Artificial Cognitive Systems — Introduction

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What is cognitive system?

Cognitive computing is the use of computerized models to simulate the human thought process in complex situations where the answers may be ambiguous and uncertain.

When we come to Artificial intelligence, it has two significant areas as Artificial Cognitive Systems and Machine Learning. In cognitive computing, synthesize information and provide the best possible answer with evidence. Also, it can provide alternative solutions, as well. Machine learning can find answers to the given problem, but it can’t explain the answer. Cognitive Systems can do both provide answers and justification of answers. Cognitive computing systems must have key attributes, as listed by the Cognitive Computing Consortium.

· Adaptive

· Interactive

· Iterative and stateful

· Contextual

In cognitive tasks, the brain uses rule-based approaches. Here are some examples of cognitive tasks. In human learning can be done using rules, theories, and practices. When we consider Machine Learning, it is treated only as a training based activity. Therefore Machine Learning is not the same as human learning.

· Learning

· Thinking

· Attention

· Understanding

· Problem Solving

If you have data, then you can go for a neural network like solutions. But you don’t have much data and but a little bit of knowledge you can’t go for training based solutions. In this kind of situation, we can use cognitive systems (For example, a new disease spreads in the world, and we don’t have much data related to this disease). In cognitive systems use a rule or theory-based knowledge.

Expert System is an excellent example of cognitive systems.

Expert System is a model of human experts. Here we simulate human expert’s problem-solving methods. The simple philosophy has inspired expert system technology that domain/subject-specific knowledge and problem-solving knowledge can be maintained separately.

Subject Area Specific Knowledge

Problem Solving Knowledge

Why expert Systems?

There are so many reasons to use expert systems. Here are some reasons for use expert systems.

· Assisting human experts.

· Distribute expert’s knowledge.

o For example a person lives in Asian country, he can easily access experts knowledge who lives in United States.

· Preserving Experts knowledge

· Avoiding mistakes by experts

o Human experts sometimes do mistakes because tiredness and etc.

· Saving expert’s time

Features of Expert Systems

· Operate in a specific area — Experts work in narrow domain.

o Example Eye specialist doctor

· Dominate asking questions

· Process Incomplete Information

· Provide Alternative Solutions

· Provide certainty of a answer — How much answer is valid

· Recommendations than extract answers.

· Use heuristics and experiences

· Provide reasons for answers

How expert systems work?

These are the basic steps followed by the expert systems.

· Start with the dialogue

· Forming Conflict Sets

· Conflict Reasoning

· Missing Information handling

· Provide alternative solutions

· Handling uncertainty

· Provide Explanations

Components of Expert System in Artificial Intelligence

· Knowledge Base

· Inference Engine

· User Interface

Here we briefly discussed artificial cognitive systems and the Expert system as an example for cognitive systems. We have images that will help you in better understanding.

Feel free to ask in a comment section.

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Artificial Intelligent Enthusiast

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