Textbook Authors

Stuart Russell and Peter Norvig

Where do and have they worked? What are their main interests?

What is Artificial Intelligence?

  • Thinking
    • like a human
    • rationally---optimally, or the best possible
  • Acting
    • like a human
    • rationally

Thinking Like a Human

How do humans think?

Cognitive Science is relatively new field that is trying to answer this question.

Acting Like a Human

In the study of "intelligence", many aspects are missed if studied in isolation of human or robot bodies.

A true "artificial intelligence" must be capable of interacting with its world.

Turing Test: a computer is intelligent if a human interrogator, after posing some written questions, cannot tell whether the written responses come from a person or from a computer.

xkcd version and another one (thanks Brock Wilcox)

Thinking Rationally

Rationality: doing the right thing, given what is known.

Logical reasoning systems.

Problems:

  • hard to write informal knowledge as logical statements
  • solving most logical reasoning problems currently takes too much time or memory.

Acting Rationally

Agent: computer programs that

  • operate autonomously
  • perceive their environment
  • persist over long time periods
  • adapt to change
  • create and pursue goals

Rational agent: an agent that acts to achieve the best outcome, or best average outcome if the agent has incomplete knowledge

Russell and Norvig take the view of rational agents in describing data structures and algorithms.

Early

Recent

Intelligent Agents

An agent "perceives its environment through sensors and acts upon that environment through actuators."

An agent's choice of action can depend on the entire history of percepts observed previously, but not on anything it has not perceived.

Rationality

But, which action to choose? A rational agent is one that does the "right thing", which depends on the performance measure.

The performance measure should be designed to reflect what one actually wants in the environment, rather than how one suspects the agent should behave. Define it in terms of effects of actions on the environment, rather than in terms of the agent's program.

Rational behavior is not perfect, because an agent cannot know everything about the environment, including past, present, and future. We focus on maximizing expected performance, given what we know about probabilities of things happening in the environment.

Nature of Environments

Specify the task environment (PEAS):

  • P: performance measure
  • E: environment
  • A: agent's actuators
  • S: agent's sensors
Agent Types Performance Measure Environment Actuators Sensors
medical diagnosis system healthy patient, reduced costs patient, hospital, staff display of questions, tests, diagnoses, treatments, referrals
satellite image analysis system correct image categorization downlink from orbiting satellite display of scene categorization color pixel arrays
part-picking robot percentage of parts in correct bins conveyor belt with parts; bins jointed arm and hand camera, joint angle sensors
refinery controller purity, yield, safety refinery, operators valves, pumps, heaters, displays temperature, pressure, chemical sensors
interactive English tutor student's score on test set of students, testing agency display of exercies, suggestions, corrections keyboard entry
Task Environment Observable Agents Deterministic Episodic Static Discrete
crossword puzzle fully single deterministic sequential static discrete
chess with clock fully multi deterministic sequential semi discrete
poker partially multi stochastic sequential static discrete
backgammon fully multi stochastic sequential static discrete
taxi driving partially multi stochastic sequential dynamic continuous
medical diagnosis partially single stochastic sequential dynamic continuous
image analysis fully single deterministic episodic semi continuous
part-picking robot partially single stochastic episodic dynamic continuous
refinery controller partially single stochastic sequential dynamic continuous
interactive English tutor partially multi stochastic sequential dynamic discrete

Structure of Agents

Reflex agents

Model-based reflex agents

Goal-based agents

Utility-based agents

Using learning to modify each of the above.