Evolving DIKWP Semantics with Wittgenstein's Logical Composition Mechanisms

Yucong Duan

International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation(DIKWP-SC)

World Artificial Consciousness CIC(WAC)

World Conference on Artificial Consciousness(WCAC)

(Email: duanyucong@hotmail.com) 

Abstract

This document presents a simulation of the evolution of semantics for concepts using the core semantics of the Data-Information-Knowledge-Wisdom-Purpose (DIKWP) Semantic Mathematics framework, integrating Wittgenstein's logical composition mechanisms. By avoiding subjective definitions and starting from fundamental principles, we model how an infant might build up understanding from basic sensory data. Through this process, we explicitly evolve the semantics of each concept, demonstrating how higher-level abstractions emerge from core data using logical compositions. This approach maintains a strong connection to real-world semantics and provides a foundation for artificial intelligence systems to develop understanding in a manner analogous to human cognitive development.

Table of Contents

1. Introduction

• 1.1. Overview

• 1.2. Objectives

2. Core Semantics of the DIKWP Framework

• 2.1. Data (D): Sameness

• 2.2. Information (I): Difference

• 2.3. Knowledge (K): Logical Structures

• 2.4. Wisdom (W) and Purpose (P): Emergent Understanding

3. Simulating Concept Evolution from an Infant's Perspective

• 3.1. Initial Sensory Data Acquisition

• 3.2. Formation of Basic Concepts through Sameness and Difference

• 3.3. Development of Complex Concepts via Logical Composition

4. Integrating Wittgenstein's Logical Composition Mechanisms

• 4.1. Elementary Propositions from Sensory Data

• 4.2. Logical Operations and Composition of Propositions

• 4.3. Emergence of Structured Knowledge

5. Evolving Semantics of Concepts Explicitly

• 5.1. Concept of "Object"

• 5.2. Concept of "Action"

• 5.3. Concept of "Cause and Effect"

6. Detailed Simulation Example

• 6.1. Data Acquisition: Visual and Tactile Inputs

• 6.2. Information Processing: Identifying Differences

• 6.3. Knowledge Formation: Recognizing Patterns

• 6.4. Wisdom and Purpose: Goal-Oriented Actions

7. Implications for Artificial Intelligence Systems

• 7.1. Mimicking Human Cognitive Development

• 7.2. Building Grounded Semantics

• 7.3. Enhancing AI Understanding and Interaction

8. Conclusion

9. References

1. Introduction1.1. Overview

Understanding how concepts evolve from basic sensory inputs is essential for developing artificial intelligence (AI) systems that can interact meaningfully with the real world. Traditional approaches often rely on subjective definitions imposed by individuals, which can abstract away real-world semantics. Instead, we aim to simulate the natural evolution of semantics by starting from core principles and modeling how an infant might develop understanding through experiences.

By utilizing the core semantics of the DIKWP Semantic Mathematics framework and integrating Wittgenstein's logical composition mechanisms, we demonstrate how concepts can emerge naturally from data without subjective definitions. This approach ensures that every concept used is explicitly bundled with evolved semantics, maintaining a strong connection to real-world experiences.

1.2. Objectives

• Simulate the evolution of semantics for concepts using core semantics as the foundation.

• Integrate Wittgenstein's logical composition mechanisms to build complex concepts.

• Avoid subjective definitions by explicitly evolving semantics from basic data.

• Model the process from an infant's perspective to reflect natural cognitive development.

• Demonstrate how this approach maintains real-world semantics and can enhance AI systems.

2. Core Semantics of the DIKWP Framework

The DIKWP framework models cognitive processes through five hierarchical levels:

1. Data (D): Raw sensory inputs characterized by sameness.

2. Information (I): Processed data highlighting differences.

3. Knowledge (K): Structured information forming logical relationships.

4. Wisdom (W): Deep understanding and judicious application of knowledge.

5. Purpose (P): Guiding motivations and intentions driving actions.

2.1. Data (D): Sameness

• Definition: Data consists of raw, unprocessed sensory inputs.

• Characteristics: Uniformity and repetition in sensory experiences.

• Example: An infant receives repeated visual inputs of light patterns.

2.2. Information (I): Difference

• Definition: Information arises when differences within data are identified.

• Characteristics: Detection of variations, contrasts, and changes.

• Example: The infant notices differences in brightness or color.

2.3. Knowledge (K): Logical Structures

• Definition: Knowledge emerges from organizing information into logical structures.

• Characteristics: Formation of patterns, categories, and relationships.

• Example: The infant recognizes that certain visual patterns correspond to specific objects.

2.4. Wisdom (W) and Purpose (P): Emergent Understanding

• Definition: Wisdom involves applying knowledge effectively, guided by purpose.

• Characteristics: Goal-oriented actions, problem-solving, and adaptation.

• Example: The infant uses knowledge of objects to reach for a toy.

3. Simulating Concept Evolution from an Infant's Perspective3.1. Initial Sensory Data Acquisition

An infant begins with basic sensory inputs:

• Visual Data: Light intensity, colors, shapes.

• Auditory Data: Sounds, tones, volumes.

• Tactile Data: Textures, temperatures, pressures.

These inputs are raw data without inherent meaning.

3.2. Formation of Basic Concepts through Sameness and Difference

Sameness:

• The infant experiences repeated sensory inputs.

• Example: Seeing a circular shape repeatedly.

Difference:

• The infant detects variations within sensory inputs.

• Example: Noticing that some shapes are circular while others are square.

Through recognizing sameness and difference, the infant begins to form basic concepts.

3.3. Development of Complex Concepts via Logical Composition

Using the recognized differences, the infant composes more complex concepts:

• Association: Linking visual and tactile experiences.

• Pattern Recognition: Identifying sequences or regularities.

• Categorization: Grouping similar sensory inputs.

4. Integrating Wittgenstein's Logical Composition Mechanisms4.1. Elementary Propositions from Sensory Data

Elementary Propositions:

• Represent atomic facts derived from sensory inputs.

• Example: "This shape is round."

Each elementary proposition corresponds to a basic observation.

4.2. Logical Operations and Composition of Propositions

Using logical connectives, the infant combines elementary propositions:

• AND (∧): "This shape is round and red."

• OR (∨): "This object is soft or warm."

• NOT (¬): "This sound is not loud."

Through logical composition, more complex propositions are formed.

4.3. Emergence of Structured Knowledge

By logically combining propositions, the infant builds structured knowledge:

• Cause and Effect: "If I shake this object (action), it makes a noise (effect)."

• Object Permanence: Understanding that objects continue to exist even when not perceived.

This knowledge is grounded in the logical relationships between experiences.

5. Evolving Semantics of Concepts Explicitly5.1. Concept of "Object"

Evolution:

1. Data: Repeated visual and tactile sensations.

2. Information: Recognition of consistent patterns across senses.

3. Knowledge: Logical proposition: "An object is something that occupies space and can be perceived through senses."

4. Semantics: The concept of "object" evolves from the consistent co-occurrence of sensory inputs.

5.2. Concept of "Action"

Evolution:

1. Data: Observations of movements and changes.

2. Information: Identifying that certain movements lead to changes.

3. Knowledge: Logical proposition: "An action is a movement that causes a change."

4. Semantics: The concept of "action" arises from the association between movements and resulting effects.

5.3. Concept of "Cause and Effect"

Evolution:

1. Data: Temporal sequences of events.

2. Information: Noting that certain events consistently follow others.

3. Knowledge: Logical proposition: "If event A occurs, then event B follows."

4. Semantics: The concept of "cause and effect" develops from the logical relationship between sequential events.

6. Detailed Simulation Example6.1. Data Acquisition: Visual and Tactile Inputs

• The infant sees a red ball (visual data) and touches it (tactile data).

• Data Elements: d1d_1d1​: Red color; d2d_2d2​: Round shape; d3d_3d3​: Soft texture.

6.2. Information Processing: Identifying Differences

• Sameness: The ball is always round and red.

• Difference: Other objects may be square or blue.

• Information Elements: i1i_1i1​: Round vs. square; i2i_2i2​: Red vs. blue.

6.3. Knowledge Formation: Recognizing Patterns

• Logical Composition:

• p1p_1p1​: "This object is round and red."

• p2p_2p2​: "This object is soft."

• Pattern Recognition: The infant associates roundness and redness with the ball.

6.4. Wisdom and Purpose: Goal-Oriented Actions

• Purpose: The desire to grasp the ball.

• Action: Reaching out to touch the ball.

• Feedback Loop: Successful grasping reinforces the understanding of cause and effect.

7. Implications for Artificial Intelligence Systems7.1. Mimicking Human Cognitive Development

• AI systems can simulate the process of building concepts from basic data.

• This approach allows AI to develop understanding organically rather than relying on predefined concepts.

7.2. Building Grounded Semantics

• Concepts are directly linked to sensory inputs and experiences.

• This grounding ensures that AI semantics are closely tied to real-world phenomena.

7.3. Enhancing AI Understanding and Interaction

• By evolving semantics explicitly, AI can better interpret and predict real-world events.

• This leads to more effective interactions with the environment and humans.

8. Conclusion

By starting from core semantics and simulating the evolution of concepts through the lens of an infant's experiences, we have demonstrated how complex understanding can emerge without subjective definitions. Integrating Wittgenstein's logical composition mechanisms allows for the structured development of knowledge from basic data. This approach maintains a strong connection to real-world semantics and provides a foundation for AI systems to develop understanding in a manner analogous to human cognitive development.

9. References

1. Wittgenstein, L. (1921). Tractatus Logico-Philosophicus. (Various translations).

2. International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation (DIKWP-SC),World Association of Artificial Consciousness(WAC),World Conference on Artificial Consciousness(WCAC). Standardization of DIKWP Semantic Mathematics of International Test and Evaluation Standards for Artificial Intelligence based on Networked Data-Information-Knowledge-Wisdom-Purpose (DIKWP ) Model. October 2024 DOI: 10.13140/RG.2.2.26233.89445 .  https://www.researchgate.net/publication/384637381_Standardization_of_DIKWP_Semantic_Mathematics_of_International_Test_and_Evaluation_Standards_for_Artificial_Intelligence_based_on_Networked_Data-Information-Knowledge-Wisdom-Purpose_DIKWP_Model

3. Duan, Y. (2023). The Paradox of Mathematics in AI Semantics. Proposed by Prof. Yucong Duan:" As Prof. Yucong Duan proposed the Paradox of Mathematics as that current mathematics will not reach the goal of supporting real AI development since it goes with the routine of based on abstraction of real semantics but want to reach the reality of semantics. ".

4. Piaget, J. (1952). The Origins of Intelligence in Children. International Universities Press.

5. Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

6. Floridi, L. (2011). The Philosophy of Information. Oxford University Press.

7. Frege, G. (1892). On Sense and Reference.

8. Barsalou, L. W. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22(4), 577-660.

9. Mandler, J. M. (2004). The Foundations of Mind: Origins of Conceptual Thought. Oxford University Press.

10. Smith, L. B., & Gasser, M. (2005). The development of embodied cognition: Six lessons from babies. Artificial Life, 11(1-2), 13-29.

Keywords: DIKWP Semantic Mathematics, Core Semantics, Concept Evolution, Wittgenstein, Logical Composition, Infant Cognitive Development, Artificial Intelligence, Semantic Grounding.

Note: This document simulates the evolution of semantics for concepts using core semantics as the foundation, integrating Wittgenstein's logical composition mechanisms. By modeling the process from an infant's perspective, we avoid subjective definitions and explicitly evolve the semantics of each concept, maintaining a strong connection to real-world experiences.