An Introduction to Artificial Intelligence

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An Introduction to Artificial Intelligence
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Introduction

• Getting machines to think .
• Imitation game and the Turing test.
• Key processes of AI
• Search, e.g. breadth first search, depth first
  search, heuristic searches.
• Knowledge representation, e.g. predicate logic,
  rule-based systems, semantic networks.

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Areas of AI

• Game playing
• Theorem proving
• Expert systems
• Natural language processing
• Modelling human performance
• Planning and Robotics
• Neural-networks
• Evolutionary algorithms and other biologically
  inspired methods
• Agent-based technology

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Game Playing

• Getting the computer to play certain board games
  that require intelligence, e.g. chess,
  checkers, 15-puzzle.
• A state space of the game is developed and a
  search applied to the space to look ahead.
• Example Deep blue vs. Kasparov.

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Theory Proving

• Automatic theorem proving.
• Generate proofs for simple theorems.
• Mathematical logic forms the basis of these
  systems.
• The General Problem Solver is one of the first
  systems.

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Expert Systems

• Performs the task of a human expert, e.g. a
  doctor, a psychologist.
• Knowledge from an expert is stored in a knowledge
  base.
• Examples ELIZA, MYCIN, EMYCIN
• Suitable for specialized fields with a clearly
  defined domain.

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Natural Language Processing

• Develop systems that are able to understand a
  natural language such as English.
• Voice input systems, e.g. Dragon.
• Systems that converse in a particular language.
• Examples SHRDLU and ELIZA

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Modeling Human Performance

• Systems that model some aspect of problem
  solving.
• Examples Intelligent tutoring systems that
  provide individualized instruction in a specific
  domain.

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Planning and Robotics

• Involves designing flexible and responsive
  robots.
• Lists of actions to be performed are generated.
• Aimed at high-level tasks, e.g. moving a box
  across the room.
• Has led to agent-oriented problem solving.

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Neural Networks

• Aimed of low-level processing.
• Are essentially mathematical models of the human
  brain.
• A neuron

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Evolutionary Algorithms Other Nature-Inspired
Algorithms

• Based on Darwins theory of evolution.
• An initial population of randomly created
  individuals is iteratively refined until a
  solution is found.
• Examples genetic algorithms, genetic
  programming, memetic algorithms
• Other methodologies ant colonization, swarm
  intelligence.

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Uncertainty Reasoning

• Uncertain terms may need to be presented.
• Example representing terms such as big or
  small.
• Methods for this purpose
• Fuzzy logic
• Bayesian reasoning and networks

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Artificial Intelligence Languages

• Programming paradigms
• Artificial intelligence languages Prolog and
  Lisp
• Prolog (Programming Logic) declarative
  predicate logic
• Lisp (List Processing) functional code takes
  the form of recursive functions.
• More recently AI systems have been developed in a
  number of languages including Smalltalk, C, C
  and Java.

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Agent-based Technology

• Intelligent agents, also called softbots, are
  used to perform mundane tasks or solve problems.
• In a multi-agent system agents communicate using
  an agent communication language.