Artificial Intelligence - Forward Chaining and Backward Chaining

Quiz

In developing intelligent systems, reasoning plays a crucial role in drawing conclusions from the existing knowledge. Two primary reasoning methods employed in AI are Forward and Backward Chaining. These techniques allow machines to engage in logical reasoning and tackle complex problems efficiently.

Forward Chaining starts from the known and applies rules step by step to find new facts and ultimately reach a conclusion.
Backward Chaining is performed in the opposite direction, beginning with a conclusion and moving backwards to determine whether any supporting facts are available.

These methods are generally employed in rule-based AI systems like decision support systems, computer-based diagnosis, and expert systems. Knowing how they work will enable us to build intelligent systems that can perform logical reasoning and good decision-making.

Inference Engine

The Inference Engine plays a crucial role in artificial intelligence systems. It draws conclusions from the knowledge within the system and uses reasoning techniques to create new insights based on the given information. This engine is essential for problem-solving and decision-making in AI.

Originally, inference engines were used in expert systems to automate logical deductions. They typically function in two ways:

Forward chaining
Backward chaining

Forward chaining

Forward chaining is a reasoning method where an intelligent system begins with known facts and uses inference rules to derive new information until it reaches a conclusion. This process moves incrementally from established facts to a final conclusion.

As a data-driven approach, forward chaining means that the system examines all possible rules based on the available information before arriving at a definitive judgment.

Properties of Forward-Chaining

The following describes the main properties of forward chaining, a technique of reasoning that begins with facts and uses rules to arrive at a conclusion −

The reasoning process starts with the data and facts present in the system.
Logical rules are used to generate new knowledge from these available facts.
This process is sequential, expanding understanding until a conclusion is reached.
It utilizes existing data to guide the next step instead of beginning with a predetermined goal.
The inference process continues until the system reaches a valid conclusion or there are no more rules to apply.
This method is commonly used in AI decision-making systems, such as diagnostic and recommendation tools. It is most effective when there is a large amount of information available, but the ultimate objective is not well-defined.
However, because it evaluates all potential rules against the available facts, it can be time-consuming to arrive at complex conclusions.

Example 1

Let us imagine there is a customer complaint system designed to handle various issues. When a consumer submits a complaint (fact), the system follows specific rules such as −

If the issue is technical, proceed with troubleshooting steps.
If the issue is related to billing, review the payment history.
If the problem remains unresolved, escalate it to a manager.
The system advances by utilizing facts until it identifies the appropriate solution.

Example 2

Let's say Tutorials Point website have an AI-driven content recommendation system. Where a user types "Python basics" (fact), then the system uses these rules −

If the user is new, recommend basic tutorials.
If the user has finished the beginner course, suggest intermediate topics.
If the user is curious about data science, we suggest Python for Machine Learning.
The system goes step-wise, suggesting appropriate information based on user activity.

This way the system moves from facts to a decision in a step-by-step manner.

Limitations of Forward Chaining

Forward chaining begins with established facts and applies rules to reach a conclusion however, it has significant limitations −

Inefficiency - This method explores a large number of irrelevant rules to achieve the goal, making it computationally expensive.
Lack of Focus - It processes all available rules, including those unrelated to the current problem.
Requires Complete Data - Without the complete data or missing data, it may be difficult to arrive at an appropriate decision.

Backward chaining

Backward chaining is a reasoning method that begins with a specific goal and works backward, applying inference rules to see if existing facts support it. This technique focuses on the goal to find relevant information.

It's particularly useful when the goal is clear, but evidence needs to be established through logical reasoning.

Properties of Backward-Chaining

The following outlines the key properties of backward chaining, a reasoning method that starts with a goal and works backward to find supporting facts −

The process of reasoning begins with a conclusion that needs to be supported. Rather than progressing from facts to conclusions, it works in the opposite direction, starting with the conclusion and looking for evidence to back it up.
It focuses solely on the necessary facts to achieve the desired conclusion or decision.
Each rule is analyzed to determine whether it leads to the desired outcome, with facts verified throughout the process.
The reasoning process is considered successful when the system identifies facts that support its decision.
This approach is often used in medical diagnosis, where an algorithm works backward to determine potential diseases based on observed symptoms.
Instead of sifting through all available data, it focuses on the most relevant information, which can lead to faster results in certain situations.
For this method to be effective, a clear hypothesis or question must be established. If there are no facts that meet the necessary criteria, the algorithm will be unable to reach a conclusion.
This technique is utilized in AI systems that require justification for their decisions, such as legal expert systems and fraud detection.

Example 1

Business Example: A manufacturing company encounters an unexpected increase in product issues. It starts by questioning why there are more problems and then traces back to explore potential causes, including equipment malfunctions, the quality of raw materials, and errors made by personnel.

Example 2

Healthcare Example: In medicine, a doctor tries to figure out if a patient has pneumonia. Rather than checking every possible condition, the diagnostic system begins with the end goal (pneumonia) and looks at specific symptoms like fever, chest pain, and trouble breathing before reaching a conclusion.

Example 3

Crime Investigation Example: The police start a robbery investigation by trying to identify the suspect. They work backwards by looking at evidence watching security footage, and choosing suspects based on motive and past criminal records.

Limitations of Backward Chaining

Backward chaining begins with a specific objective and works backward to gather supporting information however, it has few drawbacks −

Dependence on Goal Selection: It requires a well-defined goal; choosing the wrong goal can result in faulty reasoning.
Complexity: When numerous rules can support a goal, it may be difficult to identify the best option.
Inefficiency: If the goal has many dependencies, it may require extensive backtracking, making the process slow.

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