PLANNING IN AI. Determine the set of steps that are necessary to achieve a goal Some steps might be conditional, i.e., they are only taken when a set.

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PLANNING IN AI

Determine the set of steps that are necessary to achieve a goal Some steps might be conditional, i.e., they are only taken when a set of conditions is present during plan execution.

TYPICAL PLANNING PROBLEMS * Representing and reasoning about time, causality, and intentions Physical and other kinds of constraints on acceptable solutions Uncertainty in the execution of plans How the “real world” is sensed and perceived Multiple agents who may cooperate or interfere * Lucci, Stephen; Kopec, Danny: Artificial Intelligence in the 21st Century

TERMINOLOGY Operator Schema The sequence of steps followed to solve a planning problem Characterize actions/events

STRIPS (STANFORD RESEARCH INSTITUE PROBLEM SOLVER) Operator Schema Consist of: Precondition Delete List Add List Variables in the Precondition, Delete List, and Add List that are bound at run-time Example: Pickup(X) Precondition: OnTable(X) ^ HandEmpty ^ Clear(X) Delete List: OnTable(X) HandEmpty Clear(X) Add List: Holding(X)

SOME TYPES OF PLANNING Hierarchical Planning Goals are not equally valued Some might be necessary Others might be desirable, but not necessary Numerical values provide hierarchical value Opportunistic Planning Exploit the conditions in a plan state to more easily achieve a goal Conditional Planning Planning based on things that “might happen.” Partial Order Planning Not all steps in the plan must be made in order

PLANNING STRATEGIES Means-Ends Analysis Reduce the distance between the current state and the goal state (GPS – Newell and Simon) Least Commitment Planner Only commit to a plan when forced by constraints Can maintain multiple/flexible “parallel” plans until forced to select a concrete alternative Ordering of steps is deferred as long as possible Depth-First Backtracking (“lifting”) Generates combinations of steps until one that works is found Impractical for large plans (exponential time)

PLANNING STRATEGIES (CONTINUED) Beam Search Expand the top few (beam width) nodes at each level of a breadth-first search One-The-Best Backtracking Rely on local information to determine a best-guess path, then backtrack – looking for good alternative candidates along the path Dependency-Directed Search Store dependencies between decisions, assumptions, and alternatives for each On failure, keep parts of the solution that are not dependent of the cause of failure

PLANNING STRATEGIES (CONTINUED) Opportunistic Search Favor the most constrained operations Meta-level Plans Plans about plans Techniques of planning can be selected dynamically based on the type of problem at hand. Distributed Planning Different parts (subplans) of the plan are developed by different “expert” components of the system

GRAPHPLAN Graph builds a graph starting from the initial state Uses “layered” or “parallel” plans, where each layer provides a set of actions that can be performed simultaneously. Mutually exclusive actions are identified at the level where they first occur. Builds a graph where each node represents the state after all possible transitions from the previous state. Solutions are found by working backward from the final node GraphPlan can also be used to generate heuristics for A* search http://web.engr.oregonstate.edu/~afern/classes/cs533/ notes/graphplan.pdf http://web.engr.oregonstate.edu/~afern/classes/cs533/ notes/graphplan.pdf

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