The document discusses various approaches to artificial intelligence (AI), focusing on understanding and replicating human thought processes. It outlines key methodologies such as the Turing Test for assessing intelligence, cognitive modeling for studying human reasoning, and the rational agent approach for autonomous decision-making. It highlights challenges and advantages in translating informal knowledge into formal logical notation, along with the limitations of rationality in practical scenarios.

1. ARTIFICIAL INTELLIGENCE
Not just understand how we think, also built intelligent entities

2. DEFINITION
Thinking Humanly
• Effort to make machines with
minds.
• Automation of activities as
decisión making, problema
solving, learning.
Thinking Rationally
• Study of mental faculties with
computational models.
• Study of computation to make it
posible to perceive, reason and
act.
Acting Humanly
• Creation of machines that
performe functinons that requiere
intelligence.
• How to make computers do things
at which people are better.
Acting Rationally
• Study of the design of intelligent
agents.
• Concerned with intelligent
behavior in artefacts.

3. ACTING HUMANLY
THETURINGTEST APPROACH
 TheTuringTest: to provide a satisfactory operational definition of intelligence
 A human interrogator, after posing some write questions, cannot tell wheter the
responses come from a person or a computer.
 Physical stimulation of a person is unnecessary for intelligence
 TotalTuring test: includes a video signal
 Test the subject’s perceptual abilities

4. Disciplines
AI
Natural
language
processing
Knowledge
representation
Automated
reasoning
Machine
learning
Computer
Vision
Robotics

5. THINKING HUMANLY
THE COGNITIVE MODELING APPROACH
 Determine how humans think through
 Introspection
 Psychological experiments
 Brain imaging
 Cognitive science
 Brings together computer models from AI and experimental techniques from
psychology to constructe precise and testable theories of the human mind
 “an algorithm performs well on a task and that it is therefore a good model of
human performance, or viceversa the cognitive modeling approach”

6. THINKING RATIONALLY
THE “LAWS OFTHOUGHT” APPROACH
 Syllogism: patterns of argument structures that always yielded correct
conclusions when given correct premises
 Govern the operation of mind
 Logic: study of syllogisms
 Logicist tradition within AI hopes to build programs… to create intelligent systems
 Solve any solvable problem described in logical notation (if no solution exists, the
program might loop forever)
 Obstacles
 It’s not easy to take informal knowledge and state it in the formal terms required by logical
notation
 There is a big difference between solving a problema “in principle” and solving it in practice

7. ACTING RATIONALLY
THE RATIONAL AGENT APPROACH
 Agent: something that acts
 Computer agent
 Operate autonomously
 Perceive its environment
 Adapt to change
 Create and persue goals
 Rational agent
 Acts to achieve the best outcome or the best expected outcome (when there is uncertainty)
 Making correct inferences is sometimes part of being a rational agent, but it’s not all of rationallity, sometimes there is no
correct thing to do, but something must still be donde.
 Advantages
 More general than laws of thought
 More amenable to scientific approaches
 The standard of rationality is mathematically well defined and completely general and can be “unpacked”
 Limited rationallity: acting appropriately when there is not enough time to do all the computations one might like