Knowledge based expert systems
Posted by ljmacphee on April 26, 2007 under source code, topics in artificial intelligence | 
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There are knowledge based agents and expert systems that reason using rules of logic. These systems that do what an expert in a given field might do, tax consulting, medical diagnosis etc. They do well at the type of problem solving that people go to a university to learn. Usually predicate calculus is used to work through a given problem. This type of problem solving is known as ’system inference’. The program should be able to infer relationships, functions between sets, some type of grammar, and some basic logic skills. The system needs to have three major properties: soundness, confidence that a conclusion is true; completeness, the system has the knowledge to be able to reach a conclusion; and tractability, it is realistic that a conclusion can be reached.
Reasoning is commonly done with if-then rules in expert systems. Rules are easily manipulated, forward chaining can produce new facts and backward chaining can check statements accuracy. The newer expert systems are set up so that users, who are not programmers, can add rules and objects and alter existing rules and objects. This provides a system that can remain current and useful with out having to have a full time programmer working on it.
There are three main parts to the expert system: knowledge base, a set of if-then rules; working memory, a database of facts; inference engine, the reasoning logic to create rules and data.
The knowledge base is composed of sentences. Each sentence is a representation of a fact or facts about the world the agent exists in or facts the expert system will use to make determinations. The sentences are in a language known as the knowledge representation language.
Rule learning for knowledge based and expert systems is done with either inductive or deductive reasoning. Inductive learning creates new rules, that are not derivable from previous rules about a domain. Deductive learning creates new rules from existing rules and facts.
Rules are made of antecedent clauses (if), conjunctions (and, or) and consequent clauses (then). A rule in which all antecedent clauses are true is ready to fire or triggered. Rules are generally named for ease of use and usually have a confidence index. The confidence index (certainty factor) shows how true something is, i.e. 100\% a car has four wheels, 50\% a car has four doors. Sometimes sensors are also part of the system. They may monitor states in the computer or environment. The Rete algorithm is the most efficient of the forward chaining algorithms.
Reasoning can be done using ‘Horn Clauses’, these are first-order predicate calculus statements that have, at most, one true literal. Horn Clauses have linear order time algorithms and this allows for a faster method of reasoning through lots of information. This is usually done with PROLOG or lisp. Clauses are ordered as such: goal, facts, rules. Rules have one or more negative literals and one positive literal that can be strung together in conjunctions that imply a true literal. A fact is a rule that has no negative literals. A list of positive literals with out a consequent are a goal. The program loops checking the list in order, when a resolution is performed a new loop is begun with that resolution. If the program resolves its goal the proof can be given in tree form, ‘and/or tree’.
Nonmonotomic reasoning is used to fix problems created by a change in information over time. More information coming in negates a previous conclusion and a new one needs to be drawn.
A conflict resolution process must be put in place as well to deal with conflicting information. This can be done by: first come, first serve; most specific rule is kept; most recently changed data rule triggered; once rule is resolved take it out of the conflict resolution set.
Forward chaining takes the available facts and rules and deduces new facts which it then uses to deduce more new facts, or invoke actions. Forward chaining can also be done by simply the application of if-then statements: The RETE algorithm is the most efficient at doing forward chaining right now, it compiles the rules into a network that it traverses efficiently. This is similar to the blackboard systems.
Dynamic knowledge bases, known as truth maintenance systems, may be used. This uses a ’spreadline’ which is similar to a spread sheet that will calculate missing and updated values as other values entered.
General algorithm forward chaining
load rule base into memory
load facts into memory
load initial data into memory
match rules to data and collect triggered rules
LOOP
if conflict resolution done BREAK
use conflict resolution to resolve conflicts among rules
fire selected rules
END LOOP
Backward Chaining evaluates a goal and moves backward through the rules to see if true. An example is a medical diagnosis expert system, that takes in information from questions then returns a diagnoses. PROLOG systems are backward chaining.
General algorithm backward chaining
load rule base into memory
load facts into memory
load initial data
specify a goal
load rules specific to that goal onto a stack
LOOP
if stack is empty BREAK
pop stack
WHILE MORE ANTECEDENT CLAUSES
if antecedent is false pop stack and NEXT WHILE
if antecedent true fire rule and NEXT WHILE
if antecedent unknown PUSH onto stack (we may later ask user for more information about this antecedent.
END LOOP
The Rete Algorithm is considered to be the best algorithm for forward chaining expert systems. It is the fastest but also requires much memory. It uses temporal redundancy, rules only alter a few facts at a time, and structural similarity in the left hand side of rules to do so.
The Rete is a an acyclic graph that has a root node. The nodes are patterns and the paths are the left hand sides of the rules. The root node has one kind node attached to it for each kind of fact. Each kind node has one alpha node attached to it for each rule and pattern. Then the alpha nodes have associated memories which describe relationships. Each rule has two beta nodes. The left part is from alpha(i) and the right from alpha(i+1). Each beta node stores the JOIN relationships. Changes to rules are entered at the root and propagated through the graph.
Knowledge based agents loop through two main functions. One is to sense the world and TELL the knowledge base what it senses, two is to ASK what it should do about what it senses, which it then does. An agent can be constructed by giving it all the sentences it will need to perform its functions. An agent can also be constructed by a learning mechanism that takes perceptions about the environment and turns them into sentences that it adds to the knowledge base.