CSE 473
Lecture 5
Heuristics
CSE AI Faculty
Last Time: A* Search
Use an evaluation function
f(n) for node n.
f(n) = estimated total cost
of path thru n to goal
Problem: Search for
shortest path from
start to goal
f(n) = g(n) + h(n)
g(n) = cost so far
CSE 473
Lecture 16
Markov Decision Processes (MDPs)
Part II
CSE AI faculty + Chris Bishop, Dan Klein, Stuart Russell, Andrew Moore
Recall: Markov Decision Processes
An MDP is defined by:
A set of states s S
A set of actions a A
A transition function
CSE 473
Lecture 4
Informed Search
CSE AI Faculty
Last Time
Blind (Uninformed) Search
Tree Search and Graph Search
BFS
UC-BFS
DFS
2
1
2
3
4
5
6
7
(using explored set)
8
(using explored set)
(using explored set)
9
(using explored set)
Space cost is a big a
CSE 473
Lecture 15
Markov Decision Processes (MDPs)
CSE AI faculty + Chris Bishop, Dan Klein, Stuart Russell, Andrew Moore
Course Overview: Where are we?
Introduction & Agents
Search and Heuristics
Adversarial Search
Logical Knowledge Representation
Mark
CSE 473
Lecture 13
Chapter 9
Reasoning with First-Order Logic
Chaining
Resolution
Compilation to SAT
CSE AI faculty
FOL Reasoning: Motivation
What if we want to use modus ponens?
Propositional Logic:
a b, a b c
c
In First-Order Logic?
x Monkey(x) Curio
CSE 473
Chapter 7
Inference Techniques for
Logical Reasoning
Recall: Wumpus World
Wumpus
You
(Agent)
2
Wumpusitional Logic
Proposition Symbols and Semantics:
Let Pi,j be true if there is a pit in [i, j].
Let Bi,j be true if there is a breeze in [i, j].
3
CSE 473
Lecture 11
Chapter 7
Inference in Propositional Logic
CSE AI faculty
Recall: Propositional Logic Terminology
Literal
= proposition symbol or its negation
E.g., A, A, B, B, etc. (positive vs. negative)
Clause
= disjunction of literals
E.g., (B C D
CSE 473
Artificial Intelligence (AI)
Rajesh Rao (Instructor)
Yi-Shu Wei (TA)
Hunter Whalen (TA)
http:/www.cs.washington.edu/473
Based on slides by UW CSE AI faculty, Dan Klein, Stuart Russell, Andrew Moore
Outline
Goals of this course
Logistics
What is AI
CSE 473
Chapter 3
Problem Solving using Search
First, they do an on-line search
CSE AI Faculty
Pac-Man as an Agent
2
1
The CSE 473 Pac-Man
Projects
Originally developed at UC Berkeley:
http:/www-inst.eecs.berkeley.edu/~cs188/pacman/pacman.html
3
Project
CSE 473
Lecture 7
Playing Games with Minimax and
Alpha-Beta Search
CSE AI Faculty
Today
Adversarial Search
Minimax recap
- search
Evaluation functions
State of the art in game playing
1
Recall: Game Trees
From current position, unwind game into the futu
CSE 473
Lecture 8
Adversarial Search: Expectimax
and Expectiminimax
Based on slides from CSE AI Faculty + Dan Klein, Stuart Russell, Andrew Moore
Where we have been and
where we are headed
Blind Search
DFS, BFS, IDS
Informed Search
Systematic: Uniform c
Lecture 2
Agents & Environments
(Chap. 2)
Based on slides by UW CSE AI faculty, Dan Klein, Stuart Russell, Andrew Moore
Outline
Agents and environments
Rationality
PEAS specification
Environment types
Agent types
Pac-Man projects
2
Agents
An agent is any
CSE 473
Chaps 4.1 & 5
Local Search and Games
CSE AI Faculty
Local search algorithms
What if path to goal is irrelevant? Only
interested in finding the goal state !
E.g., N-queens: Put N queens on an N N board with
no two queens on the same row, column,