159 papers indexed here
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The increasing deployment of Artificial Intelligence (AI) systems in critical applications necessitates methods for understanding and trusting their decisions. Traditional explainable AI (XAI) techniques often struggle to capture the complex, dynamic relationships within systems, particularly those evolving over time....
The increasing reliance on Artificial Intelligence (AI) in hardware design presents both significant opportunities and challenges. While AI algorithms can accelerate the design process and optimize performance, their "black box" nature raises concerns regarding trust, accountability, and potential biases. This paper ar...
The increasing deployment of neural networks in critical applications necessitates methods for understanding their decision-making processes – a field often referred to as Explainable AI (XAI). Traditional XAI techniques frequently provide post-hoc explanations that lack a solid causal foundation, potentially masking s...
The increasing deployment of neural networks in critical applications necessitates a deeper understanding of their decision-making processes – a field commonly addressed through explainability methods. However, current explainability techniques often rely on approximations and heuristics, lacking rigorous guarantees ab...
This paper proposes a novel Neural Symbolic Reasoning Engine (NSRE) designed to bridge the gap between the pattern recognition capabilities of neural networks and the logical reasoning capabilities of symbolic systems. The core claim is that combining these two approaches yields a system with enhanced knowledge represe...
Reinforcement learning (RL) has achieved remarkable success in various domains, from game playing to robotics. However, the "black box" nature of many RL agents presents a significant challenge, particularly in applications where transparency and trust are paramount. This work proposes a novel approach to explainable A...
This paper proposes a novel approach to artificial intelligence by integrating hyperdimensional computing (HDC) with neural symbolic computation. The core idea is to leverage the strengths of both paradigms: HDC's efficiency in high-dimensional pattern recognition and neural networks' ability to generate and manipulate...
The increasing deployment of machine learning (ML) models in critical applications raises significant concerns regarding transparency and trustworthiness. Many ML models, particularly deep neural networks, operate as "black boxes," making it difficult to understand their internal decision-making processes. This paper p...
This paper proposes a novel approach to artificial intelligence combining the strengths of neural networks and symbolic reasoning through dynamic rule extraction. The core idea is to leverage neural networks for pattern recognition and expression generation, followed by a dynamic rule extraction module that iteratively...
Automated theorem proving (ATP) systems have achieved remarkable success in verifying complex mathematical theorems. However, their opacity – the lack of human-understandable explanations for their reasoning – remains a significant barrier to widespread adoption. This research proposes a novel approach to enhance the e...
This paper presents a novel approach to neuro-symbolic reasoning by representing symbolic knowledge as continuous tensor networks. Traditional neuro-symbolic systems often struggle with the disjointed nature of neural network pattern recognition and symbolic logic inference. Our method overcomes this challenge by allow...
This paper proposes a novel approach to explainable artificial intelligence (XAI) that leverages causal modeling to enhance the transparency and trustworthiness of AI systems. Current XAI techniques often prioritize interpretability without considering the underlying causal relationships governing the data. We introduc...
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