Constrained Object Hierarchies (COH) provides a neuroscience-inspired theoretical framework for modeling Artificial General Intelligence (AGI). As a flexible and robust foundation, COH supports the representation of general intelligent systems across diverse domains.
Formally, a COH is defined as a 9-tuple:
O = (C, A, M, N, E, I, T, G, D)
Where:
- C: Components – sub-objects organized hierarchically
- A: Attributes – state variables describing object properties
- M: Methods – executable actions associated with components
- N: Neural Components – adaptive models for learning and inference
- E: Embedding Neural Component – semantic representation of the COH
- I: Identity Constraints – rules ensuring object integrity
- T: Trigger Constraints – event-condition-action rules for dynamic behavior
- G: Goal Constraints – optimization objectives guiding intelligent behavior
- D: Constraint Daemons – real-time monitors enforcing system constraints
Based on the COH framework, the modelling process and result of Automated Essay Scorer with Feedback are as follows:
Description & Significance: This system automatically evaluates student essays and generates formative feedback. It addresses the scalability of providing detailed writing assessment [13].
Analysis & Requirements: The system must analyze text for traits like grammar, coherence, and argument strength, score based on a rubric, and generate actionable feedback.
COH Solution Design: The system is modelled as an EssayScorer object composed of TextAnalyzer, RubricEngine, and FeedbackGenerator. A neural model (N) performs the core analysis.
COH Modelling Solution:
-
- C:
TextAnalyzer, RubricEngine, FeedbackGenerator, PlagiarismDetector
- A:
submitted_essay, rubric, traits_scores
- M:
extract_features(), score_essay(), generate_feedback()
- N: A fine-tuned Transformer model (e.g., BERT) within
TextAnalyzer.
- E: Essays and rubric criteria embedded into a semantic space.
- I: Scores must adhere to rubric bounds; Feedback must reference low-scoring traits.
- T: ON
essay_submission DO score_essay() THEN generate_feedback().
- G: Minimize discrepancy between system scores and expert human scores.
- D: Flags essays with low originality scores for human review.
COH cleanly separates the analytical, scoring, and feedback generation aspects. The neural component (N) provides the analytical power, while the constraints ensure validity (I) and operational flow (T). The goal (G) ensures the system's output is aligned with human judgment.
You may choose this topic and pursue one of the following routes:
📝 Essay
- Survey existing methodologies for modeling, designing, and implementing similar intelligent systems.
- Include:
- Literature synthesis
- COH framework analysis
- Critical evaluation of competing approaches
🛠️ Project
- Design and implement the system using the COH model.
- Include:
- System architecture and design
- Development and testing
- Demonstration of core functionality
🎓 Thesis
- Explore COH’s theoretical foundations in depth.
- Include:
- Proposed enhancements or extensions
- Implementation and evaluation
- Development of an advanced intelligent system grounded in COH principles
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