CHAPTER5 PLANNING INTRODUCTION A sequence of actions that

CHAPTER-5 PLANNING

INTRODUCTION • A sequence of actions that will achieve a goal is called planning. • Planning agents. Example: search-based problem–solving agent, logical planning agent. • Environments that are fully observable, deterministic , finite, static and discrete. These are called classical planning environments.

CLASSICAL PLANNING ENVIRONMENT • Fully Observable- If an agent’s sensors give it access to the complete state of environment at each point in time, then we say that the task environment is fully observable. • Deterministic- If the next state of the environment is completely determined by the current state and the action executed by the agent, then we say the environment is deterministic.

CLASSICAL PLANNING ENVIRONMENT • Static- if the environment is not changing while an agent is deliberating. Static environments are easy to deal with because the agent need not keep looking at the world while it is deciding on an action. Eg: Crossword puzzles • Discrete- The way time is handled, and to the percepts and actions of the agent. Eg: Chess has a discrete set of precepts and actions.

THE LANGUAGE OF PLANNING PROBLEMS • The representation of planning problems— states, actions, and goals • Representation of States • Representation of Goals • Representation of Actions

THE LANGUAGE OF PLANNING PROBLEMS • Representation of States Planners decompose the world into logical conditions and represent a state as a conjunction of positive literals Example for First –order literals At (Plane 1; Melbourne) ᴧ At (Plane 2; Sydney)

THE LANGUAGE OF PLANNING PROBLEMS • Representation of Goals • A goal is a partially specified state, represented as a conjunction of positive ground literals • Example: Rich ᴧ Famous ᴧ Miserable satisfies the goal Rich ᴧ Famous.

THE LANGUAGE OF PLANNING PROBLEMS • Representation of Actions An action is specified in terms of the preconditions that must hold before it can be executed and the effects that ensue when it is executed. Example: an action for flying a plane from one location to another is:

EXPRESSIVENESS AND EXTENSIONS • STRIPS (Stanford Research Institute Problem Solver) is an automated planner developed by Richard Fikes and Nilsson in 1971. • The various planning formalisms used been systematized within a standard syntax called Planning Domain Definition Language(PDDL)

AIR CARGO TRANSPORT • The Problem can be defined by 3 actions: Load, Unload and Fly • [Load (C 1, P 1, J), Fly(P 1, J, R), Unload(C 1, P 1, R) • Load(C 2, P 2, R, ), Fly(P 2, R, J), Unload(C 2, P 2, J)] J-Jazan R-Riyadh C-cargo P-plane

PARTIAL ORDER PLANNING • Forward and Backward state-space search are particular forms of totally ordered plan search. • They explore only strictly linear sequences of actions directly connected to the start or goal. • The general strategy of delaying a choice during search is called a least commitment strategy. • Least commitment is a useful concept for analyzing when decisions should be made in any search problem.

PARTIAL ORDER PLANNING • Consider the simple problem of putting on a pair of shoes – Example • Goal(Right. Shoe. On Left. Shoe. On) • Init() • Action(Right. Shoe, PRECOND: Right. Sock. On, EFFECT: Right. Shoe. On) • Action(Right. Sock, EFFECT: Right. Sock. On) • Action(Left. Shoe, PRECOND: Left. Sock. On, EFFECT: Left. Shoe. On) • Action(Left. Sock, EFFECT: Left. Sock. On)

PARTIAL ORDER PLANNING • Any planning algorithm that can place two actions into a plan without specifying which comes first is called a Partial –order planner. • Solution is represented as a graph of actions, not a sequence. • Dummy actions called Start and Finish, which mark the beginning and end of the plan. • Every step of a plan is an action. • The partial order solution corresponds to six possible total order plans; each of these is called a linearization of the partial –order plan. • A Set of actions that make up the steps of the plan. The “empty” plan contains just the Start and Finish acitons.

POP Example & its linearization

PLANNING GRAPHS § A planning graph consists of a sequence of levels that corresponds to time steps in the plan, where level 0 is the initial state. § Planning graphs work only or propositional planning problems – ones with no variables.

GRAPH PLAN ALGORITHM

PLANNING METHODS • Sensorless Planning: Conformant Planning -The sensorless planning algorithm must ensure that the plan achieves the goal in all possible circumstances, regardless of the true initial state and the actual actions outcomes. • Conditional planning : Contingency planning ⁻ The agent plans first and then executes the plan that was produced . The agent finds out which part of the plan to execute by including sensing actions in the plan to test for the appropriate conditions.

PLANNING METHODS • Execution monitoring and Replanning: In this approach the agent can use any of the preceding techniques to construct a plan. Execution monitoring to judge whether the plan has a provision for the actual current situation or need to be revised. Replanning occurs when something goes wrong. • Continuous planning: A continuous planner is designed to persist over a life time. It can handle unexpected circumstances in the environment even if these occurs while the agent is in the middle of constructing a plan.

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