3. Reasoning: Goal Trees and Rule-Based Expert Systems

Reporting on a Goal Tree program

A Goal Tree program can answer questions about its own behavior by reporting steps up (why questions) or down (how questions) in the actions it takes.

Simon’s Ant

The complexity of a program’s behavior is a consequence of the environment, not the complexity of the program.

Complexity (behavior) = Max ( Complexity (Program) , Complexity (Environment) )

An ant’s apparently complex behavior stems from the obstacles in its environment to avoid on its way home.

Rule-Based Expert Systems

Rule-based “expert” systems are deductions systems, they can answer questions about their behaviors as they are a form of goal trees.

Forward-chaining rule-based expert systems work from characteristics to deduce a result by a set of rules. Backward-chaining rule-based expert systems verify a proposition is true by going back to the check its characteristics through the rules.

Heuristics of knowledge engineering

How to set the program’s rules:

  1. Deal with specific cases: know all details of each specific case, not vague, general ideas from people (potato chips, tomatoes vs “squishy”)
  2. Understand the vocabulary items that make two cases different (frozen vs canned)
  3. Analyze when the program breaks down to understand the missing rule.