1. Jimma University,JiT Depatment of Computing Introduction To Artificial Intelligence Zelalem H. 2.

1

Jimma University,JiTDepatment of Computing

Introduction To Artificial Intelligence

Zelalem H.

2

Outline

(1)Introduction• What is AI?• Foundations of AI• State of the art in AI• History of AI (Reading assignment)

(2) Intelligent Agents• Agents and environments• Rationality• The Nature of environments• The structure of agents

3

1. Introduction

For thousands of years, we have tried to understand how we think!

AI, goes further still; it attempts not just to understand but also to build intelligent entities.

4

Introduction…

What is AI?Some possible definitions

• Thinking humanly Thinking rationally• Acting humanly Acting rationally

5

Introduction…

Thinking humanly• Cognitive science: the brain as an

information processing machine• Requires scientific theories of how the brain works

• How to understand cognition as a computational process? • Introspection: try to think about how we think

• Predict and test behavior of human subjects • Image the brain, examine neurological data

6

Acting humanly• The Turing Test

• What capabilities would a computer need to have to pass the Turing Test?• Natural language processing• Knowledge representation• Automated reasoning• Machine learning

Introduction…

7

Turing Test: Criticism• What are some potential problems with the Turing Test?• Some human behavior is not intelligent• Some intelligent behavior may not be human• Human observers may be easy to fool

• Chinese room argument: one may simulate intelligence without having true intelligence

• Is passing the Turing test a good scientific goal?

Introduction…

8

Introduction…

Thinking rationally• Idealized or “right” way of thinking• Logic: patterns of argument that always yield correct conclusions when supplied with correct premises “Socrates is a man; All men are mortal; Therefore Socrates is mortal.”

• Beginning with Aristotle, philosophers and mathematicians have attempted to formalize the rules of logical thought

9

Introduction…

Thinking rationally …• Logicist approach to AI: describe problem in formal logical notation and apply general deduction procedures to solve it

• Problems with the logicist approach• Computational complexity of finding the solution

• Describing real-world problems and knowledge in logical notation

• A lot of intelligent or “rational” behavior has nothing to do with logic

10

Introduction…

Acting rationally• A rational agent is one that acts to achieve the best outcome• Goals are application-dependent and are expressed in terms of the utility of outcomes

• Being rational means maximizing your expected utility

• This definition of rationality only concerns the decisions/actions that are made, not the cognitive process behind them

11

Introduction…

Acting rationally…• Advantages

• Generality: goes beyond explicit reasoning, and even human cognition altogether

• Practicality: can be adapted to many real-world problems

• Amenable to good scientific and engineering methodology

• Avoids philosophy and psychology

• Any disadvantages?• Not feasible in complicated envt’s • Computational demands are just too high

12

AI FoundationsPhilosophy

• Can formal rules be used to draw valid conclusions?

• How does the mind arise from a physical brain?

• Where does knowledge come from?• How does knowledge lead to action?

Mathematics• What are the formal rules to draw valid conclusions?

• What can be computed?• How do we reason with uncertain information?

13

AI Foundations…

Economics• How should we make decisions so as to maximize payoff?

• How should we do this when others may not go along?

Neuroscience How do brains process information?

Psychology How do humans and animals think and act?

14

AI Foundations…

Computer Engineering How can we build an efficient computer?

Control Theory and Cybernetics How can artifacts operate under their own control?

Linguistics How does language relate to thought?

15

The state-of-the-art

Robotic Vehicles Speech recognition Autonomous planning and scheduling Game playing Spam fighting, fraud detection Robotics Machine translation Vision

16

The History of Artificial intelligence

Reading Assignment From Page 16 to 28 Russell and Norvig, 3rd edition

17

2. Intelligent Agents

An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators

18

Agent function

The agent function maps from percept histories to actions

The agent program runs on the physical architecture to produce the agent function

agent = architecture + program

19

Vacuum-cleaner world

Percepts: Location and status, e.g., [A,Dirty]

Actions: Left, Right, Suck, NoOp

function Vacuum-Agent([location,status]) returns an action

if status = Dirty then return Suckelse if location = A then return Rightelse if location = B then return Left

20

Rational agents

For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and the agent’s built-in knowledge

Performance measure An objective criterion for success of an agent's behavior

21

Specifying the task environment

Problem specification: Performance measure, Environment, Actuators, Sensors (PEAS)

Example: automated taxi driver• Performance measure

– Safe, fast, legal, comfortable trip, maximize profits

• Environment– Roads, other traffic, pedestrians, customers

• Actuators– Steering wheel, accelerator, brake, signal, horn

• Sensors– Cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard

23

Agent: Part-sorting robot

Performance measure• Percentage of parts in correct bins

Environment• Conveyor belt with parts, bins

Actuators• Robotic arm

Sensors• Camera, joint angle sensors

24

Agent: Spam filter

Performance measure• Minimizing false positives, false negatives

Environment• A user’s email account

Actuators• Mark as spam, delete, etc.

Sensors• Incoming messages, other information about user’s account

25

Environment typesFully observable (vs. partially observable): The agent's sensors give it access to the complete state of the environment at each point in time

Deterministic (vs. stochastic): The next state of the environment is completely determined by the current state and the agent’s action

Episodic (vs. sequential): The agent's experience is divided into atomic “episodes,” and the choice of action in each episode depends only on the episode itself

26

Environment types

Static (vs. dynamic): The environment is unchanged while an agent is deliberating• Semidynamic: the environment does not change with the passage of time, but the agent's performance score does

Discrete (vs. continuous): The environment provides a fixed number of distinct percepts, actions, and environment states• Time can also evolve in a discrete or continuous fashion

27

Environment types

Single agent (vs. multi-agent): An agent operating by itself in an environment

Known (vs. unknown): The agent knows the rules of the environment

28

Examples

Chess w Clock without clock Taxi

Fully observableDeterministic Episodic StaticDiscreteSingle Agent

29

Yes Yes No

Strategic Strategic No

No No No

Semi Yes No

Yes Yes No

No No No

Hierarchy of agent types

Simple reflex agents Model-based reflex agents Goal-based agents Utility-based agents

30

Simple reflex agent

Select action on the basis of current percept, ignoring all past percepts

31

Model-based reflex agent

Maintains internal state that keeps track of aspects of the environment that cannot be currently observed

32

Goal-based agent

The agent uses goal information to select between possible actions in the current state

33

Utility-based agent

The agent uses a utility function to evaluate the desirability of states that could result from each possible action

34

A learning agent

to build learning machines and then to teach

them.

35

A learning Agent

learning element, which is responsible for making improvements, performance element, which is responsible for selecting external actions. The learning element uses feedback from the critic on how the agent is doing and determines how the performance element should be modified to do better in the future. problem generator: responsible

for suggesting actions that will lead to new and informative experiences

Questions?