Solving Problems by
Searching
Breadth-first search
Breadth-first search
Breadth-first search
completeif the shallowest goal node is at some finite depth d,
breadth-first search will eventually find it after generating all
shallower nodes (provided the br
INFERENCE IN
FIRST-ORDER LOGIC
PROPOSITIONAL VS. FIRST-ORDER INFERENCE
Suppose our knowledge base contains this,
x King(x) Greedy(x) Evil(x).
Then it seems quite permissible to infer any of the following sentences:
King(John)Greedy(John) Evil(John)
Kin
Lecture 1
D.R.V.L.B Thambawita
CST 351 3
Artificial Intelligent Systems
Assignments 60%
End Exam 40%
Outline
Course overview
What is AI?
A brief history
The state of the art
Course overview
Introduction to AI
Problem Solving by searching
Intelligent
KNOWLEDGE-BASED AGENTS
KNOWLEDGE-BASED AGENTS
To continue this chapter, THE WUMPUS WORLD will be used.
THE WUMPUS WORLD
THE WUMPUS WORLD - PEAS
Performance measure: +1000 for climbing out of the cave with the
gold, 1000 for falling into a pit or being e
Solving Problems by
Searching
Well-defined problems and solutions
A problem can be defined formally by five components:
Initial state
The initial state that the agent starts in
Ex: In(Arad)
Actions
A description of the possible actions available to
Solving Problems by
Searching
A* search
The most widely known form of best-first search is called A search
It evaluates nodes by combining g(n), the cost to reach the node, and
h(n), the cost to get from the node to the goal:
f(n)=g(n)+h(n)
g(n) gives t
Solving Problems by
Searching
SEARCHING FOR SOLUTIONS
A search tree with the initial state at the root; the branches are
actions and the nodes correspond to states in the state space of the
problem
leaf node, that is, a node with no children in the tree
FIRST-ORDER LOGIC
FIRST-ORDER LOGIC
FIRST-ORDER LOGIC
examples of objects, relations, and functions
Objects: people, houses, numbers, theories, Ronald McDonald, colors,
baseball games, wars, centuries.
Relations: these can be unary relations or propert
Application(Expert Systems)
Production systems
Production systems
An expert systems that use rule-based knowledge representation
Examples for production systems:
MYCIN,XCON, DENDRAL
Why rule based representation
(Production System)
Simple structure
PROPOSITIONAL THEOREM
PROVING
Inference and proofs
Inference and proofs
Inference and proofs
Inference and proofs
Inference and proofs
Inference and proofs
Inference and proofs
Inference and proofs
Inference and proofs
All of the logical equivalences can
INTELLIGENT AGENTS
After this lesion:
building successful agentssystems that can
reasonably be called intelligent.
describe a number of basic skeleton agent designs
AGENTS AND ENVIRONMENTS
An agent is anything that can be viewed as perceiving its
envir
Learning
Learning
An agent is learning if it improves its performance on future tasks
after making observations about the world
In this chapter we will concentrate on one class of learning problem
from a collection of inputoutput pairs, learn a functio