Forward Chaining
Forward Chaining is a data-driven reasoning approach. It starts with known facts in working memory, matches rule conditions (IF parts), resolves conflicts between candidate rules, and fires actions to infer new facts until no further conclusions can be drawn.
Category:Rule-Based Systems
•Difficulty:Beginner
•Time complexity:O(R * F)
•Space complexity:O(F)
Interactive VisualizationForward Chaining Visualizer
PHASE: INITIALIZEDData-driven inference (matching IF conditions to derive new facts)
Resolution Strategy:
Knowledge Base (Rules)
Production rules structured as IF (antecedents) → THEN (consequent)
R1
IFfeverANDcough
THENflu_suspected
R2
IFflu_suspectedANDbody_ache
THENflu_likely
R3
IFflu_likely
THENadvise_rest
R4
IFrash
THENallergy
Working Memory
Known facts in the current state
Working memory is empty.
Conflict Set
Matching rules ready to fire
No rules in conflict set.
0/0
Step Trace & Explanation-
Action:Click 'Run Inference' to start simulation.
Forward Chaining Pseudocode
1.repeat
2. conflictSet = rules whose IF part matches working memory
3. if conflictSet is empty: stop
4. rule = resolve(conflictSet)
5. fire(rule): add its THEN fact to working memory
6.until no rule matches
Real-time Telemetry HUD
Live StreamNodes Explored
0Frontier (Open Set)
0Optimal Cost g(n)
—Total Step Frames
0