ByteSizeNotes Chapter 6.3
Chapter 6.3

Artificial Intelligence

What is AI?

  • The development of programs to simulate human behaviour. Including:
  • Image recognition:identifies objects or people in an image
  • Speech recognition:identifies words spoken and stores them
  • Natural language:receives commands or instructions that are not in a set format and performs the required response
  • Computer games:move elements or characters independently based on the environment
  • Diagnosis systems:used to diagnose certain conditions, eg. using a patient's symptoms to detect the illness and suggest actions to cure

Components of AI

  • Collection of data
  • A set of programmed rules
  • The ability to reason
  • The ability to learn and adapt

Describe the characteristics of AI

  • Collects data; stores rules for using the data
  • Has the ability to reason
  • Has the ability to learn by using machine learning by adapting what it does for example from mistakes previously made, to not make them again by changing its own rules by changing its own data by being trained
  • Makes one or more predictions (to make a decision)
  • Find/analyse patterns

Machine Learning

What is machine learning?

  • Is when a program has the ability to automatically adapt its own processes and/or data

**Explain how a program can use AI to help a robot solve different

puzzles**

  • Use machine learning algorithms
  • Collects data about where it has been
  • Collects data about obstacles
  • Stores successful actions; stores unsuccessful actions
  • Identifies and stores patterns make sure it does not repeat incorrect route so it knows how to react to obstacles next time so it knows what is most likely to work next time

Expert Systems

What is an expert system?

  • A special type of system that attempts to emulate the expertise of a human.
  • Achieved by asking questions to determine the solution to the answer
  • Depending on the choices the system will ask a different question

Expert System Key Features/Components

  • A repository of facts
  • A collection of objects and their attributes
  • A collection of inference rules used to draw conclusions
  • Inference rules are used by the inference engine to draw conclusions
  • A kind of search engine used in an expert system which examines the knowledge base for information that matches the queries
  • The inference engine is the problem-solving part of the expert system that makes use of inference rules in the rules base
  • Used to allow the user to interact with the expert system
  • Interaction can be through dialogue boxes, command prompts or other input methods

Why does an expert system need a knowledge base?

  • It needs facts... to generate the rules to make the decisions // The data it contains is essential to the decision making process

Advantages of Expert Systems

  • They offer a high level of expertise
  • They offer high accuracy
  • The results are consistent
  • They have the ability to store vast amounts of ideas and facts
  • They can make traceable logical solutions and diagnostics
  • It is possible for an expert system to have multiple expertise
  • They have very fast response times (much quicker than a human expert)
  • They provide unbiased reporting and analysis of the facts
  • They indicate the probability of any suggested solution being correct.

Disadvantages of Expert Systems

  • Users of the expert system need considerable training in its use to ensure the system is being used correctly
  • The set up and maintenance costs are very high
  • They tend to give very "cold" responses that may not be appropriate in certain medical situations
  • They are only as good as the information/facts entered into the system
  • Users sometimes make the very dangerous assumption that they are infallible.