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Understand how goal-directed agents achieve desired goals by selecting action sequences in state-space search problems. Learn problem formulation, search processes, and illustration of search problems like N-queen, 8-puzzle, and Tic-tac-toe.
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MH0023 INTELLIGENT CONTROLLERS LECTURE 5 STATE SPACE SEARCH
GOAL BASED AGENT • A GOAL DIRECTED AGENT NEEDS TO ACHIEVE CERTAIN GOALS. • MANY PROBLEMS CAN BE REPRESENTED AS SET OF STATES AND SET OF RULES OF HOW ONE STATE IS TRANSFORMED TO ANOTHER. • THE AGENT MUST CHOOSE A SEQUENCE OF ACTIONS TO ACHIEVE THE DESIRED GOAL.
GOAL BASED AGENT • INITIAL STATE- STARTING CONFIGURATION OF THE AGENT. • ACTION/OPERATOR-TAKES THE AGENT FROM ONE STATE TO ANOTHER.A STATE HAVE A NUMBER OF SUCCESSOR STATES. • PLAN-SEQUENCE OF ACTIONS. • GOAL-DESCRIPTION OF A SET OF DESIRABLE STATES OF THE WORLD. • PATH COST- FUNCTIONS THAT ASSIGNS A COST TO A PATH. • PROBLEM FORMULATION- CHOOSING A RELEVANT SET OF STATES TO CONSIDER AND A FEASIBLE SET OF OPERATORS MOVING FROM ONE STATE TO ANOTHER. • SEARCH-PROCESS OF IMAGINING SEQUENCES OF OPERATORS APPLIED TO THE INITIAL STATE AND CHECKING WHICH SEQUENCES REACHES A GOAL STATE.
REPRESENTING A SEARCH PROBLEM • A SEARCH PROBLEM IS REPRESENTED USING A DIRECTED GRAPH. • THE STATES ARE REPRESENTED AS NODES. • THE ALLOWED ACTIONS ARE REPRESENTED AS ARCS.
PEGS AND DISK PROBLEM INITIAL STATE GOAL STATE
EXPLICIT Vs IMPLICIT STATE SPACE • The state space may be explicitly represented by a graph. • The state space can be implicitly represented and generated when required. To generate the state space implicitly, the agent needs to know • The initial state. • The operators and a description of the effects of the operators.