Notes

• Run the Eliza program from next URL and describe your experience.

https://web.njit.edu/~ronkowit/eliza.html


• Write a python program to read two numbers (a, b) and print the values of add, subtract, product, division and power (ab) with proper label.
Answer:
a=5
b=4
print(a+b, a-b, a*b, a/b, a**b)

• List all 5 agent types we covered in the class and explain each type of agent.
Answer:
1. Simple Reflex Agent
These agents make decisions based only on what they see right now. They don't remember the past. They work well only when they can see everything in their environment. And don’t have a memory.
2. Model-based Reflex Agent
Model-Based Agents are like agents with a little more smarts. They understand how things work in the world, and they remember what they've seen before to make better decisions.
3. Goal-based Agent
They’re like agents with a plan. They know what they want, and they think about how to get it by considering different actions and scenarios.
4. Utility-based Agent
Utility-Based Agents are like goal-based agents with a built-in measuring tool. They pick the best actions to achieve their goals efficiently, especially when they have many choices.
5. General Learning Agent
Always improving yourself. So, these learning agents keep learning, checking how they're doing, and trying new things to get better and better. Just like a student who keeps studying and improving their grades.

• Read Sections 1.1 and 1.4 of Rusell and Norvig’s book (4th ed) and summarize the contents in 1~2 pages.
Answer: It was too much text. And my English is not that good. So, i did my best.
1. Different goals in AI: People have different objectives when it comes to AI. Some want AI to think like humans, while others want AI to behave optimally in specific situations.

2. Standard model of AI: The common understanding is that AI primarily focuses on rational actions. An intelligent agent should make the best possible decisions in any given situation. The chapter explores the idea of building such intelligent agents.

3. Challenges in rational decision making: One challenge is that making perfectly rational decisions can be very computationally complex. Another challenge is that AI should pursue objectives that benefit humans, but it might not always know what those objectives are.

4. Contributions from different fields: Various fields have contributed to the development of AI. Philosophers suggested that the mind operates like a machine. Mathematicians provided tools for logical and probabilistic reasoning. Economists helped formalize decision-making. Neuroscientists explored how the brain works. Psychologists and linguists considered humans as information-processing beings. Computer engineers built powerful machines, and software engineers made AI more user-friendly.

5. Control theory and AI: Control theory, which deals with devices that make optimal decisions based on environmental feedback, has also influenced AI, despite using different mathematical tools.

6. History of AI: The history of AI has seen cycles of success, over-optimism, setbacks, and creative approaches. AI has evolved from simple Boolean logic to complex probabilistic reasoning and from hand-crafted knowledge to learning from data, resulting in more capable AI systems.

7. Real world applications and ethical considerations: AI is now used in real-world applications, raising concerns about risks and ethical consequences.

8. Long term challenge: There's a growing concern about controlling super-intelligent AI systems that may evolve unpredictably. This challenge could lead to a rethinking of our understanding of AI.

In essence, the chapter provides an overview of AI, its historical development, the contributions of various fields, and the evolving challenges and ethical considerations in the field of artificial intelligence.