Knowledge Based Agent-Artificial Intelligence-Unit-II-Logical Reasoning
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- Опубликовано: 29 дек 2024
- Unit - 2 - Logical Reasoning
Logical Agent - Part-1- Knowledge Based Agent
An agent can represent knowledge of its world, its goals and the current situation.
Logical Agent has a collection of sentences in logic
By using these logical sentences, the agent decides what to do by inferring knowledge (conclusion(s))
The conclusions are achieved by certain action or set of actions, is appropriate to achieve its goals.
Knowledge and reasoning are important to logical agents because they enable successful behaviors to achieve a desired goal
Knowledge-Based Agents
Central component of a Knowledge-Based Agent is a Knowledge-Base (KB)
KB contains a set of sentences in a formal language
Sentences are expressed using a knowledge representation language
Two generic functions:
TELL - add new sentences (facts) to the KB
“Tell it what it needs to know”
ASK - query what is known from the KB
“Ask what to do next”
The agent must be able to:
Represent states and actions
Incorporate new percepts
Update internal representations of the world
Deduce hidden properties of the world
Deduce appropriate actions
Procedural : Encode desired behaviors directly as program code
Minimizing the role of explicit representation and reasoning can result in a much more efficient system
Declarative : Building Knowledge Base for Agent.
You can build a knowledge-based agent simply by “TELLing” it what it needs to know
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Here's an explanation of the code with an example:
The agent receives a percept (information or observation) from its environment.
Example: Let's say the agent is a self-driving car, and the percept it receives is the current traffic conditions, such as the presence of other vehicles, traffic lights, and road signs.
The agent updates its knowledge base (KB) with the percept by creating a percept sentence.
Example: The agent creates a sentence in its knowledge base that represents the current traffic conditions, such as "There is heavy traffic on the road, and the traffic light is red."
The agent asks the knowledge base for an action to take based on the current state of the environment. It generates an action query using the current time (t).
Example: The agent queries its knowledge base by asking, "What action should I take considering the current traffic conditions at time t?"
The agent receives an action from the knowledge base based on the action query.
Example: The knowledge base suggests an action, such as "Stop the car and wait for the traffic light to turn green."
The agent updates its knowledge base with the action taken by creating an action sentence.
Example: The agent creates a sentence in its knowledge base that represents the action it took, such as "The car stopped and waited for the traffic light to turn green."
The agent increments the time counter (t) by 1 to indicate the progression of time.
Example: The agent updates the time counter to t + 1.
The agent returns the action to be executed in the environment.
Example: The agent returns the action "Stop the car and wait for the traffic light to turn green" to be executed.
The process repeats as the agent continues to perceive the environment, update its knowledge base, and make decisions based on the current state of the environment
Thank you so much ma'am, Without you its very hard to understand these concepts which wasn't taught properly by my teacher & skipped some concepts
Excellent explanation, good ppt, keep doing
Thanks a lot
Excellent
excellent introduaction about AI
Thank you
@@WinningCSE 💕
My mam copies your whole lecture and the ppt ,still she cant teach, i dont know how these teachers are selected to engineering colleges!
We students have to struggle from them
Thank you very much for Your positive comments , pleasure is mine.