Introduction to Artificial Intelligence

1. Introduction to Artificial Intelligence AI definitions and History

3. What is AI? • 1950, Alan Turing (There was no term “AI” yet…) • Turing Test of Intelligence

4. What is AI? • 1955: “The goal of AI is to develop machines that behave as though they were intelligent.” • What is intelligence? What about Braitenberg vehicles?

5. What is AI? • 1991, Encyclopedia Britannica: “AI is the ability of digital computers or computer controlled robots to solve problems that are normally associated with the higher intellectual processing capabilities of humans.” • You mean like a calculator?

6. What is AI? • Elaine Rich: “Artificial Intelligence is the study of how to make computers do things at which, at the moment, people are better.” • Our author likes this definition. • Does it cover Neural Networks? Does it cover doing tasks that computers are already better at, like finding patterns?

7. What is AI? • Russell & Norvig:

9. Current Events in AI: MOBUSERV

10. Recognize people by their walk

11. Emotion Detection

12. Watson

13. Jokes • School of Informatics University of Edinburgh • I like my relationships like I like my source, open • I like my coffee like I like my war, cold • I like my boys like I like my sectors, bad

14. Back to the Turing Test • Chatbots are fun • http://www.elbot.com/

15. Agent Lingo • Agent: something that: • Perceives things about the world through sensors (inputs) • Acts in its environment through effectors or actuators (outputs) • Rational Agent: • An agent that does the right thing (vague enough for ya?) • Practically: an agent that has a proven basis for its actions

16. Recurring Theme in AI • We have a problem of Definitions. What does it mean to be rational, successful, thinking, intelligent? • Performance Measure: a function created to determine how successful an agent is in a particular environment. • Just like people, each agent has differing notions of success.

17. Performance Measure Examples • Be careful with defining success. • What is a performance measure for teaching? • Success = % of A’s in class • Success = % of happy students in class • Success = Level of difficulty of material • Success = % of students that go to grad school in AI • Success = ?

18. PEAS Descriptors • Way to define an AI problem. Define its: • Performance Measure • Environment • Actuators • Sensors

19. Properties of environments:Accessibility • Accessibility: How much knowledge of the world can be sensed. • Fully observable: Able to sense the complete state of the environment at each point in time. • Partially observable: Only part of the world is accessible

20. Properties of Environments:Agent Cardinality • Other entities are “agents” only if the performance measure depends on their behavior. • Single-agent: No other competing or helping agents • Multi-agent: Can be competitive or cooperative

21. Properties of environments:Determinism • Deterministic environments: the next world state is completely determined by the current world state and the agent’s actions. • Stochastic environments: Some probability is involved in determining the next state that the agent cannot control.

22. Properties of environments:Episodism • An episode is a perception-action pair. • An episodic environment means that the quality of an agent’s action depends only on its current state and action. • Sequential environments mean that the agent must be able to “think ahead” to determine the best action.

23. Properties of environments:Static vs Dynamic • A static environment stays the same while the agent is deliberating its next action. • A dynamic environment may change while the agent is deciding its next move. The world may need to be perceived during deliberation. • A semidynamic environment is one in which the world does not change while the agent is deliberating, but its performance score suffers.

24. Properties of environments:Discreteness • Discrete environments are made of a finite number of clearly defined percepts and actions. • Continuous environments have actions or precepts that are given in a continuous range. • Discrete and continuous can also refer to the agent’s view of time and space or its internal state.

25. Properties of Environments:Knowledge • Known environments: We understand the way the environment works, the “laws of physics” for the world • Unknown: Must first explore – what are the effects of my actions in this environment?

26. Environments • Fully vs Partially Observable • Deterministic vs Stochastic • Episodic vs Sequential • Static vs Dynamic • Discrete vs Continuous • Single vs Multiagent • Known vs Unknown

27. PEAS Descriptions • Part-picking robot • Chess player • Taxi driver • Crossword puzzle solver • Soccer-playing robot • Poker player • Backgammon player • Medical diagnosis expert system • English tutor • Facial recognition program

28. Types of Agents: Simple Reflex Agents • Agent senses the environment and chooses an action based on condition-action rules • Condition-action rules: (if-thens) Characterize the current input only, and choose action from a rule for that condition. • Not the same as a lookup table – may have multiple actions, do not need to delineate every possible input.

29. Types of Agents:Model-based Reflex Agent • Reflex agent + memory • Keep a history as you act in the environment • Use the internal state along with the current state of the environment to make decisions, still based on if-then rules.

30. Types of Agents:Goal-based Agents • Instead of having hard rules to follow, the agent has an idea of what the goal is and how its actions change its environment. • The agent chooses an action by searching the possible outcomes of each action. • This type of agent can act correctly when its goal changes.

31. Types of Agents:Utility-based Agents • Goals are overall binary distinctions: success or failure • Utility Function: A mapping from the state of the world (and internal state, if applicable) to a real number that represents a measure of “happiness”. • Allows the agent to take into account subgoals on the way to a main goal. • Examples: • Taxi-driver • Chess program

32. Types of Agents:Learning Agents • An agent gets feedback on how it is doing • It uses the feedback to decide how to change its own if-then rules and control mechanism • Finally, it must have a mechanism to promote exploration of new control strategies, even though they might not seem to be “optimal”

33. Next… • Now we can describe agents and their problem environments • How do we create the “smarts” of the agent’s control mechanism? • Logic…