CHAPTER 5 - Knowledge Representation and Reasoning

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

is the part of Artificial intelligence which is concerned with AI agents' thinking and how thinking contributes to intelligent behavior of agents.

knowledge representation and reasoning

It is responsible for representing information about the real world so that a computer can understand and can utilize this knowledge to solve the complex real world problems such as diagnosis of a medical condition or communicating with humans in natural language.

knowledge representation and reasoning

It is also a way which describes how we can represent knowledge in artificial intelligence. It is not just storing data into some database, but it also enables an intelligent machine to learn from that knowledge and experiences so that it can behave intelligently like a human

Agent

can be anything that perceive its environment through sensors and act upon that environment through actuators.

Human, robotic, software

3 types of agent (____ agent)

Human-Agent

type of agent. has eyes, ears, and other organs which work for sensors and hand, legs, vocal tract work for actuators.

Robotic Agent

type of agent. can have cameras, infrared range finder, NLP for sensors and various motors for actuators.

Software Agent

type of agent. can have keystrokes, file contents as sensory input and act on those inputs and display output on the screen.

Object, events, performance, metaknowledge, facts, knowledge base

6 kinds of knowledge that needs to be represented in AI systems (OEPMFK)

Object

kind of knowledge represented in AI systems. All the facts about objects in our world domain. E.g., Guitars contains strings, trumpets are brass instruments.

Events

kind of knowledge represented in AI systems. Events are the actions which occur in our world.

Performance

kind of knowledge represented in AI systems. It describe behavior which involves knowledge about how to do things.

Meta-knowledge

kind of knowledge represented in AI systems. It is knowledge about what we know.

Facts

kind of knowledge represented in AI systems. are the truths about the real world and what we represent.

Knowledge Base

kind of knowledge represented in AI systems. The central component of the knowledge based agents is the this. It is represented as KB. It is a group of the Sentences (Here, sentences are used as a technical term and not identical with the English language)

Knowledge

is awareness or familiarity gained by experiences of facts, data, and situations.

Declarative, procedural, metaknowledge, heuristic, structural

5 types of knowledge in AI (DPMHS)

Declarative

type of knowledge in AI. is to know about something. It includes concepts, facts, and objects. It is also called descriptive knowledge and expressed in declarative sentences. It is simpler than procedural language.

Procedural

type of knowledge in AI. It is also known as imperative knowledge. It is a type of knowledge which is responsible for knowing how to do something. It can be directly applied to any task. It includes rules, strategies, procedures, agendas, etc. It depends on the task on which it can be applied

Meta-knowledge

type of knowledge in AI. Knowledge about the other types of knowledge

Heuristic

type of knowledge in AI. is representing knowledge of some experts in a filed or subject. It is rules of thumb based on previous experiences, awareness of approaches, and which are good to work but not guaranteed

Structural

type of knowledge in AI. is basic knowledge to problem-solving. It describes relationships between various concepts such as kind of, part of, and grouping of something. It describes the relationship that exists between concepts or objects.

Experience

Knowledge plays an important role in demonstrating intelligent behavior in AI agents. An agent is only able to accurately act on some input when he has some knowledge or __________ about that input.

Perception, learning, knowledge representation and reasoning, planning, execution

5 stages in the AI knowledge cycle (PLKPE)

Simple relational, inheritable, inferential, procedural

4 main approaches to knowledge representation (SIIP)

Simple Relational Knowledge

approach to knowledge representation. It is the simplest way of storing facts which uses the relational method, and each fact about a set of the object is set out systematically in columns.

Simple Relational Knowledge

approach to knowledge representation. This approach of knowledge representation is famous in database systems where the relationship between different entities is represented. This approach has little opportunity for inference.

Inheritable knowledge

approach to knowledge representation. In this knowledge approach, all data must be stored into a hierarchy of classes. All classes should be arranged in a generalized form or a hierarchal manner.

instance relation

Inheritable knowledge shows a relation between instance and class, and it is called ________ ________. Every individual frame can represent the collection of attributes and its value

Inferential knowledge

approach to knowledge representation. Represents knowledge in the form of formal logics. This approach can be used to derive more facts. It guaranteed correctness

Procedural knowledge

approach to knowledge representation. This approach uses small programs and codes which describes how to do specific things, and how to proceed.

Procedural knowledge

approach to knowledge representation. In this approach, one important rule is used which is If-Then rule. In this knowledge, we can use various coding languages such as LISP language and Prolog language. We can easily represent heuristic or domain-specific knowledge using this approach. But it is not necessary that we can represent all cases in this approach.

Representational accuracy, inferential adequacy, inferential efficiency, acquisitional efficiency

4 requirements that a good knowledge representation system must possess

Representational Accuracy

requirement that a good knowledge representation system must possess. KR system should have the ability to represent all kind of required knowledge.

Inferential Adequacy

requirement that a good knowledge representation system must possess. KR system should have ability to manipulate the representational structures to produce new knowledge corresponding to existing structure.

Inferential Efficiency

requirement that a good knowledge representation system must possess. The ability to direct the inferential knowledge mechanism into the most productive directions by storing appropriate guides.

Acquisitional efficiency

requirement that a good knowledge representation system must possess. The ability to acquire the new knowledge easily using automatic methods