Planning & Reasoning in AI - tech9tel/ai GitHub Wiki
๐งฉ Planning & Reasoning in AI
Planning & Reasoning is a core subfield of Artificial Intelligence (AI) that focuses on enabling machines to plan sequences of actions and make logical decisions to achieve specific goals under given constraints and environments.
๐ค What is Planning?
Planning in AI is the process of selecting a sequence of actions that transforms the current state into a desired goal state.
- It answers the question: "What should the agent do next?"
- Involves defining states, actions, and transitions.
- Used in games, robotics, logistics, and more.
๐ Example: A robot vacuum planning its path to clean every room without redundancy.
๐ง What is Reasoning?
Reasoning allows AI systems to infer new information from existing knowledge.
- Types:
- Deductive: Logical conclusions from known facts.
- Inductive: Generalizing from examples.
- Abductive: Inferring the most likely explanation.
๐งฉ Example: A medical diagnosis system reasoning symptoms to identify diseases.
๐งฌ Planning + Reasoning = Intelligence
When combined:
- AI can analyze situations, reason about consequences, and plan optimal actions to achieve goals.
๐งญ Real-World Applications
| Area | Use Case Example |
|---|---|
| ๐งน Robotics | Autonomous navigation, task scheduling |
| ๐ฆ Logistics | Delivery route optimization, warehouse task allocation |
| ๐ฎ Game AI | NPC behavior and action planning |
| ๐งฌ Bioinformatics | Protein folding, drug discovery workflows |
| ๐ญ Manufacturing | Workflow automation, intelligent scheduling |
| ๐ผ Virtual Assistants | Task reminders, smart calendar scheduling |
๐งฑ Key Concepts
- State Space: All possible configurations the agent can be in.
- Goal State: The desired outcome the system aims to reach.
- Actions: Steps that transition the system between states.
- Search Algorithms: Techniques to find the optimal path to the goal.
- Heuristics: Rules to speed up the decision-making process.
๐งฐ Common Algorithms
| Algorithm | Purpose |
|---|---|
| A* Search | Optimal path planning with heuristics |
| STRIPS | Planning representation language |
| PDDL | Planning Domain Definition Language |
| Minimax | Decision making in adversarial games |
| Forward/Backward Chaining | Logical inference techniques |
๐ง Models & Frameworks
- Hierarchical Task Networks (HTNs) โ Plan using task decomposition.
- Constraint Satisfaction Problems (CSPs) โ Solve planning under rules.
- Probabilistic Planning โ Handle uncertainty in outcomes.
- Markov Decision Processes (MDPs) โ Sequential decision making under uncertainty.
๐ Related AI Subfields
| Closely Connected With | Description |
|---|---|
| ๐ค Robotics | Uses planning for autonomous task execution |
| ๐ง Knowledge Representation | Stores data in usable logic for reasoning |
| ๐ฏ Reinforcement Learning | Learns optimal policies instead of fixed plans |
| ๐ Search & Optimization | Core technique behind planning algorithms |
๐ข Companies Using It
- Boston Dynamics โ Robot movement and task planning
- Amazon Robotics โ Warehouse automation
- Waymo โ Autonomous driving decisions
- IBM Watson โ Diagnostic reasoning in healthcare
- DeepMind โ Game strategies and decision systems
๐ Future Scope
- ๐ Better integration with ML for adaptive planning
- ๐งฉ Hybrid systems combining symbolic and probabilistic reasoning
- ๐ Use in climate modeling, global logistics, and space exploration
๐ง Analogy
Planning is like writing a to-do list with steps to reach a goal. Reasoning is like figuring out which items on the list are worth doing and why.
๐งพ Summary
| Topic | Insight |
|---|---|
| What | AI that decides what to do and why |
| How | Using search, heuristics, and logical inference |
| Where | Used in robots, logistics, assistants, and games |
| Key Challenge | Adapting to dynamic and uncertain environments |
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