Hi! I'm [M Aqsam] , a student at FAST University. This blog post is for my Artificial Intelligence course assignment, where I'm reviewing two research papers from 2025.

PAPER 1: A* Algorithm with Adaptive Weights

What is the Goal of This Paper?

The traditional A* algorithm that we studied in class has three main problems:

Goal of this paper: Solve these problems so robots can navigate efficiently.

Four Improvements

Results

PAPER 2: The Rise of Agentic AI

What is the Goal of This Paper?

Agentic AI = AI that can think and act independently.

Five Patterns of Agentic AI

1. Tool Use - AI can use calculators, APIs, search engines

2. Reflection - AI learns from its mistakes

3. Re Act - Reason and Act in cycles (think then do)

4. Planning - Break complex goals into smaller steps

5. Multi-Agent - Multiple AI systems work together

📋 Seven Types of Agents

COURSE CONNECTION

Connection to Part A: Rescue Robot Scenario

Why we chose Utility-Based Agent? Paper 2 confirms that utility-based agents work best in complex environments like our flood scenario!

Connection to Part B: Search Algorithms

Connection to Part C: Simulated Annealing

Remember this formula from Part C?

P(accept) = e^(-ΔE / T)

Paper 1 uses the SAME concept! When the algorithm gets stuck in a local optimum, it temporarily accepts "worse" paths to explore better options - exactly like Simulated Annealing!

Connection to CSPs (Constraint Satisfaction Problems)

Paper 1's Grey Wolf Optimizer treats weight adjustment as an optimization problem - exactly like we formulated survivor prioritization in Part C!

PERSONAL INSIGHT: Manual Reading vs NotebookLM

Phase 1: Manual Reading (Confusion)

When I first read these papers by myself:

• The mathematics of Grey Wolf Optimizer was difficult to understand

• Technical terms like "Re Act" and "Reflection" were confusing

• I felt overwhelmed by the complexity

Honest feeling: "Will I ever be able to understand research papers?"

Phase 2: NotebookLM(Clarity)

I uploaded both papers to Google NotebookLM (notebooklm.google.com) and asked:

• "Explain Grey Wolf Optimizer in simple terms with an example"

• "Create a table comparing all 7 agent types"

• "Summarize the five operational patterns in simple words"

• "Show me the results in a bullet list"

What NotebookLM gave me:

• Simple explanations with everyday examples

• Clean tables and easy comparisons

• Clear summaries of complex sections

• Citations back to the original paper

Example: NotebookLM explained Grey Wolf Optimizer as:

"Imagine a pack of wolves hunting. The alpha wolf leads, beta and delta help, and omega follows. They search (explore), surround (exploit), and attack (converge). GWO mimics this behavior."

So easy to understand!

Phase 3: Back to the Paper (Understanding)

With NotebookLM's explanations, I read the papers again. Everything became clear!

Key Realization:

• NotebookLM helped me understand 30% of the paper

• I understood the remaining 70% by reading myself

• AI didn't replace learning - it accelerated learning

What I Found Most Interesting

From Paper 1: Grey Wolf Optimizer is inspired by wolf hunting behavior! Nature + algorithms = beautiful combination.

From Paper 2: Self-improving AI agents that get better without human intervention - science fiction is becoming reality!

Both Papers: They take what we learned in class and show how real research improves it. This makes me feel confident about my choice of field!

🎥 VIDEO WALKTHROUGH

I made video explaining these papers. Watch here:

[https://youtu.be/794eacYVp_U]

📊 COMPARISON TABLE: Traditional vs Improved A*

🎯 Why This Matters for AI Students

We often think that what we learn in class is the final truth. These papers prove:

1. Algorithms constantly improve - A* was created in 1968, still improving in 2025!

2. Research builds on fundamentals - What we study is the foundation

3. Interdisciplinary thinking works - Wolf hunting behavior inspired an algorithm!

4. Course material is essential - Without basics, you cannot understand research

📝 Conclusion

Both papers showed me the depth of AI:

Paper 1 showed that even classic algorithms like A* have room for 76% improvement when you think creatively.

Paper 2 showed that agentic AI - which sounds like science fiction - is already here with clear patterns, types, and frameworks.

Course connection proved that what we are learning isn't outdated theory - it's the foundation for cutting-edge research.

👋 About Me

Name: [M Aqsam]

University: FAST University

Course: Artificial Intelligence

Follow me:

• Hashnode: https://hashnode.com/@aqsamqureshi

• Dev.to: https://dev.to/muaqqu

Tagging @Raqeeb_26 (Dev.to) and @raqeebr (Hashnode) as per assignment requirement.