Introduction to Intelligent Systems

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Questions and Answers

What is a key component of the Turing Test?

  • Ability to process natural language (correct)
  • Physical strength of machines
  • Speed of computation
  • Size of the memory storage

Which of the following best describes artificial intelligence?

  • Creating machines that perform human-like functions (correct)
  • Developing new programming languages
  • The automation of natural disasters
  • Building larger computing devices

What does the term 'acting rationally' refer to in the context of AI?

  • Rational agents displaying intelligent behavior (correct)
  • Performing actions without any reasoning
  • Machines following human emotional responses
  • Reproducing human thoughts exactly

Which area of study focuses on understanding how the brain works in the context of AI?

<p>Cognitive science (D)</p> Signup and view all the answers

What aspect of human behavior does AI seek to replicate?

<p>Intelligent decision-making and problem solving (A)</p> Signup and view all the answers

Which of these options is NOT associated with the definition of AI?

<p>Performing routine manual tasks (B)</p> Signup and view all the answers

What does the concept of 'thinking rationally' involve in AI?

<p>Utilizing Aristotle’s syllogisms (B)</p> Signup and view all the answers

Which statement best aligns with the goals of cognitive science in AI?

<p>To validate theories of human thought and behavior (D)</p> Signup and view all the answers

What does acting rationally in the context of AI imply?

<p>Achieving the best expected outcome based on perceived information (A)</p> Signup and view all the answers

Which of the following was an early program developed during the inception of AI?

<p>General Problem Solver (C)</p> Signup and view all the answers

What marked the period known as 'The AI winter'?

<p>An overestimation of AI capabilities leading to criticism (D)</p> Signup and view all the answers

Who authored the paper titled 'Computing Machinery and Intelligence'?

<p>Alan Turing (B)</p> Signup and view all the answers

Which AI development is associated with the concept of Hebbian learning?

<p>Neural networks (B)</p> Signup and view all the answers

What was a significant outcome of the Dartmouth workshop in 1956?

<p>Inspiration for modern AI research (A)</p> Signup and view all the answers

Which of the following was one of the first commercial expert systems?

<p>RI (D)</p> Signup and view all the answers

What limitation was noted regarding early AI systems in complex problem performance?

<p>They lacked funding due to unrealistic expectations (B)</p> Signup and view all the answers

What is a significant advancement in AI during the 1980s?

<p>Back-propagation algorithm (A)</p> Signup and view all the answers

Which of the following is a technique used in probabilistic reasoning?

<p>Hidden Markov Models (D)</p> Signup and view all the answers

What allowed the rise of AI applications during the 2000s?

<p>Emergence of very large datasets (A)</p> Signup and view all the answers

What characterizes deep learning models introduced after 2011?

<p>Multiple layers of simple, adjustable computing elements (B)</p> Signup and view all the answers

Which component is NOT part of the definition of an intelligent agent?

<p>Random decision making (C)</p> Signup and view all the answers

What is essential for evaluating an intelligent agent's performance?

<p>The degree to which it attains its goals (C)</p> Signup and view all the answers

What type of data has AI been significantly able to process since the 2000s?

<p>Large scale, unstructured data (C)</p> Signup and view all the answers

Bayesian networks are important for which aspect of AI?

<p>Uncertain knowledge representation (D)</p> Signup and view all the answers

What distinguishes reactive agents from other types of intelligent agents?

<p>Their actions depend solely on current percepts. (D)</p> Signup and view all the answers

Which of the following characteristics describes a dynamic environment?

<p>The environment can change while the agent is deliberating. (D)</p> Signup and view all the answers

What is the main function of information gathering in intelligent agents?

<p>To gather data for immediate action selection. (D)</p> Signup and view all the answers

Which type of intelligent agent focuses on maximizing expected utility of action outcomes?

<p>Utility-based agents (C)</p> Signup and view all the answers

What key aspect differentiates a model-based agent from a rule-based agent?

<p>Model-based agents utilize internal states to inform behavior. (D)</p> Signup and view all the answers

In which scenario would a competitive multiagent environment be observed?

<p>Agents have conflicting goals and compete for resources. (A)</p> Signup and view all the answers

Which evaluation aspect is NOT typically considered in intelligent agent performance evaluation?

<p>Cost of agent creation (A)</p> Signup and view all the answers

What distinguishes a stochastic environment from a deterministic environment?

<p>Stochastic environments involve random outcomes. (C)</p> Signup and view all the answers

Flashcards

What is Artificial Intelligence?

The art of creating machines that can perform tasks requiring human intelligence, such as problem-solving and decision-making.

Turing Test

A test used to determine if a machine can exhibit intelligent behavior, specifically by mimicking human conversation.

Thinking humanly

The study of computational processes involved in perception, reasoning, and action, with a focus on how humans think.

Cognitive Science

A field that investigates how human intelligence works by studying mental processes and behavior.

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Neuroscience

The study of the nervous system, with a particular focus on the brain and how it supports cognitive functions.

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Acting rationally

A method for achieving intelligent behavior in artifacts by designing agents that can reason rationally and choose actions based on their goals.

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Rational agent

An entity that can perceive its environment, reason about its actions, and act autonomously to achieve its goals.

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Automation of human activities

The use of AI to automate tasks that typically require human intelligence, like language understanding or object recognition.

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Thinking rationally

The ability of a machine to think and reason like a human. It involves using logical principles to arrive at sound conclusions.

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Logic

A system for formalizing and codifying correct thinking. It involves using precise symbols and rules to represent and manipulate information.

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Agent

An entity that can perceive information from its environment and take actions based on that perception.

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Mathematics

The branch of mathematics that deals with the study of abstract structures and their properties. It is crucial for understanding and developing AI systems.

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Artificial Intelligence (AI)

The use of computer programs to simulate human intelligence. It encompasses diverse tasks like solving problems, understanding language, and learning.

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Expert systems

A type of computer program designed to solve specific problems in specialized domains. They utilize rules and knowledge to address complex tasks.

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Early enthusiasm (1952-1969)

A period marked by renewed enthusiasm and advancements in AI research. It saw the development of early programs and the formalization of the field.

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Intelligent ≠ Omniscient

An intelligent agent is not perfect or all-knowing. It may lack access to all relevant information for making decisions.

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Information gathering (exploration)

Agents constantly seek information by exploring their environment, but actions may not always produce the expected outcomes.

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Autonomous (learning)

Autonomous agents learn and adapt based on their own experiences, shaping their future behavior.

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Performance Evaluation

This refers to how well an intelligent agent performs in its designated task.

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Environment

The environment where an intelligent agent operates, encompassing factors such as objects, conditions, and other agents.

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Actuators

The tools an agent uses to interact with the environment, such as physical manipulators or communication devices.

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Sensors

Sensors enable agents to perceive their environment, gathering data such as images, sounds, or temperature.

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Rule-based (reactive)

An agent's behavior is determined by rules or procedures depending on its current perception of the environment, without considering past perceptions.

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Probabilistic Reasoning

A mathematical approach to dealing with uncertainty, where conclusions are based on probabilities rather than absolute truths. It's used in AI to represent and reason about knowledge with incomplete or imperfect information.

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Machine Learning (ML)

A type of machine learning where algorithms learn from data without explicit programming. It relies on statistical models to learn patterns and make predictions.

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Deep Learning (DL)

A specific type of ML algorithm inspired by the structure and function of the human brain. It uses interconnected layers of processing units (neurons) to learn complex patterns and make decisions.

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Bayesian Networks

A computational model that uses probabilities to represent and reason about uncertain knowledge. Bayesian networks are used for tasks such as medical diagnosis and fault analysis.

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Intelligent Agent

An entity that senses its environment, perceives information, and takes actions based on its understanding and goals. In AI, agents are designed to perform tasks autonomously.

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Rational Behavior (for an agent)

The ability of an agent to perform the correct actions to achieve its goals in a given environment, optimizing its performance based on its understanding of the situation and its desired outcome.

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Learning (for an agent)

The process by which an agent learns from its experiences and adapts its behavior to improve its performance. This involves updating its knowledge and strategies based on feedback from the environment.

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Study Notes

Introduction to Intelligent Systems

  • This presentation covers introductory topics of intelligent systems, including artificial intelligence (AI).
  • The presenters are Ezequiel López-Rubio and Enrique Domínguez from the Department of Computer Languages and Computer Science at the University of Málaga, Spain.

Contents

  • The presentation outlines topics that will be discussed: Artificial Intelligence (AI), History of AI, and Intelligent Agents.
  • These sections cover historical developments and fundamental principles of AI.

1. Artificial Intelligence (AI)

  • Definitions:
    • Acting humanly: The Turing Test (creating machines that perform functions requiring intelligence when performed by people)
    • Thinking humanly: Cognitive science (study of human mind processes), Neuroscience (study of brain function).
    • Thinking rationally: Aristotle's syllogisms (codifying "right thinking"), Formal Logic.
    • Acting rationally: Rational agents (acting to achieve the best expected outcomes)

1. Artificial Intelligence (AI) - Acting humanly

  • Can machines think?
  • Can machines exhibit intelligent behavior?
  • Turing Test (Process natural language, Represent knowledge, Reason, Learn, Perceive objects, Manipulate objects; NON-reproducible, NON-constructive, NO mathematical analysis; Requires a human interrogator to distinguish machine from human)

1. Artificial Intelligence (AI) - Thinking humanly

  • How does a human think?
  • How does the brain work?
  • Validation of human mind theories.
  • Prediction and comparison with human behavior (Cognitive Science).
  • Identification with neural behavior and implementation (Neuroscience)

1. Artificial Intelligence (AI) - Thinking rationally

  • Aristotle's syllogisms.
  • Codify "right thinking."
  • Logic (Notation, Inference rules).
  • An automated process?

1. Artificial Intelligence (AI) - Acting rationally

  • Rational behavior (Act so as to achieve the best expected outcome, Achieve one's goals efficiently, Environment.)
  • Agent (Entity whose actions are based on the perceived information).

2. History of AI

  • Inception (1943-1956):
    • W. McCulloch & W. Pitts (1943): Early work in neural networks.
    • Hebbian learning (1949).
    • SNARC - First neural network computer (1950).
    • A. Turing: "Computing Machinery and Intelligence" (1950): Can machines think?, Turing test.
    • E. Dijkstra (Can submarines swim?)

2. History of AI - Early enthusiasm (1952-1969)

  • First programs (General Problem Solver, Checkers Program, Geometry Theorem Prover).
  • Dartmouth workshop (1956): 10 people during 2 months.
  • Lisp (John McCarthy, 1958).
  • Genetic algorithms (1958-59).
  • Friedberg – Machine evolution.
  • Neural networks (1962): Widrow – Adalines, Rosenblatt – Perceptron

2. History of AI - Expert systems (1969-1986)

  • "A dose of reality" (1966-1973): Overconfidence, Illusion of unlimited computational power.
  • Criticisms: (Lighthtill report, 1973) and lack of funding (“The AI winter”).
  • Limitations of basic structures.
  • Computational complexity.
  • General-purpose programs (weak methods): Domain-specific programs.
  • Expert systems (1969): DENDRAL, MYCIN, Prolog
  • RI – First commercial expert system (1982): Digital Equipment Corporation (McDermott); saved more than $40 million / year.

2. History of AI - Probabilistic reasoning and ML (1980-1990s)

  • Return of neural networks: Back-propagation algorithm, Rumelhart & McClelland (1986).
  • Probability in AI.
  • More scientific approach.
  • Benchmark datasets and competitions.
  • Hidden Markov models (mathematical theory + training).
  • Bayesian networks (uncertain knowledge representation + algorithms).
  • Reunification of subfields (Computer vision, speech recognition, natural language processing,... New applications and faster deployment).

2. History of AI - Big Data and Deep Learning (2000s)

  • Appearance of large datasets (e.g., World Wide Web).
  • Unstructured data (text, images, audio, video).
  • New algorithms to deal with unlabeled data.
  • AI starts attracting commercial attention (e.g., Watson systems (IBM).
  • Deep learning (2011-): Multiple layers (convolutional neural networks).
  • More applications (machine translation, medical diagnosis, game playing).
  • DL relies heavily on powerful hardware (GPUs).
  • The present (and future): Next generation of conversational assistants (Chatbots), Generative AI (multimodal).

3. Intelligent Agents

  • Definition: Agent: entity which acts based on its perception of its environment.
  • Environment, Sensors, Actuators (f: P* → A)

3. Intelligent Agents - Definition 2

  • Intelligent agent (Optimizing its expected performance): According to the received percepts, it selects the right actions.
  • Doing the right thing.
  • Goals (desirable states).
  • Evaluation of agent's performance.

3. Intelligent Agents - Omniscience, learning and autonomy

  • Intelligent ≠ Omniscient (Not all relevant information can be perceived).
  • Information gathering (exploration).
  • Outcomes might be different from expectation.
  • Autonomous (learning), Behavior determined by its own experience.

3. Intelligent Agents - Specification

  • Performance evaluation.
  • Environment.
  • Actuators.
  • Sensors.

3. Intelligent Agents - Properties of task environments

  • Observable (partially / fully).
  • Single agent / multiagent (Competitive / Cooperative).
  • Deterministic / stochastic.
  • Episodic / sequential.
  • Static / dynamic.
  • Discrete / continuous.
  • Known / unknown.

3. Intelligent Agents - Structure

  • Artificial intelligence (Find way to implement f: P* → A)
  • Architecture + Program = Agent

3. Intelligent Agents - Kinds of intelligent agents

  • Rule-based (reactive)
  • Model-based.
  • Goal-based (Search and planning).
  • Utility-based (Maximize the expected utility).

3. Intelligent Agents - Reactive

  • Actions depend on current percepts, ignoring the rest of the percept history.

3. Intelligent Agents - Model-based

  • The current state depends on the percept history.

3. Intelligent Agents - Goal-based

  • Search and planning.

3. Intelligent Agents - Utility-based

  • Maximize the expected utility of action outcomes.

3. Intelligent Agents - Learning agents

  • Performance standard, Critic, Learning, Problem Generator

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