Forward and Backward Chaining

Forward-chaining and backward-chaining are properties of an architecture that refer to the maintenance of knowledge, while forward-planning and backward-planning refer to methods of planning usually utilizing means-ends analysis. Forward-chaining by an architecture means that upon assertion of new knowledge, all relevant inductive and deductive rules are fired to quiescence, effectively making all knowledge about the current state explicit within the state. Forward chaining may be regarded as progress from a known state (the original knowledge) towards a goal state(s). Backward-chaining by an architecture means that no rules are fired upon assertion of new knowledge, but when an unknown predicate about a known piece of knowledge is detected in an operator's condition list, all rules relevant to the knowledge in question are fired until the question is answered or until quiescence. Thus, backward chaining systems normally work from a goal state back to the original state.

From this admittedly superficial description, it seems that backward-chaining, since it saves computation is superior to forward-chaining. However, since knowledge cascades, certain pieces of inductive knowledge can be missed. Additionally, the branching factor (the number of considerations at each state) may vary between forward and backward chaining and thus also drive a consideration of which method is the more efficient. This trade-off between computation and assuredness of knowledge must be measured and decided by the architect of the agent.

Architectures that utilize forward chaining engines:

Architectures that utilize backward chaining engines:

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