DEV Community: 24P-0697 Muhammad Usman

Research on A* Algorithm Based on Adaptive Weights (2025)

24P-0697 Muhammad Usman — Sat, 14 Mar 2026 17:07:13 +0000

1. Introduction
This paper is about improving the A* search algorithm. The main purpose is to make searching faster and more efficient by adjusting weights automatically.

2. Summary and Course Connection
In our AI course, we learned about uninformed and informed search methods. A* is an informed search that uses heuristics. This paper modifies A* by adjusting weights during the search process.
This connects with our topic of heuristic search and pathfinding problems.

3. Technical Understanding
The paper explains how adaptive weights reduce unnecessary node expansion. It also compares traditional A* with improved A*. Some formulas were difficult, but the results were clearly explained.

4. Personal Insight
Manual reading was difficult for me because of mathematics and technical terms. NotebookLM helped me summarize the steps and provided simple explanations.

5. Conclusion
This paper helped me understand how the A* algorithm can be improved for real-world problems such as navigation and robotics.

@raqeeb_26.

The Rise of Agentic AI (2025)

24P-0697 Muhammad Usman — Sat, 14 Mar 2026 16:54:21 +0000

1. Introduction
This paper is about agentic AI, which means AI systems that can act on their own. The main goal of this paper is to explain how modern AI agents are designed, their frameworks, and the problems they face. It helps students understand how intelligent agents work in real life.

2. Summary and Course Connection
In our AI course, we study intelligent agents that sense the environment and take actions. This paper talks about planning, memory, and decision-making in AI agents. It connects with topics like reflex agents, goal-based agents, and learning agents.
The paper also discusses how agents can use tools and reasoning to complete tasks. This is similar to what we learned in agent architecture and agent logic.

3. Technical Understanding
The authors explain different frameworks for agentic AI. They also describe challenges like safety, errors, and lack of control. Some explanations were difficult, but overall they were clear.

4. Personal Insight
When I read the paper manually, I felt confused in some parts. NotebookLM helped me by giving summaries and examples. It made the paper easier to understand.

5. Conclusion
This paper improved my understanding of intelligent agents. It showed how AI is becoming more independent and intelligent.

@raqeeb_26.

Agentic AI means AI that can act on its own. This paper explains how AI agents plan, remember, and make decisions. It connects to reflex, goal-based, and learning agents. Tools like NotebookLM help understand it. I learned how AI is becoming smarter.

24P-0697 Muhammad Usman — Sat, 14 Mar 2026 16:48:11 +0000