Introduction to Backward Chaining
Backward Chaining is an inference method of reasoning in the field of Artificial Intelligence. It refers to the process of backtracking from the goal or endpoint to previous steps which led to the goal itself. It is a goal-driven inference algorithm to find solutions where the end goal is defined.
For e.g., Various retirement or savings calculators use such algorithms where, if you need 1 million when you retire, it’ll help identify the sum required at the beginning to invest or monthly payments required to reach that desired goal.
Need for Backward Chaining
Development of Artificial Intelligence has been going on for many years, it made on certain predefined systems or paths that the machine or AI follows to provide logical solutions. There are certain challenges where the end goal in a path or series of logical steps is predetermined and the reverse step is required to build reverse or backtracking the logical steps to determine a solution in mind. It aims to find the steps through which the given solution was achieved.
This is where backward chaining shines which works similar to its counterpart, Forward Chaining but with a change that it starts from the endpoint to find logical solutions. It’s used in almost every field where AI has made inroads and is a series of processes of inferring unknown results from known conclusions.
How Does Backward Chaining Work?
As discussed, backward Chaining starts with certain conclusions or Goals and then works backward to understand the steps taken to achieve those goals and whether we can logically find out any inference or logical steps to find out other solutions.
In order to understand how it works, we need to understand the ‘Then’ concept,
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Examples of Backward Chaining
Rule 1: If X is Cheetah then X is fast or if Y is Fast.
Rule 2: If Its Cheetah and Fast then it has Dots on the body.
Now taking the above example of a cheetah, if an animal is to be identified and ascertain if its a cheetah, we can derive it from the information we have in hand. We have an unidentified animal named Tony from a zoo and we need to identify if it’s a cheetah.
Tony has dots on his body is the goal we have. Now the system will see based on the inferences if it has if the animal has Dots on the body and see if it matches the inference.
“If Tony is a Cheetah then Tony is a Cheetah”.
From this, we can identify that Tony is a cheetah.
“If Tony is Cheetah then he has dots on his body”.
From this, we can identify that Tony is a Cheetah who has Dots on his body. In the above example, the rules were the inference engines through which logical implication can be derived. Also, it’s called a Top-Down approach and backward chaining mostly uses Depth First Search to traverse through the graph to derive inferences searching and expanding all possible nodes. It also generates only a finite number of possibilities whereas its counterpart Forward chaining can generate infinite inferences.
Uses of Backward Chaining
Below are uses of Backward Chaining:
- It can be used in the financial services, markets research industry where certain outcomes are known and inferences can be deduced to decide and take necessary steps to either change that outcome or processes to actually reach that goal. Like if returns on investment need to be at 25%, inferences can be ascertained to achieve those targets or goals.
- In Medicine, when a disease needs to be ruled out inference can be deduced to see if there are logical patterns directing towards that disease and if not, we can safely rule out that disease. All of this is possible to backward chaining algorithms where a very large volume of data can be processed and results ascertained in mere seconds.
- In the Manufacturing Sector, Faults can be found out in certain units or processes by making assumptions for results and then running the backward chaining algorithms. It provides analytical ability to make
- Advancements in AI using both Backward and Forward Chaining algorithms has and will continue to affect almost all possible sectors be Legal, Medicine, Production, IT and others in one way or another. Also, certain programming languages like Prolog, knowledge Machine and the likes, support for backward chaining is increasing drastically opening up new
Advantages & Disadvantages of Backward Chaining
Below are the advantages and disadvantages of Backward Chaining:
Advantages
- Backward chaining is advantageous when the result is known and inferences are to be deduced.
- Therefore it is also quicker than Forward chaining where multiple conclusions can be drawn.
- It is an efficient way to reach the desired solution as it majorly depends upon the inference engine and certain rules which are pre-defined. If the inference meets those rules it can effectively and efficiently derive correct solutions.
- Backward chaining checks only check for the required rules, hence it’s faster than forward chaining where all the rules are checked.
Disadvantages
- In backward chaining, the goal has to be known in advance.
- If multiple answers or solutions are to be derived to exercise or be given the best possible solution for users to choose, backward chaining fails as it deduces only one correct solution and therefore denies multiple meaningful insights for users.
- It is therefore also less flexible as compared to forward chaining as only data needed is derived from it and the user is limited to that and not given choice to derive multiple conclusions which is a major business need for decision making.
Conclusion
Backward Chaining is an important tool along with Forward Chaining as both have distinct use cases and cannot be used together. They are important for anyone to understand how Artificial Systems work and how we can enable Intelligence among systems to provide logical solutions based on certain rules or inference engines. This is especially important to learn for anyone looking to study to advance their career in Artificial Intelligence or even Search or Database management systems as its key in designing solutions for complex database systems across domains.
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