Artificial Intelligence - Comparison Between Forward and Reverse Reasoning
In the realm of artificial intelligence, two primary reasoning strategies are employed in the design of expert systems: forward chaining and backward chaining. These strategies differ in their approach to deriving conclusions from a given set of facts.
Forward chaining is a data-driven strategy that starts with known facts in the knowledge base and applies inference rules to deduce new facts or reach conclusions. Essentially, it begins with available data and works towards a goal by continuously applying rules until the goal is achieved. This approach is useful when all or most facts are available, and the system seeks to discover what conclusions can be drawn from them.
On the other hand, backward chaining is a goal-driven strategy. It starts with a specific goal or hypothesis and works backwards through the rules to find if the known facts support this goal. The system traces back from the conclusion, checking which facts must be true to achieve it, and verifying those facts recursively. This method is efficient when trying to prove or disprove a particular conclusion, especially where many potential outcomes exist but only a few are relevant.
These methods influence the design of the expert system in several ways:
| Aspect | Forward Chaining | Backward Chaining | |----------------------|-------------------------------------------------|----------------------------------------------| | Reasoning Approach | Starts from facts, moves forward to derive goals | Starts from goals, works backward to find supporting facts | | Use Case | Best when lots of data/facts are known upfront | Best when focusing on specific hypotheses/goals | | Direction | Bottom-up | Top-down | | Suitable Applications | Monitoring, real-time systems | Diagnostic, planning systems | | Example | Inferring all possible consequences from sensors | Diagnosing disease by confirming symptoms |
An example of backward chaining in practice is the MYCIN expert system designed for medical diagnosis, which starts with a suspected diagnosis and works back to verify supporting evidence from symptoms.
In essence, forward chaining moves from data to conclusions, while backward chaining moves from goals to data to validate conclusions within expert systems in AI. Forward reasoning moves from facts to a conclusion, while backward reasoning moves backwards from a conclusion to find facts that support the conclusion.
Technology and artificial-intelligence are intertwined in the design of expert systems, as two primary reasoning strategies - forward chaining and backward chaining - are used to derive conclusions from a given set of facts. While forward chaining is a data-driven strategy that starts with known facts and moves towards findings, backward chaining is a goal-driven strategy that works backwards from a specific goal to find supporting facts.
