Planning and Decision Making

Planning in AI focuses on generating sequences of actions to transition from an initial state to a desired goal state. Various planning systems, such as STRIPS and Goal Stack Planning, facilitate problem-solving in complex environments, while Markov Decision Processes (MDPs) deal with decision-making under uncertainty. These tools enable the design of intelligent agents capable of effective long-term goal achievement and rational behavior in both deterministic and uncertain contexts.

Sections

  • 5

    Planning And Decision Making

    This section explores the fundamentals of planning and decision-making in AI, detailing the essential components, mechanisms like STRIPS, Goal Stack Planning, and Markov Decision Processes (MDPs).

    Learning Practice

  • 5.1

    Introduction To Planning In Ai

    This section introduces the concept of planning in artificial intelligence, focusing on the generation of action sequences to achieve specific goals.

    Learning

  • 5.1.1

    Why Planning?

    Planning in AI is crucial for navigating complex environments and achieving long-term goals.

    Learning Practice

  • 5.1.2

    Components Of A Planning System

    This section outlines the essential components of a planning system in AI, including initial state, goal state, actions, and plans.

    Learning Practice

  • 5.2

    Strips And Goal Stack Planning

    This section delves into STRIPS, a formal language for representing planning problems, and Goal Stack Planning, a method that approaches problem-solving through backward chaining.

    Learning Practice

  • 5.2.1

    Strips (Stanford Research Institute Problem Solver)

    STRIPS is a formal language for representing planning problems in AI, focusing on defining actions through preconditions, add lists, and delete lists.

    Learning Practice

  • 5.2.2

    Goal Stack Planning

    Goal Stack Planning is a backward-chaining method used in AI to systematically break down goals into actions.

    Learning Practice

  • 5.3

    Markov Decision Processes (Mdps)

    Markov Decision Processes (MDPs) provide a mathematical framework for making decisions under uncertainty, promoting optimal action selection in various scenarios.

    Learning Practice

  • 5.3.1

    Mdp Definition

    This section defines Markov Decision Processes (MDPs), outlining their components and significance in decision-making under uncertainty.

    Learning Practice

  • 5.3.2

    Objective Of Mdps

    The objective of Markov Decision Processes (MDPs) is to determine a policy that maximizes expected utility over time.

    Learning Practice

  • 5.3.3

    Solving Mdps

    This section outlines methods for solving Markov Decision Processes (MDPs), focusing specifically on value iteration and policy iteration.

    Learning Practice

  • 5.3.4

    Applications Of Mdps

    This section discusses various applications of Markov Decision Processes (MDPs) in fields such as robotics, inventory control, game AI, and healthcare.

    Learning Practice

References

Chapter 5_ Planning and Decision Making.pdf

Class Notes

Memorization

What we have learnt

  • Planning is crucial in AI f...
  • STRIPS simplifies planning ...
  • MDPs provide a framework fo...

Final Test

Revision Tests

Chapter FAQs