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CS.462 Artificial Intelligence. SOMCHAI THANGSATHITYANGKUL Lecture 02 : Search. Searching for Solutions. The state space model provides a formal definition of a problem and what constitutes a solution to a problem.

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## CS.462 Artificial Intelligence

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**CS.462Artificial Intelligence**SOMCHAI THANGSATHITYANGKUL Lecture 02 : Search**Searching for Solutions**• The state space model provides a formal definition of a problem and what constitutes a solution to a problem. • State space is the set of all states reachable from the initial state by any sequence of operators/actions. • Operators -- generate new states from existing states • A solution is • a state (called a goal state) whose attributes have certain properties and maybe • a sequence of operators that will change the initial state to a goal state • A solution is found by searching through the state space until a (goal) state with “specific” properties is found Chapter 1**What is a Solution in this type of Search?**• A solution to a search problem is a sequence of operators that generate a path from the initial state to a goal state. • An optimal solution is a minimal cost solution. • Solution cost versus search cost -- which one to optimize? Chapter 1**Three coins problem**Given three coins arrange as in the picture, which is the initial state I. These are goal state G.**Three coins problem**Operation : Flipping the coin one at a time Let A represent flipping the first coin Let B represent flipping the middle coin Let C represent flipping the last coin • The search problem: find a path from a state in I to a state in G. • Draw the state space Graph**Three coins problem**State space graph**Three coins problem**Let put some rule into the problem Rule : Use exactly 3 flips. This means that 1flip cannot reach the goal and 2 flips also cannot reach the goal. For example: state I : HHTc HHH not goal State I : HHT B HTT A TTT not goal Let draw the state space**Three coins problem**State space tree**Search**• Many AI problems can be formulated as search. • Iterative deepening is good when you don’t know much. First Method of Search • Uninformed Search**Depth First Search (DFS)**• Search • Put start state in the agenda • Loop Get a state from the agenda If goal, then return Expand state (put children in agenda) Avoiding loops Don’t add a node to the agenda if it’s already in the agenda Don’t expand a node (or add it to the agenda) if it has already been expanded.**DFS**Graph:**DFS**Agenda: • Expansion: put children at top of stack • Get new nodes from top of stack**O**S F Z A R B P D M T L C Try this Find a path from node A to the goal nod B. Use DFS method.**Breadth First Search (BFS)**• Search • Put start state in the agenda • Loop Get a state from the agenda If goal, then return Expand state (put children in agenda) Avoiding loops Don’t add a node to the agenda if it’s already in the agenda Don’t expand a node (or add it to the agenda) if it has already been expanded.**BFS**Graph:**BFS**Agenda: • Expansion: put children at end of queue • Get new nodes from the front of queue**O**S F Z A R B P D M T L C Try this Find a path from node A to the goal nod B. Use BFS method.

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