Knowledge Representation lab report using comments (Instructor Name: Nuruzzaman Faruqui)

 Knowledge Representation

 

Introduction: 

Knowledge Representation in AI describes the representation of knowledge. Basically, it is a study of how the beliefs, intentions, and judgments of an intelligent agent can be expressed suitably for automated reasoning. One of the primary purposes of Knowledge Representation includes modeling intelligent behavior for an agent. Humans are best at understanding, reasoning, and interpreting knowledge. Human knows things, which is knowledge and as per their knowledge they perform various actions in the real world. But how machines do all these things comes under knowledge representation and reasoning. Knowledge Representation and Reasoning (KR, KRR) represents information from the real world for a computer to understand and then utilize this knowledge to solve complex real-life problems like communicating with human beings in natural language. Knowledge representation in AI is not just about storing data in a database, it allows a machine to learn from that knowledge and behave intelligently like a human being. 

This is an important part of Artificial Intelligence course and this  lab report is done by myself under the supervision of Nuruzzaman Faruqui, lecturer of City University, Bangladesh. From this course we get to explore the real applicable approaches through AI and also acquires better knowledge of the functionality of AI and how AI is making our daily life easier. That's why this is the best Artificial Intelligence course in Bangladesh.

 

Problem Statement: 

Artificial Intelligent Systems usually consist of various components to display their intelligent behavior. Some of these components include in figure below. Here this figure is shown the different components of the system and how it works:

Knowledge representation is a way to feed information to a Knowledge-based agent. An intelligent agent needs knowledge about the real world for taking decisions and reasoning to act efficiently.Knowledge-based agents are those agents who have the capability of maintaining an internal state of knowledge, reason over that knowledge, update their knowledge after observations and take actions. These agents can represent the world with some formal representation and act intelligently. Generally we humans use sentences to collect information. The machine can not be fed directly with human language. That’s where propositional logic comes to the rescue. It helps to make sentences in a way that AI can store knowledge and infer new information.

Here we have a scenario to understand how we can build a knowledge-based agent and its operation, consider the following example: -

•   If it didn’t rain, Harry visited Hagrid today.

•  Harry visited Hagrid or Dumbledore today, but not both.

•  Harry visited Dumbledore today.

So, we will use the above scenario to make a knowledge-base and make an intelligent system that will tell us, did it rain today? Or, did Harry visit Hagrid today? 

And here we will use the basic logical connectives that we have learned in discrete mathematics will applied to make a knowledge-base. Such as, Not (¬), And (∧), Or (∨), Implication (→), Bidirectional (↔). Another logical connective you may or may not be familiar that is Entailment (⊨). What it means is, if a ⊨ b, then in any world where a is true, b is also true.

 

Code Commentary: 

Here we have used Python Language to implement the scenario by logical connectives.

 

 

To import libraries in Python we will import the above code to use all the logical connectives.

 

 

Result:

After executing the code, we get the following output/result

 

 

Conclusion:

Knowledge-base is required for updating knowledge for an agent to learn with experiences and take action as per the knowledge and also it an important topic in Artificial Intelligent. It has to do with the ‘thinking’ of AI systems and contributes to its intelligent behavior. Knowledge Representation is a radical and new approach in AI that is changing the world. Let’s look into what it is and its applications. knowledge representation allows machines to behave like humans by empowering an AI machine to learn from available information, experience or experts. However, it is important to choose the right type of knowledge representation.