Properties

Three Key Components

1. Representation
   • Declarative & Procedural knowledge
   • Typically human-readable symbols
2. Reasoning
   • Ability to solve problems
   • Express and solve range of problems and types
   • Make explicit and implicit information known to it
   • Control mechanism to decide which operations to use if and when, when a solution has been found
3. Learning

An AI system must be able to

1. Store knowledge
2. Apply knowledge to solve problems
3. Acquire new knowledge through experience

Expert Systems

• Usually easier to obtain compiled experience from experts than duplicate experience that made them experts for network

Information Processing

Inductive

• General patterns and rules determined from data and experience
• Similarity-based learning

Deductive

• General rules are used to determine specific facts
• Proof of a theorem

Explanation-based learning uses both

Classical AI vs Neural Nets

Level of Explanation

• Classical has emphasis on building symbolic representations
  • Models cognition as sequential processing of symbolic representations
• Neural nets emphasis on parallel distributed processing models
  • Models assume information processing takes place through interactions of large numbers of neurons

Processing style

• Classical processing is sequential
  • Von Neumann Machine
• Neural nets use parallelism everywhere
  • Source of flexibility
  • Robust

Representational Structure

• Classical emphasises language of thought
  • Symbolic representation has quasi-linguistic structure
  • New symbols created from compositionality
• Neural nets have problem describing nature and structure of representation

Symbolic AI is the formal manipulation of a language of algorithms and data representations in a top-down fashion

Neural nets bottom-up