed the A-NeSI method, which achieved an accuracy of 95.96% for two-digit and 91.65% for four-digit sequences. EXAL demonstrated superior scalability, maintaining a competitive accuracy of 92.56% for 15-digit sequences, while A-NeSI struggled with a significantly lower accuracy of 73.27%. On the Warcraft pathfinding task, which requires finding the shortest path on a grid, EXAL achieved an impressive accuracy of 98.96% on a 12 × 12 grid and 80.85% on a 30 × 30 grid, significantly outperforming other NeSy methods in terms of accuracy and learning time.
In conclusion, the EXAL method addresses the scalability and efficiency challenges that have limited the application of NeSy systems. By leveraging a sampling-based approach with strong theoretical guarantees, EXAL improves the accuracy and reliability of NeSy models and significantly reduces the time required for training. EXAL is a promising solution for many complex ai tasks, in particular large-scale data and symbolic reasoning. EXAL’s success on tasks such as MNIST summation and Warcraft pathfinding underscores its potential to become a standard approach in the development of next-generation ai systems.
Take a look at the Paper. All credit for this research goes to the researchers of this project. Also, don't forget to follow us on twitter.com/Marktechpost”>twitter and join our Telegram Channel and LinkedIn GrAbove!. If you like our work, you will love our fact sheet..
Don't forget to join our Subreddit with over 48 billion users
Find upcoming ai webinars here
Asif Razzaq is the CEO of Marktechpost Media Inc. As a visionary engineer and entrepreneur, Asif is committed to harnessing the potential of ai for social good. His most recent initiative is the launch of an ai media platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is technically sound and easily understandable to a wide audience. The platform has over 2 million monthly views, illustrating its popularity among the public.
<script async src="//platform.twitter.com/widgets.js” charset=”utf-8″>
