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Define Control Strategy and requirements for good search strategy – Artificial Intelligence
Production systems in artificial intelligence consist of:
1. A set of rules, “each consisting of a left side (a pattern) that determines the applicability of the rule and a right side that describes the operation to be performed if the rule is applied.”
In the Water Jug Problem, Rule (x, y) is x<4 ->(4, Y) indicates Fill the 4-gallon jug and (x, y) if y<3 -> (x, 3) indicates Fill the 3-gallon jug.
2. One or more knowledge/databases “that contain whatever information is appropriate for the particular task. Some plans of the database may be permanent, while other plans of it may pertain only to the solution of the current problem.”
3. A control strategy “that specifies the order in which the rules will be compared to the database and a way of resolving the conflicts that arise when several rules match at once.”
3. A rule applier.
Given a problem definition, it is very difficult to decide which rule to choose from a set of rules which can solve the problem efficiently.
This question usually arises when we have more than one rule (and sometimes fewer than one rule) that will match with the left side of the current state. Even without a great deal of thought, it is clear that such decisions will have a crucial impact on how quickly. and even whether, a problem is finally solved.
There are mainly two requirements to choose a control strategy.
The first requirement of a good control strategy is that it causes motion.
Example: Consider the water jug problem. Suppose we implemented the simple control strategy of starting each time at the top of the list of rules and choosing the first applicable one. If we did that, we would never solve the problem. We would continue indefinitely filling the 4-gallon jug with water Control strategies that do not cause motion will never lead to a solution.
The second requirement of a good control strategy is that it be systematic.
Example: Consider the water jug problem. On each cycle, choose at random from among the applicable rules. This strategy is better than the first. It causes motion. It will lead to a solution eventually. But we are likely to arrive at the same state several times during the process and to use many more steps than are necessary. Because the control strategy is not systematic, we may explore a particular useless sequence of operators several times before we finally find a solution.
Search is the systematic examination of states to find paths from the start/root state to the goal state.
Many traditional search algorithms are used in AI applications. For complex problems, the traditional algorithms are unable to find the solution within some practical time and space limits. Consequently, many special techniques are developed; using heuristic functions. The algorithms that use heuristic functions are called heuristic algorithms. Heuristic algorithms are not really intelligent; they appear to be intelligent because they achieve better performance.
Heuristic algorithms are more efficient because they take advantage of feedback from the data to direct the search path.
There are mainly two types of search strategies:
1. Uninformed search (blind search)
Also called blind, exhaustive or brute-force search uses no information about the problem to guide the search and therefore may not be very efficient.
2. Informed search (heuristic search)
Also called heuristic or intelligent search, uses information about the problem to guide the search, usually guesses the distance to a goal state, and is therefore efficient, but the search may not be always possible.
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