Knowledge Representation and Reasoning | Chapter 1-Basic Gyaan about Knowledge Representation
What is Knowledge Representation and Reasoning?
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.
In this series of the chapter, we are gonna learn about knowledge representation and reasoning, Various methods of representation, some hands-on code to represent the knowledge representation.
Firstly define the Knowledge Representation or, KR or, KRR or, KR²:-
Knowledge representation and reasoning is the part of Artificial intelligence which concerned with AI agents thinking and how thinking contributes to intelligent behavior of agents.
It is responsible for representing information about the real world so that a computer can understand and can utilize this knowledge to solve complex real-world problems. It also enables an intelligent machine to learn from that knowledge and experiences so, that it can behave intelligently like a human.
Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build.
Examples of knowledge representation formalisms include semantic nets, systems architecture, frames, rules, and ontologies. Examples of automated reasoning engines include inference engines, theorem provers, and classifiers.
Now the question is, What to Represent?
Following are the kind of knowledge which needs to be represented:-
- Object:- All the facts about objects in our world domain.
- Events: Events are the actions that occur in our world.
- Performance: It describes behavior that involves knowledge about how to do things.
- Meta-knowledge: It is knowledge about what we know.
- Facts: Facts are the truths about the real world and what we represent.
- Knowledge-Base: It is the main component of the knowledge-based agents. It is represented as KB. A knowledgebase is the representation of all of the knowledge that is stored by an agent. Rich enough to express the knowledge needed to solve the problem as close to the problem as possible; it should be compact, natural, and maintainable.
What is Knowledge?
Knowledge is a familiarity, awareness, or understanding of someone or, something, such as facts, information, descriptions, or skills, which is acquired through experience or, education by perceiving, discovering or, learning.
In the field of AI, there are various types of knowledge:-
- Declarative Knowledge:- It is also called as Descriptive knowledge, propositional knowledge, or constative knowledge. It is the type of knowledge that is, by its very nature, expressed in declarative sentences or indicative propositions. It includes concepts, facts, and objects.
- Procedural Knowledge:- Procedural knowledge, also known as imperative knowledge, is the knowledge exercised in the performance of some task. It can be directly applied to any task. It includes rules, strategies, procedures, agendas, etc. Procedural knowledge depends on the task on which it can be applied.
- Meta-knowledge:- Metaknowledge or meta-knowledge is knowledge about preselected knowledge. Examples of the first-level individual meta-knowledge are methods of planning, modeling, tagging.
- Heuristic knowledge:- Heuristic knowledge is representing knowledge of some experts in a filed or subject. Heuristic knowledge is rules of thumb based on previous experiences, awareness of approaches, and which are good to work but not guaranteed.
- Structural knowledge:- Structural knowledge is knowledge of how the ideas within a domain are integrated and interrelated. It describes relationships between various concepts such as kind of, part of, and grouping of something.
Big Question is, What is the role of Knowledge in AI-based Agents? Why we need it?
- Knowledge of real-worlds plays a vital role in intelligence and the same for creating artificial intelligence.
- Knowledge plays an important role in demonstrating intelligent behavior in AI agents.
- Use knowledge to make intelligent decisions in real-worlds.
Let’s suppose if you met a person who is speaking in a language which you don’t know, then how you will able to act on that. The same thing applies to the intelligent behavior of the agents.
As we can see in a diagram, there is one decision-maker that acts or, takes decisions by sensing the environment and using knowledge.
But if the knowledge part will not present then, it cannot display intelligent behavior.
The other component of an Agent help in decision making and displaying intelligent behaviors are:-
- Perception
- Learning
- Knowledge Representation and Reasoning
- Planning
- Execution
The above diagram is showing how an AI system can interact with the real world and what components help it to show intelligence. AI system has a Perception component like a sensor, camera, etc., which retrieves information from its environment. It can be visual, audio or other form of sensory input.
The learning component is responsible for learning from data captured by Perception comportment.
In the complete cycle, the main components are knowledge representation and Reasoning. These two components are involved in showing the intelligence in machine-like humans. These two components are independent of each other but also coupled together.
The planning and execution depend on the analysis of Knowledge representation and reasoning.
Summary:
- Knowledge Representation are Concise representation of knowledge that is manipulatable in software.
- Knowledge plays an important role in demonstrating intelligent behavior in AI agents.
- KR is important in making intelligent decisions in real-worlds.
In the next chapter, we will learn little more theory about KR like., what are the different ways in which we represent the knowledge and then directly jump to the code.
