Problem solver in Aje

 Essay in Problem Solving in Ai

Solving problems and Looking

IT Choose 104

(Chapter 3)

Some text and pictures in these photo slides were sucked from

Russel & Norvig's posted material

Solving problems

Agent Function

Problem Solving Agent

* Agent finds a task sequence to attain a goal

2. Requires problem formulation

* Determine aim

* Formulate problem based upon goal

* Searches for an action sequence that solves the problem * Activities are then carried out, disregarding percepts during that period


* Primary state

5. Possible actions / Heir function

5. Goal test

* Course cost function

* Express space could be derived from the initial state and the successor function

Example: Cleaner World

5. Environment includes two pieces,

A (left) and B (right)

5. Each sq may or may not be filthy

* A real estate agent may be in A or M

* An agent can understand whether a sq . is dirty or not

* A realtor may push left, maneuver right, pull dirt (or do nothing) * Issue: is this a whole PEAS description?

Vacuum World Problem

5. Initial state: configuration explaining

* position of agent

* dirt status of any and B

* Successor function

* R, M, or S, causes a unique configuration

* Goal test

* Verify whether A and B are not soiled

* Course cost

2. Number of actions

State Space

* two possible locations


2 x 2 combinations

( A is clean/dirty,

W is clean/dirty )


8 says

Sample Trouble and Solution

* Preliminary State: two

* Action Sequence:

Pull, Left, Pull

(brings all of us to which express? )

Says and Successors

Example: 8-Puzzle

* Primary state:

while shown

5. Actions?

replacement, beneficiary function?

2. Goal test?

* Path cost?

Case: 8-Queens Trouble

* Location 8 queens on a chessboard so that not any queen disorders any other queen * Preliminary state?

5. Successor function?

* Aim test?

2. Path expense?

Example: Route-finding

* Offered a set of spots, links (with values) between locations, an initial location and a vacation spot, find the best course * Initial state?

5. Successor function?

* Objective test?

* Path cost?

Some Factors

* Environment ought to be become static, deterministic, and observable * For what reason?

* In the event some of the over properties are relaxed, what happens? * Doll problems vs real-world problems

Searching for Alternatives

* Looking through the condition space

2. Search woods rooted at initial state

* A node in the tree is definitely expanded by applying successor function for each valid action 5. Children nodes are produced with a distinct path cost and depth * Come back solution once node with goal condition is reached

Tree-Search Algorithm

Search Technique

* Approach: specifies the order of node development

* Oblivious search strategies: no additional information beyond says and successors * Educated or heuristic search: extends " more promising” states Evaluating Tactics

* Completeness

* will it always find a solution if one exists?

* Time difficulty

* volume of nodes made

* Space complexity

2. maximum number of nodes in memory

2. Optimality

2. does it often find a least-cost solution?

As well as space complexity

Expressed when it comes to:

* m: branching component

* depends on possible activities

* utmost number of successors of a client

* m: depth of shallowest goal node

* m: optimum path-length in state space

Uninformed Search Strategies

2. Breadth-First Search

* Uniform-Cost Search

* Depth-First Search

* Depth-Limited Search

5. Iterative Deepening Search

Breadth-First Search

* fringe can be described as regular first-in-first-out queue

2. Start with first state; after that process the successors of initial express, followed by all their successors, so on… 5. Shallow nodes first before deeper nodes

* Complete

5. Optimal (if path-cost sama dengan node depth)

* Time Complexity:...

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 Fvwev Composition

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