# Nomological Deductive Reasoning for Trustworthy, Human-Readable, and Actionable AI Outputs
## 4 Twofold Need for Improvement in XAI Research
### The Need for Enhanced Knowledge Representation  
From cognitive, philosophical, and psychological perspectives, intelligence and knowledge are fundamentally intertwined. Intelligence is broadly defined as the capacity to apply knowledge for problem-solving, adapt to new situations, and understand complex relationships, while knowledge refers to the facts, information, and skills acquired through  
Figure 6: The two facets of XAI challenges in the field. experience or education. It is the dynamic interaction between the two that underpins intelligent behavior. Cognitive science provides a structured view of this relationship, emphasizing that intelligence emerges from both the storage and effective application of structured knowledge. [PERSON]'s cognitive architecture and the ACT-R theory, for instance, illustrate the centrality of schemas and mental representations in enabling intelligent action [111]. Likewise, [PERSON] and [PERSON]'s problem-solving theory posits that intelligent behavior involves navigating a solution space by leveraging prior knowledge [112]. Philosophical discourse reinforces this perspective. [PERSON], in _The Republic_, asserts that knowledge is the foundation of intellectual virtue and a prerequisite for achieving truth and understanding [113]. [PERSON] famous assertion, \"Cogito, ergo sum\" (\"I think, therefore I am\"), underscores the indivisibility of thought and knowledge, suggesting that clear and distinct thinking is contingent upon well-grounded knowledge [114]. Psychological theories echo this view. [PERSON] emphasizes that intelligence develops through the assimilation and accommodation of new knowledge into existing mental schemas [115], while [PERSON] argues that cognitive development--and by extension, intelligence--is socially and culturally mediated through knowledge acquisition and transmission [116]. Building on these foundational insights, the formalization of knowledge for artificial systems began in the 1950s, coinciding with the emergence of early AI. Pioneers such as [PERSON] [117], [PERSON] [118; 119], and [PERSON] [120], drawing inspiration from classical philosophers like [PERSON] and [PERSON] [121], laid the groundwork for what is now recognized as the field of knowledge representation (KR). Contributions from ancient Eastern traditions, including the Nalanda School, also played a critical role in the early development of KR by promoting structured thought as a cognitive tool [122]. Over the past seven decades, KR has matured into a robust scientific discipline --particularly in ontology engineering--featuring a wide array of techniques that model knowledge through symbolic systems, natural language, and imagery to support intelligent and autonomous software systems [123]. However, despite this rich foundation, many contemporary explainable AI (XAI) approaches inadequately integrate core KR principles. This oversight is particularly evident in data-driven methods, where even knowledge-based approaches, which should enhance interpretability, often suffer from misrepresentation. These methods frequently fall short of the established criteria for KR adequacy, resulting in representations that are incomplete, ambiguous, or opaque [124]. To mitigate these issues, hybrid approaches such as neuro-symbolic AI have emerged that combine the learning capacity of neural networks with the interpretability of symbolic reasoning [125]. While promising in theory, these models encounter challenges in aligning sub-symbolic and symbolic components, often leading to inconsistencies. This misalignment can undermine interpretability, scalability, and transparency--especially in complex or high-stakes domains. Similarly, knowledge graphs have gained popularity for encoding semantic relationships and supporting AI decision-making processes [126]. They are particularly effective for modeling static relationships and offer high interpretability.