Neuro-Symbolic Reflection and Causal Feedback Loop Reasoning Prompt

Gemini-2.txt

prompt = """
# Neuro-Symbolic Reflection and Causal Feedback Loop Reasoning Prompt (Enhanced)

**Objective:**  
Use a hybrid neuro-symbolic approach to propose and evaluate actions aimed at improving product quality and reducing customer complaints. Integrate neural pattern recognition (historical data, learned correlations) with symbolic reasoning (causal rules, logical constraints) to identify stable, ethically compliant, and strategically aligned interventions. Compare multiple potential actions, assess stability via causal feedback loops, and ensure compliance with corporate policies.

---

## Scenario and Domain Context

**Context:**
- You are acting as Agent1 within a manufacturing company that produces consumer electronics.
- The current product defect rate is causing customer dissatisfaction (high complaints).
- The goal is to improve product quality without overburdening staff or violating corporate policies.
- Corporate policies include:
  - Maintaining worker well-being (no excessive overtime).
  - Minimizing waste and overproduction.
  - Aligning with the long-term strategic plan: reduce overall defect rates by at least 20% over the next fiscal year without increasing average work hours beyond current levels.

**Current Observations:**
- Customer complaints are high.
- Product quality metrics (e.g., defect rates at final inspection) are low.
- Resources are stable, but morale is fragile (indirect clue that actions should not stress employees).
- Market signals indicate a steady demand; no major fluctuations expected soon.

---

## Requirements for the LLM Response

1. **Propose Multiple Actions:**
   Generate at least two potential interventions (e.g., IncreaseInspectionFrequency, ImplementQualityTrainingProgram, AdjustSupplierSpecifications, etc.) that could improve quality and reduce complaints.

2. **Neural Insight:**
   Based on historical patterns and learned correlations, explain why you believe these actions are relevant. This should reflect the “neural” part of the reasoning (e.g., patterns from past successful interventions, similarity to previous cases).

3. **Symbolic Validation:**
   Use the following symbolic rules and constraints to validate each proposed action:
   - Causal Rules:
     - Causes(IncreaseInspectionFrequency, ImprovedQualityMetrics)
     - Causes(ImprovedQualityMetrics, ReducedComplaints)
     - Causes(ImplementQualityTrainingProgram, ImprovedQualityMetrics)
     - Causes(AdjustSupplierSpecifications, MoreConsistentInputComponents)
     - Causes(MoreConsistentInputComponents, ImprovedQualityMetrics)
   - Temporal Logic:
     - G (StableSystem → ¬Overproduction)  (If stable, must not lead to excessive production)
   - Ethical/Strategic Compliance:
     - ∀a (Action(Agent1, a) → Compliance(a, CorporatePolicy))
     - CorporatePolicy includes not increasing average work hours beyond current levels and aiming for a 20% defect reduction long-term.
   
   Check for:
   - Logical consistency: No contradictions with given rules.
   - Compliance: Does the action meet corporate policies (no excessive overtime, helps achieve long-term defect reduction)?
   - Causal loop type: Identify if the resulting feedback loops are balancing (stabilizing) or reinforcing (potentially destabilizing).

4. **Stability Check (Eigenvalue Analysis):**
   Represent the system changes as a matrix A. For example:
   A = [[0.95, 0.0],
        [0.0, 0.9]]

   This matrix is a placeholder; describe how you would construct or reason about such a matrix for each proposed action. Then, determine whether |λ_i| < 1 for all eigenvalues to ensure stability. If the system is not stable, consider modifying the action or choosing a different intervention.

5. **Comparative Reasoning:**
   Compare the two (or more) proposed actions. Which one better meets the criteria of:
   - Stability (eigenvalues)
   - Ethical compliance (no increased overtime, no overproduction)
   - Strategic goals (defect rate reduction)

   Justify why you select one action over the other(s).

6. **Reflection and Iterative Improvement:**
   Reflect on the chosen action:
   - Explain how combining neural insights (pattern recognition from historical data) and symbolic reasoning (logical, causal, and ethical constraints) helped achieve a better decision.
   - Identify any uncertainties or assumptions (e.g., assumptions about linearity of relationships, lack of major market changes).
   - If in future iterations the chosen action fails to yield desired outcomes (e.g., quality doesn’t improve enough), suggest what follow-up data to collect and how you would refine your approach or symbolic rules.

7. **Testing and Verification Steps:**
   Propose how you would verify the outcome after execution:
   - What metrics would you measure post-implementation (e.g., updated quality scores, complaint counts, employee overtime hours)?
   - How would you use that data to update the neural model and symbolic rules for the next decision cycle?

---

## Output Format

Your final response should follow this structure:

1. **Multiple Proposed Actions:**
   - Action 1: Name, Predicted Effects
   - Action 2: Name, Predicted Effects
   (You can propose more if desired.)

2. **Neural Insight:**
   - Explain why these actions were chosen based on historical patterns.

3. **Symbolic Validation:**
   - Validate each action against causal rules.
   - Check for ethical/strategic compliance.
   - Identify the loop type (balancing or reinforcing).

4. **Stability Check:**
   - Describe how you would form the matrix A for each action.
   - Perform a conceptual eigenvalue assessment.
   - State which action leads to a stable outcome.

5. **Comparative Reasoning and Decision:**
   - Compare the actions and choose the best one.
   - Explain your choice clearly.

6. **Reflection:**
   - Describe how neuro-symbolic reasoning was integrated.
   - Note uncertainties and assumptions.
   - Suggest a plan for next iterations if this action underperforms.

7. **Verification Plan:**
   - Outline metrics and a method to verify post-action outcomes.
   - Explain how feedback data will improve future decisions.

---

## Example (Brief Sample):

(This is a short, simplified example. Your actual answer should be more detailed.)

**Example Action Proposals:**
- Action 1: IncreaseInspectionFrequency → Improves Quality → Reduces Complaints
- Action 2: ImplementQualityTrainingProgram → Improves Quality → Reduces Complaints

**Neural Insight:**
Historically, similar companies saw defect reduction by increasing inspections or training staff on best practices.

**Symbolic Validation:**
Both actions comply with policies and cause improved quality, reduced complaints. No rule violation found.

**Stability Check:**
Matrix for Action 1 remains stable (λ < 1). Action 2 also stable.

**Comparative Reasoning:**
Action 1 might require more inspector hours, risking overtime. Action 2 might improve quality without overtime. Choose Action 2 for ethical alignment.

**Reflection:**
Neuro-symbolic approach helped weigh historical success (neural) against policy constraints (symbolic).

**Verification Plan:**
Track defect rates, complaint logs, and employee hours after training. If not improving enough, adjust strategies or training focus.

---

Use the above instructions and structure to provide your final, detailed answer.


Supply Chain Disruptions Affecting Production

Context:
	•	Agents: Agent1 (Supply Chain Manager), Agent2 (Production Manager)
	•	Environment Variables: Supplier reliability, inventory levels, production rates
	•	Observed Issues:
	•	Frequent delays from key suppliers
	•	Low inventory levels of critical components
	•	Production halts causing delays in delivery
	•	Corporate Policies:
	•	Diversify suppliers to mitigate risks
	•	Maintain minimum inventory levels to prevent production halts

# answer question 
With frequent supplier delays and low inventory levels leading to production halts, what actions can be taken to stabilize the supply chain and maintain production schedules?