1. A N I NTRODUCTION TO A RTIFICIAL I NTELLIGENCE AND I NTELLIGENT A GENTS Research Project By Basim Alsalmi

2. O UTLINE Concise Review of the Project Proposal Introduction to AI Intelligent Agents References

3. I NTRODUCTION TO A RTIFICIAL I NTELLIGENCE (AI) What Is AI? The Foundations of Artificial Intelligence The History of Artificial Intelligence The State of the Art

4. W HAT IS AI? [1]

5. Acting Humanly: The Turning Test Approach This test is designed to give a definition of intelligence. The test examines if a computer can figure out whether a response came from a person or a computer. When a computer gives a response and a human can't tell whether it came from a human or a computer, the computer passes the test. To pass, six disciplines are required which compose most of Al: natural language processing, knowledge representation, automated reasoning, machine learning, computer vision, and robotics.

6. Thinking Humanly: The Cognitive Approach It requires scientific theories of the inside activities of the brain. If we are going to say that a given program thinks like a human, we must have some way of determining how humans think. There are three ways to do this: 1- through introspection 2- through psychological experiments 3- through brain imaging [2]

7. Thinking Rationally: Laws of Thought It is normative but not descriptive. For example, “Socrates is a man; all men are mortal; therefore Socrates is mortal.” Obstacles: 1- it is not easy to take informal knowledge and state it in the formal terms. 2- there is a big difference between being able to solve a problem “in principle” and doing so in practice. [1]

8. Acting Rationally: The Rational Agent Approach An agent is just something that acts (agent comes from the Latin agere, to do). All computer programs do something, but computer agents are expected to do more. merits : 1) it is more general than the “laws of thought” approach. 2) it is more adjustable to scientific development. [1]

9. The Foundation of Artificial Intelligence Disciplines that contributed ideas, viewpoints, and techniques to AI: Philosophy: philosophical picture of the mind is the connection between knowledge and action. This question is vital to Al because intelligence requires action as well as reasoning; logic, methods of reasoning, foundations of learning, and rationality. Mathematics: formal representation and proof, algorithms, tractability, and probability. Economics: Decision theory and good enough decisions

10. The Foundation of Artificial Intelligence Neuroscience: mental activity. Psychology: adaptation, phenomena of perception, and experimental techniques. Computer engineering: artifact that makes AI applications possible such as advances in speed and memory. Control theory: homeostatic systems and stability Linguistics: knowledge representation and grammar.

11. The History of Artificial Intelligence [3]

12. The History of Artificial Intelligence The gestation of artificial intelligence (1943-1955) The birth of artificial intelligence (1956) Early enthusiasm, great expectations (1952-1969) Knowledge-based systems (1969 - 1979) AI becomes an industry (1980-present) The return of neural networks (1986-present) AI adopts the scientific method (1987-present) The emergence of intelligent agents (1995-present) The availability of very large data sets (2001-present) [5]

13. State of The Art What can AI do today? o Robotic vehicles. o Speech recognition. o Autonomous planning and scheduling. o Game playing. o Spam fighting. o Logistic planning. o Robotics. o Translation.

14. I NTELLIGENT A GENTS Agents and environments Rationality The nature of environments Agent Types

15. A GENTS AND E NVIRONMENTS An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators. [7]

16. A GENTS AND E NVIRONMENTS Software agent: o receives keystroke, file contents, and network packets as sensor inputs o acts on the environment by displaying on the screen, writing files, and sending network packets. ActuatorsSensorsAgent hands, legs, vocal tract, and other body parts eyes, ears, and other organs Human agent various motorscameras and infrared range finders Robotic agent

17. A GENTS AND E NVIRONMENTS [1]

18. A GENT F UNCTION [ f : P*  A ] an agent is a function from percept histories to actions The goal is to find a way to implement the rational agent function concisely

19. V ACUUM C LEANER W ORLD E XAMPLE Precept sequenceAction [A, Clean]Right [A, Dirty]Suck [B, Clean]Left [B, Dirty]Suck [1]

20. R ATIONAL A GENTS A rational agent is one that does the right thing(action). The right action is the one that will cause the agent to be most successful Performance measure: An objective judgment for success of an agent's behavior o i.e. performance measure of a vacuum-cleaner agent could be the amount of dirt cleaned up, amount of time taken, amount of electricity consumed, amount of noise generated, etc.

21. R ATIONAL A GENTS Rational Agent : A rational agent should select an action, for each possible percept sequence, that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built- in knowledge the agent has.

22. R ATIONAL A GENTS Agents can perform actions in order to modify future percepts and to obtain useful information o information gathering and exploration An agent is autonomous if its behavior is determined by its own experience o with ability to learn and adapt

23. T HE N ATURE OF E NVIRONMENTS There are four terms, for any agent, that have to be identified. PEAS: ( P erformance, E nvironment, A ctuators, S ensors). Example: [1] [6]

24. E NVIRONMENTS T YPES environmentVSenvironment partially observable Fully observable multiagentSingle agent stochasticDeterministic sequentialEpisodic dynamicStatic continuous Discrete unknownKnown

25. T HE S TRUCTURE OF A GENTS Agent Program The job of AI is to design the agent program which is a function that implements the agent mapping from percepts to actions. The program is assumed that it will run on some sort of computing device with physical sensors and actuators which is called architecture. agent = architecture + program

26. T HE S TRUCTURE OF A GENTS A trivial agent program that keeps track of the precept sequence and just looks up the best action. [1]

27. T HE S TRUCTURE OF A GENTS The four basic kinds of agent programs o Simple reflex agents o Model-based reflex agents o Goal-based agents o Utility-based agents

28. S IMPLE R EFLEX A GENT Figure gives the structure of this general program in schematic form, showing how the condition-action rules allow the agent to make the connection from percept to action. [1]

29. M ODEL -B ASED R EFLEX A GENT Figure gives the structure of the model-based reflex agent with internal state, showing how the current percept is combined with the old internal state to generate the updated description of the current state, based on the agent's model of how the world works. [1]

30. G OAL -B ASED A GENTS It keeps track of the world stale as well as a set of goals it is trying to achieve, and chooses an action that will (eventually) lead to the achievement of its goals. [1]

31. U TILITY -B ASED A GENT It uses a model of the world, along with a utility function that measures its preferences among states of the world. Then it chooses the action that leads to the best expected utility, where expected utility is computed by averaging over all possible outcome states, weighted by the probability of the outcome. [1]

32. R EFERENCES [1] Stuart, Rand Peter, N. Artificial Intelligence : A modern approach. Third edition, 2009. [2] Stuart, R and Peter, N (1995). Artificial Intelligence: A Modern Approach. Retrieved Oct 10, 2012, from http://aima.cs.berkeley.edu/contents.html. http://aima.cs.berkeley.edu/contents.html [3] Intro to Artificial Intelligence. Retrieved Nov 26, 2012, from http://library.thinkquest.org/2705/basics.html http://library.thinkquest.org/2705/basics.html [4] An Introduction to the Science of Artificial Intelligence. Retrieved Nov 26, 2012, from http://www.cs.brown.edu/courses/cs141/lectures/01.pdf http://www.cs.brown.edu/courses/cs141/lectures/01.pdf [5] Bruce G. Buchanan. Timeline: A brief history of artificial intelligence. http://aaai.org/AITopics/BriefHistory, 2012.http://aaai.org/AITopics/BriefHistory [6] Wikipedia. Agent. http://en.wikipedia.org/wiki/Agent, 2012.http://en.wikipedia.org/wiki/Agent [7] Stuart J. Russell and Peter Norvig. Chapter 2 slides. http://ima.cs.berkeley.edu/slides-pdf/chapter02.pdf, 1998. http://ima.cs.berkeley.edu/slides-pdf/chapter02.pdf

33. Q UESTIONS ?