Download Chapter 1(ppt) - Ahmad Falah Aljaafreh, Ph.D.

AI
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Dr. Ahmad aljaafreh
What is AI?
“AI” can be defined as the simulation of human intelligence on a
machine, so as to make the machine efficient to identify and use the right
piece of “Knowledge” at a given step of solving a problem.
A system capable of planning and executing the right task at the right
time is generally called rational.
What is AI?
subject dealing with computational models that can think and act
rationally.
Does rational thinking and acting include all possible
characteristics of an intelligent system?
learning, perception and planning.
General Problem Solving
Approaches in AI
state : status of the solution at a given step of the problem solving
Procedure.
The problem solving procedure applies an operator to a state to get the
next state.
transition to the next state, thus, is continued until the goal (desired) state is
derived. Such a method of solving a problem is generally referred to as statespace approach.
Example
4-puzzle problem
two operations, blank-up (BU) / blank-down (BD) and blank-left (BL) / blank-right (BR)
state-space
Algorithm for solving state-space problems
Algorithm for solving state-space problems
Begin
1. state: = initial-state; existing-state:=state;
2. While state ≠ final state do
Begin
a. Apply operations from the set {BL, BR, BU,
BD} to each state so as to generate new-states;
b. If new-states ∩ the existing-states ≠ φ
Then do
Begin state := new-states – existing-states;
Existing-states := existing-states ∪ {states}
End;
End while;
End.
trick in solving problems by the state-space Approach
1- determine the set of operators
2- use it at appropriate states of the problem.
AI problems and non-AI problems.
Generally, problems, for which straightforward mathematical /
logical algorithms are not readily available and which can be solved by
intuitive approach only, are called AI problems.
Examples:
water-jug problem, Travelling Salesperson, diagnosis problems, and pattern
classification.
The key to AI approach is intelligent search and matching. In an intelligent
search problem / sub-problem, given a goal (or starting) state, one has to
reach that state from one or more known starting (or goal) states
how to control the generation of states?
Some of the well-known search algorithms are:
a) Generate and Test
b) Hill Climbing
c) Heuristic Search
d) Means and Ends analysis
(1) Generate and Test Approach:
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Generation of the state-space from a known starting state (root)
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Continues expanding the reasoning space until the goal node
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After generation of each and every state, the generated node is
compared with the known goal state.
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When the goal is found, the algorithm terminates.
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Does not allow filtering of states.
(2) Hill Climbing Approach:
-total cost for reaching the goal from the given starting state (f).
- While f ≤ a predefined utility value and the goal is not reached,
new nodes are generated of the current node.
-in case all the neighborhood nodes (states) yield an identical
value of f and the goal is not included in the set of these nodes,
the search algorithm is trapped
-to overcome this problem is to select randomly a new starting
state and then continue.
Example: trigonometric identities proof.
(3) Heuristic Search:
- use one or more heuristic functions to determine
the better candidate states among a set of legal states.
-The heuristic function measures the fitness of the candidate states.
(d) Means and Ends Analysis:
-attempts to reduce the gap between the current state and the goal state.
-measure the distance between the current state and the goal, and then
apply an operator to the current state, so that the distance between the
resulting state and the goal is reduced.
Example: mathematical theorem- proving processes.
Good general problem solving techniques in AI:
-Problem Decomposition
-Constraint Satisfaction:
evaluate the variables X1, X2 and X3 from the following set of constraints:
{ X1 ≥ 2; X2 ≥3 ; X1 + X2 ≤ 6; X1 , X2 , X3 ∈ I }.
The Disciplines of AI
Topics which we find significant and worthwhile to understand
Learning Systems:
Knowledge Representation and Reasoning:
Knowledge Acquisition:
Intelligent Search:
Logic Programming:
Soft Computing:
Fuzzy, Neural, Genetic
Applications of AI Techniques
- Expert Systems:
consists of a knowledge base, database and an inference
engine
- Image Understanding and Computer Vision:
- Navigational Planning for Mobile Robots:
camera or ultrasonic sensors
static and dynamic environments
- Speech and Natural Language Understanding:
- Intelligent Control & Scheduling:
A Brief History of AI
Professor Peter Jackson of the University of Edinburgh classified the history
of AI into three periods namely i) the classical period (of game playing and
theorem proving), ii) the romantic period, and iii) the modern period the
major research work carried out during these periods is presented below.
The Classical Period
This period dates back to 1950. The main research works carried out
during this period include game playing and theorem proving. The
concept of statespace approach for solving a problem, which is a
useful tool for intelligent problem-solving even now, was originated
during this period.
Turing’s “test”, which is a useful tool to test machine intelligence,
originated during this period.
The Romantic Period
The romantic period started from the mid 1960s and continued until the mid
1970s. During this period, people were interested in making machines
“understand”, by which they usually mean the understanding of natural
languages.
The Modern Period
The modern period starts from the latter half of the 1970s to the present day.
This period is devoted to solving more complex problems of practical
interest.