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Note for Artificial Intelligence - AI By ANNA SUPERKINGS

  • Artificial Intelligence - AI
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  • ANNA UNIVERISTY - HITECH
  • Information Technology Engineering
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function SIMPLE-REFLEX-AGENT( percept) returns action static: rules, a set of condition-action rules state <-INTERPRET-INPUT( percept) rule <-RULE-MATCH(state, rules) action <-RULE-ACTION[rule] return action CS6659 & Artificial Intelligence Unit I Page 3

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2.Agents that keep track of the world(Reflex Model) The simple reflex agent described before will work only if the correct decision can be made on the basis of the current percept. In order to choose an action sometimes INTERNAL STATE will help to take good decision. function REFLEX-AGENT-WITH-STATE( percept) returns action static: state, a description of the current world state rules, a set of condition-action rules state <-UPDATE-STATE(state, percept) rule <-RULE-MATCH(state, rules) action <-RULE-ACTION[rule] state <-UPDATE-STATE(state, action) return action 3. Goal-based agents Knowing about the current state of the environment is not always enough to decide what to do. In other words, as well as a current state description, GOAL the agent needs some sort of goal information, which describes situations that are desirable. 4. Utility Based Model: Goals alone are not really enough to generate high-quality behavior. For example, there are many action sequences that will get the taxi to its destination, thereby achieving the goal, but some are quicker, safer, more reliable, or cheaper than others. Goals just provide a crude distinction between “happy” and “unhappy” states, whereas a more general performance measure should allow a comparison of different world states. Then it has higher utility for the agent CS6659 & Artificial Intelligence Unit I Page 4

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Search: Formulate a problem as a state space search by showing the legal problem states, the legal operators, and the initial and goal states . • A state is defined by the specification of the values of all attributes of interest in the world • An operator changes one state into the other; it has a precondition which is the value of certain attributes prior to the application of the operator, and a set of effects, which are the attributes altered by the operator • The initial state is where you start • The goal state is the partial description of the solution State Space Search Notations The set of notations involved in the state space search is : 1) An initial state is the description of the starting configuration of the agent 2) An action or an operator takes the agent from one state to another state which is called a successor state. A state can have a number of successor states. 3) A plan is a sequence of actions. The cost of a plan is referred to as the path cost. Problem formulation & Problem Definition Problem formulation means choosing a relevant set of states to consider, and a feasible set of operators for moving from one state to another. Search is the process of considering various possible sequences of operators applied to the initial state, and finding out a sequence which culminates in a goal state. A search problem consists of the following: • S: the full set of states • s0 : the initial state • A:S→S is a set of operators • G is the set of final states. Note that G ⊆S The search problem is to find a sequence of actions which transforms the agent from the initial state to a goal state g∈G. CS6659 & Artificial Intelligence Unit I Page 5

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A simple search procedure is Do until a solution is found or the state space is exhausted. 1. Check the current state 2. Execute allowable actions to find the successor states. 3. Pick one of the new states. 4. Check if the new state is a solution state If it is not, the new state becomes the current state and the process is repeated Basic Search Algorithm is Let L be a list containing the initial state (L= the fringe) Loop if L is empty return failure Node <- select (L) if Node is a goal then return Node (the path from initial state to Node) else generate all successors of Node, and merge the newly generated states into L End Loop Factors to measure the search Algorithms: 1. Complete 2. Optimal 3. What is the search cost associated with the time and memory required to find a solution? a. Time complexity b. Space complexity The different search strategies that we will consider include the following: 1. Blind Search strategies or Uninformed search a. Depth first search b. Breadth first search c. Iterative deepening search d. Iterative broadening search 2. Informed Search 3. Constraint Satisfaction Search Blind search: Blind search or uninformed search that does not use any extra information about the problem domain. The two common methods of blind search are: • BFS or Breadth First Search • DFS or Depth First Search Search Tree is helpful for the searching the goal node. The terminologies involved in the search tree is Root Node: The node from which the search starts. Leaf Node: A node in the search tree having no children. Ancestor/Descendant: X is an ancestor of Y is either X is Y’s parent or X is an ancestor of the parent of Y. If S is an ancestor of Y, Y is said to be a descendant of X. CS6659 & Artificial Intelligence Unit I Page 6

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