A branch of computer science dealing with the simulation of intelligent human behaviour by a computer. Cognitive functions can be replicated in AI and measured against human benchmarks
AI is a collection of rules and data, with the ability to reason, learn and adapt to external stimuli
Narrow AI is a machine that has superior performance to a human in one specific task
General AI is when a machine is similar, but not superior to humans in a wide range of tasks
Strong AI is a machine which has superior performance to humans in many tasks
Reasoning is the ability to draw conclusions based on given data, and deductive reasoning is when a series of facts are built up to create set of rules that can be applied in situations
Expert Systems are a form of AI designed to mimic human intelligence. They use knowledge and inference to solve problems
Expert systems consist of a user interface, an explanation system, a rules base, a knowledge base and an inference engine
A rules base is a set of inference rules which the engine uses to draw conclusions using logic
A knowledge base is a repository of facts about a given subjects
The inference engine examines the knowledge base for information matching a query, it then asks questions and applies the rules in the rules base to the information to generate a response
Advantages of Expert Systems:
High level of expertise, accuracy and speed
Stores vast amounts of information
Unbiased reporting
Simple to use
Disadvantages of Expert Systems:
Users need training to operate them
Maintenance is time consuming
Give “cold” responses that can be inappropriate
Only as good as it’s knowledge base
Users can sometimes dangerously assume an AI is infallible
Setting up an expert system:
Info gathered to populate the knowledge base
A rules base is created full of inference rules
Inference engine set up, which is a complex system
User interface needs developing
Testing needs to be done before the system is used