Uploaded September 2025 | Updated September 2026, 1 week ago
Artificial Intelligence (AI) playlist:
youtube.com/watch?v=deCJN0gfc_k&list=PLFxhgwM1F4yydQIq-ab2jYjPUL9LITF8_
For access to this presentation materials, membership is required: I need the Material PPT
Sent me an email to Technologies.Discussion@gmail.com
If you need the whole playlist material, send me email and we discuss.
Give me some time to response. Thanks.
AI #2. Problem-Solving AI Vs Reasoning AI. Most AI Task, Reasoning First Before Problem-Solving.
3. Problem-Solving (Finding a Solution to a Goal)
Problem-solving is a critical component of AI, focusing on the ability of machines to analyze problems, devise strategies, and find solutions.
AI systems employ various algorithms and techniques to tackle problems, often using heuristics and optimization methods.
For instance, search algorithms can explore possible paths or solutions to find the best one. Planning algorithms can create sequences of actions to achieve desired goals. Optimization algorithms can optimize solutions based on specific criteria.
Types of Problem-Solving Technique
Search & Optimization:
Constraint Satisfaction:
Game Theory:
3. Problem-Solving (Finding a Solution to a Goal)
Problem-solving involves finding a path from a given initial state to a desired goal state. It requires defining the problem, planning a sequence of actions, and overcoming obstacles.
Techniques:
Search & Optimization: Searching through a set of possible solutions to find the best one (e.g., Google Maps finding the fastest route).
Constraint Satisfaction: Solving problems where the solution must meet a set of constraints or rules (e.g., scheduling classes so no teacher is in two places at once).
Game Theory: Making strategic decisions in competitive situations.
Example: A chess engine like AlphaZero is a master of problem-solving, evaluating millions of possible moves to find the one that maximizes its chance of winning.
Example: Planning a Trip
Let's imagine an AI assistant helping you plan a weekend trip.
1. The Reasoning Component
First, the AI needs to reason about the information you give it and its own knowledge.
User Input: "I want to go to a warm beach this weekend. I have a budget of $500."
AI's Knowledge Base: Facts like "Miami is a beach city," "Miami is in Florida," "Florida is warm in summer," "Flights to Miami cost ~$300," "Hotels in Miami cost ~$200/night," "A weekend is two nights."
The AI now begins to reason:
Deductive Reasoning: "If Florida is warm, and Miami is in Florida, then Miami is warm." (This uses logic to derive a new fact).
Logical Inference: "The user wants a warm beach. Miami is a warm beach. Therefore, Miami is a potential destination."
Constraint Checking: "The budget is $500. A flight is $300. A hotel for two nights is $400. $300 + $400 = $700. $700 more than $500." (This uses arithmetic, a form of symbolic reasoning).
Conclusion: "The trip to Miami exceeds the budget."
This entire process is reasoning. The AI isn't The AI will now search through these possibilities. For example:
Action: Search for beaches within driving distance.
New State: Potential destination: Outer Banks, NC. Cost: $100 for gas, $250 for hotel. Total: $350.
Check Goal: $350 ≤ $500. Goal is achieved!
The process of generating options, evaluating them against the goal, and selecting a successful path is problem-solving. It used the conclusions from its reasoning (e.g., "Miami is too expensive") to guide its search toward a viable solution.
solving the problem yet; it's just figuring out what is true and what isn't based on logic and constraints.
The Problem-Solving Component
Now that the AI has reasoned that the initial idea fails, it needs to solve the problem: "Find a warm beach destination for a weekend that costs ≤ $500."
The AI switches to a problem-solving mode. It defines:
Initial State: User is at home, has $500, wants a beach trip.
Goal State: User is on a warm beach, total cost ≤ $500.
Possible Actions: Search for alternative destinations, search for cheaper flights on different days, search for cheaper accommodation, suggest a driving destination to save on flight costs.
Artificial Intelligence (AI) playlist:
youtube.com/watch?v=deCJN0gfc_k&list=PLFxhgwM1F4yydQIq-ab2jYjPUL9LITF8_
For access to this presentation materials, membership is required: I need the Material PPT
Sent me an email to Technologies.Discussion@gmail.com
If you need the whole playlist material, send me email and we discuss.
Give me some time to response. Thanks.
AI #2. Problem-Solving AI Vs Reasoning AI. Most AI Task, Reasoning First Before Problem-Solving.
3. Problem-Solving (Finding a Solution to a Goal)
Problem-solving is a critical component of AI, focusing on the ability of machines to analyze problems, devise strategies, and find solutions.
AI systems employ various algorithms and techniques to tackle problems, often using heuristics and optimization methods.
For instance, search algorithms can explore possible paths or solutions to find the best one. Planning algorithms can create sequences of actions to achieve desired goals. Optimization algorithms can optimize solutions based on specific criteria.
Types of Problem-Solving Technique
Search & Optimization:
Constraint Satisfaction:
Game Theory:
3. Problem-Solving (Finding a Solution to a Goal)
Problem-solving involves finding a path from a given initial state to a desired goal state. It requires defining the problem, planning a sequence of actions, and overcoming obstacles.
Techniques:
Search & Optimization: Searching through a set of possible solutions to find the best one (e.g., Google Maps finding the fastest route).
Constraint Satisfaction: Solving problems where the solution must meet a set of constraints or rules (e.g., scheduling classes so no teacher is in two places at once).
Game Theory: Making strategic decisions in competitive situations.
Example: A chess engine like AlphaZero is a master of problem-solving, evaluating millions of possible moves to find the one that maximizes its chance of winning.
Example: Planning a Trip
Let's imagine an AI assistant helping you plan a weekend trip.
1. The Reasoning Component
First, the AI needs to reason about the information you give it and its own knowledge.
User Input: "I want to go to a warm beach this weekend. I have a budget of $500."
AI's Knowledge Base: Facts like "Miami is a beach city," "Miami is in Florida," "Florida is warm in summer," "Flights to Miami cost ~$300," "Hotels in Miami cost ~$200/night," "A weekend is two nights."
The AI now begins to reason:
Deductive Reasoning: "If Florida is warm, and Miami is in Florida, then Miami is warm." (This uses logic to derive a new fact).
Logical Inference: "The user wants a warm beach. Miami is a warm beach. Therefore, Miami is a potential destination."
Constraint Checking: "The budget is $500. A flight is $300. A hotel for two nights is $400. $300 + $400 = $700. $700 more than $500." (This uses arithmetic, a form of symbolic reasoning).
Conclusion: "The trip to Miami exceeds the budget."
This entire process is reasoning. The AI isn't The AI will now search through these possibilities. For example:
Action: Search for beaches within driving distance.
New State: Potential destination: Outer Banks, NC. Cost: $100 for gas, $250 for hotel. Total: $350.
Check Goal: $350 ≤ $500. Goal is achieved!
The process of generating options, evaluating them against the goal, and selecting a successful path is problem-solving. It used the conclusions from its reasoning (e.g., "Miami is too expensive") to guide its search toward a viable solution.
solving the problem yet; it's just figuring out what is true and what isn't based on logic and constraints.
The Problem-Solving Component
Now that the AI has reasoned that the initial idea fails, it needs to solve the problem: "Find a warm beach destination for a weekend that costs ≤ $500."
The AI switches to a problem-solving mode. It defines:
Initial State: User is at home, has $500, wants a beach trip.
Goal State: User is on a warm beach, total cost ≤ $500.
Possible Actions: Search for alternative destinations, search for cheaper flights on different days, search for cheaper accommodation, suggest a driving destination to save on flight costs.
