Lecture 2 Flashcards
How does forward chaining work?
Forward chaining is data-driven reasoning.
The reasoning starts from the known data and proceeds forward with that data.
Each time only the topmost rule is executed.
When fired, the rule adds a new fact in the database.
Any rule can be executed only once.
The match-fire cycle stops when no further rules can be fired.
what is forward chaining?
Forward chaining is a technique for gathering information and then inferring from it whatever can be inferred.
However, in forwarding chaining, many rules may be executed that have nothing to do with the established goal.
Therefore, if our goal is to infer only one particular fact, the forward chaining inference technique would not be efficient.
How does backwards chaining work?
Backward chaining is the goal-driven reasoning.
In backward chaining, an expert system has a goal, and the inference engine attempts to find the evidence to prove it.
First, the knowledge base is searched to find rules that might have the desired solution. Such rules must have the goal in their THEN (action) parts.
If such a rule is found and its IF (condition) part matches data in the database, then the rule is fired and the goal is proved.
However, this is rarely the case.
Thus the inference engine puts aside the rule it is working with (the rule is said to stack) and sets up a new goal, a subgoal, to prove the IF part of this rule.
Then the knowledge base is searched again for rules that can prove the subgoal.
The inference engine repeats the process of stacking the rules until no rules are found in the knowledge base to prove the current subgoal.
What is backward chaining?
Backward chaining is the most common inference method found in expert systems. An inference engine back chains by determining the highest priority goal from user specifications, then asking questions (collecting facts) about rules in order to find a rule or rules that lead to the goal
How do we choose between forward and backward chaining?
If an expert first needs to gather some information and then tries to infer from it whatever can be inferred, choose the forward chaining inference engine.
However, if your expert begins with a hypothetical solution and then attempts to find facts to prove it, choose the backward chaining inference engine.
What is meta-knowledge?
Metaknowledge can be simply defined as knowledge about knowledge. Metaknowledge is knowledge about the use and control of domain knowledge in an expert system.
In rule-based expert systems, metaknowledge is represented by metarules. A metarule determines a strategy for the use of task-specific rules in the expert system.
what is metarule 1?
Rules supplied by experts have higher priorities than rules supplied by novices.
what is metarule 2?
Rules governing the rescue of human lives have higher priorities than rules concerned with clearing overloads on power system equipment.
Advantages of rule-based expert systems
Natural knowledge representation. An expert usually explains the problem-solving procedure with such expressions as this: “In such-and-such situation, I do so-and-so”. These expressions can be represented quite naturally as IF-THEN production rules.
Uniform structure. Production rules have the uniform IF-THEN structure. Each rule is an independent piece of knowledge. The very syntax of production rules enables them to be self-documented.
Separation of knowledge from its processing. The structure of a rule-based expert system provides an effective separation of the knowledge base from the inference engine. This makes it possible to develop different applications using the same expert system shell.
Dealing with incomplete and uncertain knowledge. Most rule-based expert systems are capable of representing and reasoning with incomplete and uncertain knowledge.
Disadvantages of rule-based expert systems
Opaque relations between rules. Although the individual production rules are relatively simple and self-documented, their logical interactions within the large set of rules may be opaque. Rule-based systems make it difficult to observe how individual rules serve the overall strategy.
Ineffective search strategy. The inference engine applies an exhaustive search through all the production rules during each cycle. Expert systems with a large set of rules (over 100 rules) can be slow, and thus large rule-based systems can be unsuitable for real-time applications.
Inability to learn. In general, rule-based expert systems do not have an ability to learn from the experience. Unlike a human expert, who knows when to “break the rules”, an expert system cannot automatically modify its knowledge base, or adjust existing rules or add new ones. The knowledge engineer is still responsible for revising and maintaining the system.
What is conflict resolution?
The inference engine must determine which rule to fire from such a set. A method for choosing a rule to fire when more than one rule can be fired in a given cycle is called conflict resolution.
Methods for conflict resolution?
Fire the rule with the highest priority. In simple applications, the priority can be established by placing the rules in an appropriate order in the knowledge base. Usually this strategy works well for expert systems with around 100 rules.
Fire the most specific rule. It is based on the assumption that a specific rule processes more information than a general one.
Fire the rule that uses the data most recently entered in the database. This method relies on time tags attached to each fact in the database. In the conflict set, the expert system first fires the rule whose antecedent uses the data most recently added to the database.
