Lesson 22 - Module 9 Planning Version 1 CSE IIT, Kharagpur...

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Module 9 Planning Version 1 CSE IIT, Kharagpur
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9.1 Instructional Objective The students should understand the formulation of planning problems The student should understand the difference between problem solving and planning and the need for knowledge representation in large scale problem solving Students should understand the STRIPS planning language Students should be able to represent a real life planning problem using STRIPS operators Students should understand planning using situation calculus and the related frame problems Students should understand the formulation of planning as a search problem Students should learn the following planning algorithms o Situation space planning o Plan space planning o Progression planning o Regression planning The student should understand the difficulties of full commitment planning Students should understand the necessity of least commitment Students should learn partial order planning algorithms At the end of this lesson the student should be able to do the following: Represent a planning problem in STRIPS language Use a suitable planning algorithm to solve the problem. Version 1 CSE IIT, Kharagpur
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Lesson 22 Logic based planning Version 1 CSE IIT, Kharagpur
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9. 1 Introduction to Planning The purpose of planning is to find a sequence of actions that achieves a given goal when performed starting in a given state. In other words, given a set of operator instances (defining the possible primitive actions by the agent), an initial state description, and a goal state description or predicate, the planning agent computes a plan. What is a plan? A sequence of operator instances, such that "executing" them in the initial state will change the world to a state satisfying the goal state description. Goals are usually specified as a conjunction of goals to be achieved. 9.1.1 A Simple Planning Agent: Earlier we saw that problem-solving agents are able to plan ahead - to consider the consequences of sequences of actions - before acting. We also saw that a knowledge- based agents can select actions based on explicit, logical representations of the current state and the effects of actions. This allows the agent to succeed in complex, inaccessible environments that are too difficult for a problem-solving agent Problem Solving Agents + Knowledge-based Agents = Planning Agents In this module, we put these two ideas together to build planning agents. At the most abstract level, the task of planning is the same as problem solving. Planning can be viewed as a type of problem solving in which the agent uses beliefs about actions and their consequences to search for a solution over the more abstract space of plans, rather than over the space of situations 9 .1.2 Algorithm of a simple planning agent: 1. Generate a goal to achieve 2. Construct a plan to achieve goal from current state 3. Execute plan until finished 4. Begin again with new goal Version 1 CSE IIT, Kharagpur
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This note was uploaded on 09/20/2010 for the course MCA DEPART 501 taught by Professor Hemant during the Fall '10 term at Institute of Computer Technology College.

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Lesson 22 - Module 9 Planning Version 1 CSE IIT, Kharagpur...

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