INTRODUÇÃO AOS SISTEMAS
INTELIGENTES
Prof. Dr. Celso A.A. Kaestner
PPGEE-CP / UTFPR
Agosto de 2011
Referências
• Stuart Russel e Peter Norvig, “Artificial
Intelligence, a Modern Approach”:
http://aima.cs.berkeley.edu
• Outras referências indicadas na proposta da
disciplina.
INTRODUÇÃO
O que é IA ?
O teste de Turing
O que é IA ?
• Abordagens:
– Simbólica:utiliza formalismos do tipo lógico para simular o
comportamento inteligente expresso através da
linguagem. Base para os sistemas especialistas.
– Conexionista: visa à modelagem da inteligência humana
através da simulação dos componentes do cérebro, isto é,
de seus neurônios, e de suas interligações. Base para as
Redes Neurais.
– Evolutiva: simula a evolução natural para encontrar
soluções para problemas complexos. Base para métodos
de otimização, como os Algoritmos Genéticos.
• História da IA, linha do tempo:
http://en.wikipedia.org/wiki/Artificial_intelligence
Pré-história da IA
• Philosophy
Logic, methods of reasoning, mind as physical
system foundations of learning, language,
rationality
• Mathematics
Formal representation and proof algorithms,
computation, (un)decidability, (in)tractability,
probability
• Economics
utility, decision theory
• Neuroscience physical substrate for mental activity
• Psychology
phenomena of perception and motor control,
experimental techniques
• Computer
building fast computers
engineering
• Control theory
design systems that maximize an objective
function over time
• Linguistics
knowledge representation, grammar
Resumo da História da IA
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1943
1950
1956
1952—69
1950s
• 1965
• 1966—73
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1969—79
1980-1986-1987-1995--
McCulloch & Pitts: Boolean circuit model of brain
Turing's "Computing Machinery and Intelligence"
Dartmouth meeting: "Artificial Intelligence" adopted
Look, Ma, no hands!
Early AI programs, including Samuel's checkers
program, Newell & Simon's Logic Theorist,
Gelernter's Geometry Engine
Robinson's complete algorithm for logical reasoning
AI discovers computational complexity
Neural network research almost disappears
Early development of knowledge-based systems
AI becomes an industry
Neural networks return to popularity
AI becomes a science
The emergence of intelligent agents
Estado da arte
• Deep Blue defeated the reigning world chess champion Garry
Kasparov in 1997
• Proved a mathematical conjecture (Robbins conjecture)
unsolved for decades
• No hands across America (driving autonomously 98% of the
time from Pittsburgh to San Diego)
• During the 1991 Gulf War, US forces deployed an AI logistics
planning and scheduling program that involved up to 50,000
vehicles, cargo, and people
• NASA's on-board autonomous planning program controlled
the scheduling of operations for a spacecraft
• Proverb solves crossword puzzles better than most humans
Agentes e ambientes
Agentes e ambientes
Agente racional
• 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 whatever built-in knowledge
the agent has.
Agentes e ambientes
Tipos de agentes
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Simple reflex agents
Model-based reflex agents
Goal-based agents
Utility-based agents
Agente reflexo simples
Agente reflexo baseado em modelos
Agente dirigido por objetivos
Agente baseado em utilidade
PEAS
• PEAS: Performance measure, Environment,
Actuators, Sensors
• Design of an 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
PEAS
• Agent: Medical diagnosis system
 Performance measure: Healthy patient,
minimize costs, lawsuits
 Environment: Patient, hospital, staff
 Actuators: Screen display (questions, tests,
diagnoses, treatments, referrals)
 Sensors: Keyboard (entry of symptoms,
findings, patient's answers)
Diferentes ambientes
• Fully observable (vs. partially observable): An 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 action executed by the agent. (If the environment is
deterministic except for the actions of other agents, then the
environment is strategic)
• Episodic (vs. sequential): The agent's experience is divided
into atomic "episodes" (each episode consists of the agent
perceiving and then performing a single action), and the
choice of action in each episode depends only on the episode
itself.
Diferentes agentes, diferentes
ambientes
• Static (vs. dynamic): The environment is unchanged
while an agent is deliberating. (The environment is
semidynamic if the environment itself does not
change with the passage of time but the agent's
performance score does)
• Discrete (vs. continuous): A limited number of
distinct, clearly defined percepts and actions.
• Single agent (vs. multiagent): An agent operating by
itself in an environment.