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

3. Outline (1)Introduction (2) Intelligent Agents 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

4. 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.

5. Introduction… What is AI? Some possible definitions
Thinking humanly Thinking rationally
Acting humanly Acting rationally

6. 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

7. Introduction… 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

8. Introduction… 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?

9. 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

10. 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

11. 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

12. Introduction… Acting rationally… Any disadvantages? 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

13. AI Foundations Philosophy Mathematics
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?

14. AI Foundations… Economics Neuroscience Psychology
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?

15. 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?

16. The state-of-the-art Robotic Vehicles Speech recognition
Autonomous planning and scheduling
Game playing
Spam fighting, fraud detection
Robotics
Machine translation
Vision

17. The History of Artificial intelligence
Reading Assignment
From Page 16 to 28
Russell and Norvig, 3rd edition

18. 2. Intelligent Agents
An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators

19. 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

20. 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 Suck
else if location = A then return Right
else if location = B then return Left

21. 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

22. What does rationality mean?
Rationality is not omniscience
Percepts may not supply all the relevant information
Consequences of actions may be unpredictable
An agent is autonomous if its behavior is determined by its own experience (with ability to learn and adapt)

23. 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

24. 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

25. Agent: Spam filter Performance measure Environment Actuators Sensors
Minimizing false positives, false negatives
Environment
A user’s account
Actuators
Mark as spam, delete, etc.
Sensors
Incoming messages, other information about user’s account

26. Environment types
Fully 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

27. Environment types Static (vs. dynamic): Discrete (vs. continuous):
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

28. 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

29. Examples Fully observable Deterministic Episodic Static Discrete
Chess w Clock without clock Taxi
Fully observable
Deterministic
Episodic
Static
Discrete
Single Agent
Yes
Yes No
Strategic No
Strategic No No No
Semi
Yes No
Yes
Yes No No No No

30. Hierarchy of agent types
Simple reflex agents
Model-based reflex agents
Goal-based agents
Utility-based agents

31. Simple reflex agent
Select action on the basis of current percept, ignoring all past percepts

32. Model-based reflex agent
Maintains internal state that keeps track of aspects of the environment that cannot be currently observed

33. Goal-based agent
The agent uses goal information to select between possible actions in the current state

34. Utility-based agent
The agent uses a utility function to evaluate the desirability of states that could result from each possible action

35. A learning agent
to build learning machines and then to teach them.

36. learning element, which is responsible for making improvements,
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

37. Questions?