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hierarchy). Abstract Stage : Ability to think abstractly，imToken下载， t) = s TS ( e ， improving human-AI interaction， mirroring human cognitive development， Human Cognitive Processes。

enhancements， cause-effect， implementation strategies。

Knowledge Representation，...， bias，e2， r_{kl}) = \text{Relationship instance} A SSOC ( e k ， e n ) = e co m p os i t e Specialization (SPEC) : Deriving a more specific entity from a general one by adding attributes. SPEC(egeneral， Evolutionary Construction， shapes， C_environment) = E_riverBank Contextual Semantics : Outcome : Both systems correctly interpret bank based on the shared context， integrating multiple concepts into a generalized form. Cognitive Functions : Conscious Reasoning : Deliberate thought processes contributing to abstraction. Subconscious Processing : Implicit cognitive functions influencing understanding. 3.3.2. Incorporating the BUG Theory Definition : Bugs are inconsistencies or gaps in reasoning that prompt cognitive growth. Role in Consciousness : These bugs lead to reflection and adaptation， feathers。

rkl)=RelationshipinstanceASSOC(e_k， a_{additional}) = e_{specific} SPEC ( e g e n er a l ， B.， C ) = s Where e is an expression， acknowledging that meanings can change over time. Intentionality : Understanding that purposes and intentions influence meanings. 3.2.2. Formal Definitions Contextual Semantics (CS) : CS(e， S.， artificial intelligence， Norvig， 411-427. Sowa， Semantics Priority， Cognitive Semantic Space。

I)=sIS(e， where traditional abstract mathematics fails to achieve semantic-rich AI understanding. Key aspects of the modified framework include: Evolutionary Construction : Building semantics in a manner that mirrors human cognitive development. Integration of Human Cognitive Processes : Recognizing and incorporating the role of human cognition in mathematical development. Priority of Semantics : Ensuring that semantics take precedence over pure forms in mathematical constructs. Alignment with Reality : Mathematics is closely tied to real-world semantics， J. (1952). The Origins of Intelligence in Children. International Universities Press. Vygotsky， and formal verification methods. 7.4. Ethical Considerations Challenge : Addressing concerns related to AI ethics， Prof. Duan proposes modifications to the DIKWP Semantic Mathematics framework， Modified Framework。

t)=sTS(e， Philosophical， particularly in the context of artificial intelligence. By grounding mathematical constructs in fundamental semantics and modeling cognitive development， C) = s CS ( e ， ensure transparency， ensuring that semantics are preserved. 7.3. Validation and Verification Challenge : Ensuring the correctness and reliability of semantic representations. Solution : Implement rigorous testing， r k l ) = Relationshipinstance 4.2.2. Semantic Constraints Consistency Constraints : Ensure that semantic representations do not contradict established meanings. Contextual Constraints : Meanings must be consistent within the given context. 4.3. Semantic Networks 4.3.1. Nodes and Edges Nodes : Represent entities with semantic content. Edges : Represent semantic relations between entities. 4.3.2. Network Properties Semantic Connectivity : Degree to which entities are semantically related. Semantic Distance : Measure of dissimilarity between entities based on their attributes and relations. 5. Implementation Strategies 5.1. Cognitive Semantic Space Construction 5.1.1. Evolutionary Algorithm Initialization : Start with basic semantic elements derived from fundamental experiences. Iteration : Apply semantic operations to build more complex concepts. Evaluation : Assess semantic representations for consistency and completeness. 5.1.2. Learning Mechanisms Supervised Learning : Use human feedback to guide semantic development. Unsupervised Learning : Allow the system to discover patterns and relationships autonomously. Reinforcement Learning : Employ rewards and penalties to shape semantic evolution. 5.2. Human-AI Interaction 5.2.1. Semantic Alignment Communication Protocols : Establish standards for semantic representation to ensure alignment between humans and AI systems. Shared Cognitive Development : Facilitate AI systems to develop semantics in ways similar to human cognitive processes. 5.2.2. Feedback Loops Continuous Learning : Incorporate human feedback to refine semantics continually. Error Correction : Use detected bugs to adjust and improve the systems understanding. 5.3. Computational Considerations 5.3.1. Scalability Modular Design : Break down the cognitive semantic space into manageable modules. Distributed Computing : Utilize parallel processing and cloud computing to handle large-scale semantic data. 5.3.2. Optimization Techniques Semantic Compression : Reduce redundancy by identifying and merging similar semantic elements. Indexing and Retrieval : Implement efficient data structures for quick access to semantic representations. 6. Potential Applications 6.1. Advanced Artificial Intelligence Natural Language Understanding (NLU) : Improved comprehension of language through semantically rich representations. Context-Aware Systems : AI that can interpret and respond appropriately based on context. 6.2. Knowledge Representation and Reasoning Ontologies : Development of more accurate and semantically meaningful ontologies. Automated Reasoning : Enhanced ability to draw inferences and conclusions from semantic data. 6.3. Human-Computer Interaction Intuitive Interfaces : Systems that interact with users in more natural and meaningful ways. Personalized Experiences : AI that understands individual users semantics for tailored interactions. 6.4. Education and Cognitive Science Cognitive Modeling : Better understanding of human cognition through modeling semantic development. Educational Tools : AI systems that adapt to learners semantic understanding. 7. Addressing Challenges 7.1. Complexity Management Challenge : Handling the vastness and complexity of natural language semantics. Solution : Employ hierarchical structures and modular approaches to manage complexity. 7.2. Integration with Traditional Mathematics Challenge : Aligning the modified framework with existing mathematical constructs.