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1. Intelligent Agents aur Software Agents

• Artificial intelligence mein intelligent agent ek autonomous entity hai jo sensors se environment ko observe karti hai aur actuators se act karti hai apne goals ko paane ke liye.
• In agents ke paanch main classes hote hain: simple reflex, model-based, goal-based, utility-based, aur learning agents[cite: 1].
• Software agents physical nahi hote, ye computer systems ke andar APIs aur databases ka use karke kaam karte hain, aur inke main components communication interface, reasoning engine aur knowledge base hote hain[cite: 1].

2. Search Strategies aur AI Problems

• Uninformed search bina kisi extra information ke blindly har path ko check karta hai, jabki informed search (jaise A* search) heuristics ka use karke goal ke sabse kareeb wale path ko pehle chunta hai[cite: 1].
• Constraint Satisfaction Problems (CSPs) mein AI ko ek set of rules (constraints) ko follow karte hue variables ko values assign karni hoti hain (jaise map coloring problem)[cite: 1].
• Game playing mein Alpha-Beta Pruning ka use Minimax algorithm ko fast banane ke liye hota hai taaki AI bekar moves ko ignore (prune) kar sake[cite: 1].

3. Natural Language Processing (NLP)

• NLP mein sabse bade challenges words ke double meanings (ambiguity), complex grammar, aur sarcasm ko samajhna hai[cite: 1].
• Modern AI in problems ko Deep Learning aur Large Language Models (LLMs) ke self-attention mechanism se solve karta hai jo pure sentence ka context ek sath dekhte hain[cite: 1].
• Lexical analysis mein text ko tokens mein break karne ke liye Finite State Automata (FSA) ka use hota hai, jo fast aur memory-efficient hota hai[cite: 1].

4. Internet of Things (IoT)

• IoT ka vision ek fully connected smart world banana hai jahan physical environment automatic tarike se human needs ko respond kare[cite: 1].
• IoT ke enabling technologies mein miniaturized sensors, wireless connectivity, cloud computing aur IPv6 shamil hain[cite: 1].
• Smart cities mein IoT ka use traffic aur electricity manage karne ke liye hota hai, par isme massive data handle karna aur high installation cost jaise challenges hote hain[cite: 1].

5. Deep Learning aur Cloud Computing

• Deep learning models raw data (jaise images ya text) se automatic feature extraction karte hain, jabki purane ML models mein humans ko features manually code karne padte the[cite: 1].
• Cloud computing users ko internet ke through hardware, software, aur storage rent par lene ki suvidha deta hai[cite: 1].
• Cloud ke 3 main service models hain: IaaS (Infrastructure as a Service), PaaS (Platform as a Service), aur SaaS (Software as a Service)[cite: 1].