Backward chaining is a logic system used by AI to work backwards from an end goal through a set of rules. It can be used for various tasks, including identifying objects and solving chess games. The system requires robust programming with logical and inductive rules to arrive at a solution. It examines facts and determines if they fit a given product, discarding incorrect ones. This method can also be used by primates to solve problems.
Backward chaining is a system of logic used by AI systems. It is designed to solve a problem by working backwards from an end goal through a set of rules. This approach can be used by a wide variety of systems, from programs that solve chess games to algorithms used to identify unknown objects. The basis requires robust programming with a set of logical and useful inductive rules that the system can use to precisely move through a set of options to arrive at a solution.
In this method, the system is provided with a set of rules by the programmer, which presents it with an end product or goal. The system works backwards through the rules to determine how the ultimate goal might be arrived at. In backward induction used by programs that solve chess games, for example, the computer can take the positions of the pieces and move through a series of if-then statements to determine the likely course of motion in the game. A computer can also use backward chaining to explore other possible solutions and branches that may have occurred during the game to change the outcome.
Systems that use backward chaining can have rules that vary in complexity, depending on the type of work they need to do. A system that can identify flowers, for example, might need a large set of branching options to accurately pinpoint the species it’s looking at. It might begin with a series of color-related statements, work through flower types, number of petals, foliage, and other characteristics, and determine the identity of any given flower by answering questions at each step to determine a final answer. Errors in this process could lead to identification errors.
This reasoning system requires simple logic. The system examines a fact, determines if it fits a given product, and takes another step from there. If the fact doesn’t match the information available, it is incorrect, and backward chaining logic can discard that fact and others that might arise from it. Facts that fit allow a program to work with logic and explore branching facts to see which fits best. This can work well for a variety of tasks.
AI isn’t the only entity that can use backward chaining. Researchers working with primates note that some species appear to use this logical method to solve problems. This illustrates the ability to understand problems and develop a system for dealing with them.
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