IT Elect 104

(Chapter 3)

Some text and images in these slides were drawn from

Russel & Norvig’s published material

Problem Solving

Agent Function

Problem Solving Agent

* Agent finds an action sequence to achieve a goal

* Requires problem formulation

* Determine goal

* Formulate problem based on goal

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

Problem

* Initial state

* Possible actions / Successor function

* Goal test

* Path cost function

* State space can be derived from the initial state and the successor function

Example: Vacuum World

* Environment consists of two squares,

A (left) and B (right)

* Each square may or may not be dirty

* An agent may be in A or B

* An agent can perceive whether a square is dirty or not

* An agent may move left, move right, suck dirt (or do nothing) * Question: is this a complete PEAS description?

Vacuum World Problem

* Initial state: configuration describing

* location of agent

* dirt status of A and B

* Successor function

* R, L, or S, causes a different configuration

* Goal test

* Check whether A and B are both not dirty

* Path cost

* Number of actions

State Space

* 2 possible locations

x

2 x 2 combinations

( A is clean/dirty,

B is clean/dirty )

=

8 states

Sample Problem and Solution

* Initial State: 2

* Action Sequence:

Suck, Left, Suck

(brings us to which state?)

States and Successors

Example: 8-Puzzle

* Initial state:

as shown

* Actions?

successor function?

* Goal test?

* Path cost?

Example: 8-Queens Problem

* Position 8 queens on a chessboard so that no queen attacks any other queen * Initial state?

* Successor function?

* Goal test?

* Path cost?

Example: Route-finding

* Given a set of locations, links (with values) between locations, an initial location and a destination, find the best route * Initial state?

* Successor function?

* Goal test?

* Path cost?

Some Considerations

* Environment ought to be be static, deterministic, and observable * Why?

* If some of the above properties are relaxed, what happens? * Toy problems versus real-world problems

Searching for Solutions

* Searching through the state space

* Search tree rooted at initial state

* A node in the tree is expanded by applying successor function for each valid action * Children nodes are generated with a different path cost and depth * Return solution once node with goal state is reached

Tree-Search Algorithm

Search Strategy

* Strategy: specifies the order of node expansion

* Uninformed search strategies: no additional information beyond states and successors * Informed or heuristic search: expands “more promising” states Evaluating Strategies

* Completeness

* does it always find a solution if one exists?

* Time complexity

* number of nodes generated

* Space complexity

* maximum number of nodes in memory

* Optimality

* does it always find a least-cost solution?

Time and space complexity

Expressed in terms of:

* b: branching factor

* depends on possible actions

* max number of successors of a node

* d: depth of shallowest goal node

* m: maximum path-length in state space

Uninformed Search Strategies

* Breadth-First Search

* Uniform-Cost Search

* Depth-First Search

* Depth-Limited Search

* Iterative Deepening Search

Breadth-First Search

* fringe is a regular first-in-first-out queue

* Start with initial state; then process the successors of initial state, followed by their successors, and so on… * Shallow nodes first before deeper nodes

* Complete

* Optimal (if path-cost = node depth)

* Time Complexity:...