AI concept
Forward and backward chaining are inference techniques used in rule-based AI systems to derive conclusions from facts and rules.
Computer Science AI Problem Lab
Forward and Backward Chaining Interactive Visualizer
Visualize rule-based reasoning as AI moves from facts to conclusions or works backward from a goal to required facts. Learn the model, state transitions, search behavior, and practical AI reasoning flow through an OpenLabs interactive simulator.
AI Visualizer
Facts, rules, goals, and inference paths
Inference Chain
1Fact: A
2Rule: A -> B
3Rule: B -> Goal
4Goal proven
Step 1
Load facts
Step 2
Match rule
Step 3
Infer conclusion
Step 4
Prove goal
Reasoning flow
Forward chaining starts with known facts and applies rules to infer new facts, while backward chaining starts with a goal and searches for rules that prove it.
Model focus
Facts, rules, goals, and inference paths
Visualization
Trace each state, decision, or rule step through an interactive learning flow.
Learn by simulating
Understand Inference Chaining with step-by-step AI reasoning
Forward chaining starts with known facts and applies rules to infer new facts, while backward chaining starts with a goal and searches for rules that prove it. The lab turns abstract AI problem solving into a visible sequence of states, decisions, and results.
Understand the difference between data-driven and goal-driven inference.
Trace how rules fire from known facts.
Visualize how a goal can be proven by searching backward.
Connect chaining with expert systems and knowledge bases.
Where this AI concept is used
- Expert systems
- Medical decision rules
- Troubleshooting systems
- Knowledge-base reasoning
How the interactive lab works
Open the Inference Chaining lab, adjust the problem inputs, and follow how the visualizer updates each state or inference step. Use it to compare AI theory with observable behavior.
Curriculum Continuity
View All AI ProblemsExplore Sibling AI & Search Experiments
Variables, domains, and constraintsBacktracking search with constraint checking
Constraint Satisfaction Problem
Explore how AI solves constraint satisfaction problems by assigning values to variables while respecting rules and pruning invalid choices.
Search and guessing strategyState space search and probabilistic guessing
Hangman AI Problem
Study how an AI agent can reason about hidden words, update possible candidates, and choose letters based on available evidence.
Local search optimizationHeuristic optimization over neighboring states
Hill Climbing Search
Watch hill climbing move from one state to a better neighboring state until it reaches a peak or gets stuck in a local optimum.
Reinforcement learningStates, actions, rewards, and Q-value updates
Maze Q-Learning
Explore reinforcement learning by watching an agent move through a maze, collect rewards, update Q-values, and learn better paths.
Classical AI planningState-space planning and goal achievement
Monkey Banana Problem
Visualize how an AI planner solves the classic monkey banana problem by sequencing actions to reach a goal state.
State-space searchState transitions and goal search
Water Jug Problem
Solve the classic water jug problem by pouring, filling, and emptying jugs while tracing each state toward the target amount.
Deep learning & backpropagationForward pass activations, loss gradients, and weight updates
Multilayer Perceptron Neural Network
Interactive neural network simulator: configure layers, train on non-linear datasets, adjust learning rates, and visualize real-time decision boundary convergence.
Inference Chaining FAQs
What is forward chaining?
Forward chaining starts from known facts and applies rules to infer new conclusions.
What is backward chaining?
Backward chaining starts with a goal and works backward to find facts and rules that prove it.
Where is chaining used in AI?
It is used in expert systems, rule engines, diagnosis tools, and knowledge-based reasoning systems.
Ready to explore Inference Chaining?
Launch the visualizer and turn AI search, reasoning, and planning into a clear hands-on learning path.