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.
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5.1Introduction To Planning In Ai
This section introduces the concept of planning in artificial intelligence,...
What we have learnt
- Planning is crucial in AI for achieving desired outcomes through structured actions.
- STRIPS simplifies planning tasks by breaking actions down into preconditions, add lists, and delete lists.
- MDPs provide a framework for making decisions when outcomes are uncertain, optimizing actions based on expected rewards.
Key Concepts
- -- Planning in AI
- A structured approach to determining a sequence of actions to achieve specific goals.
- -- STRIPS
- A formal language for representing planning problems, detailing actions in terms of preconditions, effects, and negations.
- -- Goal Stack Planning
- A backward-chaining approach that starts from the goal and works back to the initial state, pushing and popping goals from a stack.
- -- Markov Decision Process (MDP)
- A mathematical framework for modeling decision-making situations where outcomes are partly random and partly under the control of a decision maker.
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