artificial intelligence presentation.pdf
- 2. AboutUs ABDUL MOEED TALLHA JAVAID USMAN BILAL MEHMOOD TOPIC FIRST ORDER LOGIC FORWARD CHAINING BACKWARD CHAINING 2
- 3. FORWARD CHAINING Forward chaining is a method of reasoning in artificial intelligence in which inference rules are applied to existing data to extract additional data until an endpoint (goal) is achieved In this type of chaining, the inference engine starts by evaluating existing facts, derivations, and conditions before deducing new information. An endpoint (goal) is achieved through the manipulation of knowledge that exists in the knowledge base Forward chaining is known as data-driven technique because we reaches to the goal using the available data. 3
- 4. EXPERTSYSTEM 4 A brief overview of an expert system An expert system is a computer application that uses rules, approaches, and facts to provide solutions to complex problems. There are three components in an expert system: User Interface, Inference Engine Knowledge Base Inference Engine: Inference Engine is a component of the EXPERT SYSTEM that applies logical rules to the knowledge base to deduce new information. It interprets and evaluates the facts in the knowledge base in order to provide an answer. A knowledge base is a structured collection of facts about the system’s domain.
- 5. FORWARD CHAINING EXAMPLE A A->B B A Is The Starting Point A->B Represents a Fact This Fact Is Used To Achieve A Decision B A Practical Example Will Go As Follows; Tom Is Running (A) If A Person Is Running, He Will Sweat (A->B) Therefore, Tom Is Sweating, (B) 5
- 6. BACKWARD CHAINING Backward Chaining Is A Concept In Artificial Intelligence That Involves Backtracking From The Endpoint Or Goal To Steps That Led To The Endpoint. This Type Of Chaining Starts From The Goal And Moves Backward To Comprehend The Steps That Were Taken To Attain This Goal. The Backtracking Process Can Also Enable A Person Establish Logical Steps That Can Be Used To Find Other Important Solutions. 6
- 7. BACKWRDCHAINING 7 ADVANTAGES The result is already known, which makes it easy to deduce inferences. It’s a quicker method of reasoning than forward chaining because the endpoint is available. In this type of chaining, correct solutions can be derived effectively if pre-determined rules are met by the inference engine. DISADVANTAGES The process of reasoning can only start if the endpoint is known. It doesn’t deduce multiple solutions or answers. It only derives data that is needed, which makes it less flexible than forward chaining.
- 8. BACKWARD CHAINING EXAMPLE Backward chaining can be explained in the following sequence. B A->B A B is the goal or endpoint, that is used as the starting point for backward tracking. A is the initial state. A->b is a fact that must be asserted to arrive at the endpoint B. A practical example of backward chaining will go as follows: Tom is sweating (B). If a person is running, he will sweat (a->b). Tom is running (a).A 8
- 9. FIRST ORDER LOGIC 9 A brief overview of an expert system First-order logic is another way of knowledge representation in artificial intelligence. It is an extension to propositional logic. FOL is sufficiently expressive to represent the natural language statements in a concise way. First-order logic is also known as Predicate logic or First-order predicate logic.
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