Ai lecture 01(unit03)
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The document provides an overview of knowledge-based reasoning and agents in artificial intelligence, highlighting the distinctions between logic and reasoning and the importance of knowledge representation. It explains how knowledge-based agents operate by maintaining internal knowledge, reasoning over it, and updating knowledge through observations. Key components of these agents include a knowledge base and an inference system that enables them to derive new information and act intelligently.
Ai lecture 01(unit03)
- 1. Topic To Be Covered: I. Knowledge Based Reasoning II. Knowledge Based Agents Jagdamba Education Society's SND College of Engineering & Research Centre Department of Computer Engineering SUBJECT: Artificial Intelligence & Robotics Lecture No-1(UNIT-03) Logic & Reasoning Prof.Dhakane Vikas N
- 2. Knowledge Based Reasoning What is Logic & Reasoning In AI? The main difference between logic and reason is that logic is basic facts or knowledge about our world. For example, logic says that the “Throwing stone will fall”. (Obviously, because of gravity!). Logic is the key behind any knowledge. It allows a person to filter the necessary information from the bulk and draw a conclusion. In artificial intelligence, the representation of knowledge is done via logics Reasoning is subject to personal opinion(Personal Thought Process), whereas logic is an actual science that follows clearly defined rules and tests for critical thinking.
- 3. Knowledge Based Reasoning What is Knowledge Based Reasoning??? Knowledge Base is a set of representation of Facts/Information about the surrounding environment(Real World) Knowledge representation and reasoning (KR², KR&R) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system(Or AI Agent) can utilize to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language or Solving various real world Problems. Etc. 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.
- 4. What is Knowledge Based Agents In AI? What is Knowledge Based Agents In AI? Knowledge is the basic element for a human brain to know and understand the things logically. When a person becomes knowledgeable about something, he is able to do that thing in a better way. In AI, the agents which copy such an element of human beings are known as knowledge-based agents. An intelligent agent needs knowledge about the real world for taking decisions and reasoning to act efficiently.
- 5. What is Knowledge Based Agents In AI? What is Knowledge Based Agents In AI? 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. Knowledge-based agents are composed of two main parts: Knowledge-base and Inference system.
- 6. The architecture of knowledge-based agent: The architecture of knowledge-based agent: The diagram is representing a generalized architecture for a knowledge-based agent. The knowledge-based agent (KBA) take input from the environment by perceiving the environment. The input is taken by the inference engine of the agent and which also communicate with KB to decide as per the knowledge store in KB. The learning element of KBA regularly updates the KB by learning new knowledge.
- 7. The architecture of knowledge-based agent: The architecture of knowledge-based agent: Knowledge base: Knowledge- base is a central component of a knowledge-based agent, it is also known as KB. It is a collection of sentences (here 'sentence' is a technical term and it is not identical to sentence in English). These sentences are expressed in a language which is called a knowledge representation language. The Knowledge-base of KBA stores fact about the world.
- 8. The architecture of knowledge-based agent: The architecture of knowledge-based agent: Why use a knowledge base? Knowledge-base is required for updating knowledge for an agent to learn with experiences and take action as per the knowledge.
- 9. The architecture of knowledge-based agent: Inference system A program’s protocol for navigating through the rules and data in a knowledge system in order to solve the problem. Inference means deriving new sentences from old. Inference system allows us to add a new sentence to the knowledge base. Inference system applies logical rules to the KB to deduce new information.
- 10. The architecture of knowledge-based agent: Inference system Inference system generates new facts so that an agent can update the KB. An inference system works mainly in two rules which are given as: Forward chaining Backward chaining
- 11. The architecture of knowledge-based agent: Operations Performed by KBA Following are three operations which are performed by KBA in order to show the intelligent behavior: TELL: This operation tells the knowledge base what it perceives from the environment. ASK: This operation asks the knowledge base what action it should perform. Perform: It performs the selected action.