Expert System

Computer systems which try to mimic human expertise to produce a decision that does not require judgment. Artificial Intelligence (AI)  -  group of technologies that attempt to emulate certain aspects of human behaviour, such as reasoning and communicating.Expert systems are the most important product of AI research to date.

Expert system is a knowledge-based system:

• provides specific knowledge about a narrow problem domain
• knowledge stored in the knowledge base
• system uses knowledge and an inferencing (reasoning) procedure to solve problems that would otherwise require human competence or expertise.

The power of expert systems stems primarily from the specific knowledge about a narrow domain stored in the expert system's knowledge base. It is important to stress to students that expert systems are assistants to decision makers and not substitutes for them. Expert systems do not have human capabilities. They use a knowledge base of a particular domain and bring that knowledge to bear on the facts of the particular situation at hand. The knowledge base of an ES also contains heuristic knowledge - rules of thumb used by human experts who work in the domain.

Applications of Expert Systems

The test outlines some illustrative minicases of expert systems applications. These include areas such as high-risk credit decisions, advertising decision making, and manufacturing decisions.

Classification - identify an object based on stated characteristics

Diagnosis Systems - infer malfunction or disease from observable data

Monitoring - compare data from a continually observed system to prescribe behaviour

Process Control - control a physical process based on monitoring

Design - configure a system according to specifications

Scheduling & Planning - develop or modify a plan of action

Generation of Options - generate alternative solutions to a problem

The strength of an ES derives from its knowledge base - an organized collection of facts and heuristics about the system's domain. An ES is built in a process known as knowledge engineering, during which knowledge about the domain is acquired from human experts and other sources by knowledge engineers. 

The accumulation of knowledge in knowledge bases, from which conclusions are to be drawn by the inference engine, is the hallmark of an expert system.

To use an expert system,

(1) gather input problem variables and criteria

(2) consult computerized base of knowledge

(3) system reasons out an answer ES often assistants to decision makers and not substitutes for them

i.e. use ES to help DM with part of a larger problem

Let us discuss about the components of expert system and its environment types with a block diagram.we are having two environments of Expert System,

• Development Environment

• Consultation environment

 

Components of Expert System:

1)         knowledge base

            2)         inference engine

            3)         knowledge acquisition module

            4)         explanatory interface

Knowledge base - a declarative representation of the expertise, often in IF THEN rules

2 types of info: 

(1) book knowledge about a domain

(2) heuristic knowledge - rules of thumb used by human experts who work in the domain

The knowledge base of an ES contains both factual and heuristic knowledge. Knowledge representation is the method used to organize the knowledge in the knowledge base. Knowledge bases must represent notions as actions to be taken under circumstances, causality, time, dependencies, goals, and other higher-level concepts.

Several methods of knowledge representation can be drawn upon. Two of these methods include:

1. Frame-based systems 

- are employed for building very powerful ESs. A frame specifies the attributes of a complex object and frames for various object types have specified relationships.

2. Production rules 

- are the most common method of knowledge representation used in business. Rule-based expert systems are expert systems in which the knowledge is represented by production rules.

A production rule, or simply a rule, consists of an IF part (a condition or premise) and a THEN part (an action or conclusion). IF condition THEN action (conclusion).

Working storage - the data which is specific to a problem being solved

Inference engine - the code at the core of the system 

The inference engine:

1. Combines the facts of a specific case with the knowledge contained in the knowledge base to come up with a recommendation. In a rule-based expert system, the inference engine controls the order in which production rules are applied.

2. Directs the user interface to query the user for any information it needs for further inferencing.

The facts of the given case are entered into the working memory, which acts as a blackboard, accumulating the knowledge about the case at hand. The inference engine repeatedly applies the rules to the working memory, adding new information (obtained from the rules conclusions) to it, until a goal state is produced or confirmed.

 One of several strategies can be employed by an inference engine to reach a conclusion. Inferencing engines for rule-based systems generally work by either forward or backward chaining of rules. Two strategies are:

1. Forward chaining

- is a data-driven strategy. The inferencing process moves from the facts of the case to a goal (conclusion). The strategy is thus driven by the facts available in the working memory and by the premises that can be satisfied. The inference engine attempts to match the condition (IF) part of each rule in the knowledge base with the facts currently available in the working memory. If several rules match, a conflict resolution procedure is invoked; for example, the lowest-numbered rule that adds new information to the working memory is fired. The conclusion of the firing rule is added to the working memory.

Forward-chaining systems are commonly used to solve more open-ended problems of a design or planning nature, such as, for example, establishing the configuration of a complex product.

 

2. Backward chaining

- the inference engine attempts to match the assumed (hypothesized) conclusion - the goal or subgoal state - with the conclusion (THEN) part of the rule. If such a rule is found, its premise becomes the new subgoal. In an ES with few possible goal states, this is a good strategy to pursue.

If a hypothesized goal state cannot be supported by the premises, the system will attempt to prove another goal state. Thus, possible conclusions are review until a goal state that can be supported by the premises is encountered.

Backward chaining is best suited for applications in which the possible conclusions are limited in number and well defined. Classification or diagnosis type systems, in which each of several possible conclusions can be checked to see if it is supported by the data, are typical applications.

 

Explanary interface - system shows the trail of reasoning it used to reach a decision

-     explains the facts it used

-     what rules it applied

-     and in what order

User interface - the code that controls the dialog between the user and the system 

Knowledge acquisition module:

Domain expert – currently experts solving the problems the system is intended to solve 

Knowledge engineer - encodes the expert's knowledge in a declarative form that can be used by the expert system 

Benefits of Expert System:

An Es can complete its part of the tasks much faster than a human expert.

2. The error rate of successful systems is low, sometimes much lower than the human error rate for the same task.

3. ESs make consistent recommendations

4. ESs are a convenient vehicle for bringing to the point of application difficult-to-use sources of knowledge.

5. ESs can capture the scarce expertise of a uniquely qualified expert.

6. ESs can become a vehicle for building up organizational knowledge, as opposed to the knowledge of individuals in the organization.

7. When use as training vehicles, ESs result in a faster learning curve for novices.

8. The company can operate an ES in environments hazardous for humans.