Unit-1

EXPERT SYSTEM

Comparison of Human Vs Expert system

Contd..

Structure of Expert System

Expert system Differ From conventional Program

Basic Activities for Expert Systems

• T• 1. The interpretation of data• Such as sonar data or geophysical measurements• 2. PREDICTION :-• inferring likely consequences of given situations• 3. Diagnosis of malfunctions• Such as equipment faults or human diseases• 4. Design :- Configuring objects under constraints.

• .

• 5. Monitoring:- comparing observations to expected outcomes.

• 6.Planning: designing Actions• 7. Debugging: prescribing remedies for

malfunctions• 8. Repair :- Executing plans to administer

prescribed remedies• 9. Instruction : diagnosing ,debugging & reparing.• 10. Control:- Governing overall system behavior

Features of prospector

PROSPECTOR: Operational details

• PROSPECTOR performs a consultation to

determine such things as • • which model best fits the data • where the

most favourable drilling sites are located • • what additional data would be most helpful in

reaching firmer conclusions • • what is the basis for these conclusions and

recommendations

Unit-2

EXPERT SYSTEM

knowledge representation in expert systems

• Semantic NetsThe semantic net technique describes the state of the world through a collection of nodes that represent objects, object properties, concepts, events, and arcs of links for a discussion of the graph data structure. For example, shows the bank accounts as a semantic net. Like an object-oriented class structure, the objects at the bottom of the top-down hierarchy inherit properties from higher-levels but also possess unique properties. For example, an individual checking account inherits such properties as an account number and an account type, but such properties as overdraft protection, minimum balance, and linkages to other accounts can change from account to account.

• With a semantic net, important associations and relationships can be described explicitly, and the inheritance hierarchy is easy to understand and revise. Consequently, it is easier to add new situations to a semantic net than to a set of slots and frames. Also, the relevant facts can be found within the hierarchy, so it is not necessary to search through a large database to find specific information. Establishing an inheritance hierarchy is a difficult task, however, and determining the unique (non-inherited) properties for each low-level node calls for (subjective and/or objective) judgment.

Frames and slots • A frame is a complex data structure representing a

stereotyped situation, such as an object, an activity, or a person. Slots are frame-like structures for representing stereotyped sequences of events or values. For example, in a frame that describes a bank account, the slots can be used to represent the account number, the account type, and the account balance. Generally, a frame is composed of a concept, one or more slots, one or more values, and one or more attached procedures

• Fig shows a set of frames that represent information about bank accounts. A bank representative opens the account file for a customer by soliciting, entering, and verifying all the required information. The associated expert system then automatically triggers an attached procedure that asks the representative to select a transaction type (for example, add a new customer, update customer information, delete a customer, and so on). The expert system then responds by triggering the appropriate procedure

Forward vs.Backward Reasoning

• Forward Reasoning• given a set of basic facts, we try to derive a• conclusion from these facts.• Backward Reasoning• try to find supportive evidence (i.e. facts) for

a• hypothesis.

• Forward Reasoning Backward Reasoning

• planning, control diagnosis

• data-driven goal-driven (hypothesis)

• bottom-up reasoning top-down reasoning

• find possible conclusions find facts that support a given hypothesis• supported by given facts•• similar to breadth-first search similar to depth-first search

• antecedents (LHS) control consequents (RHS) control Evaluation• Evaluation•

TYPES OF TOOLS AVAILABLE FOR EXPERT SYSTEM BUILDING

Expert SystemProblem oriented

Symbol manipulation

Skeletal

General purpose

Knowledge acquisition

Design

Programming

Explanation

Prog. Lang

Knowledge Engg. Lang

System building tool

Support Facilities

Stages in the development of Expert system Tools

EXPERIMENTAL SYSTEM

Research System Commercial System

Programming Methods supported by Expert system Tools

• Rule Based METHOD• use if-then rules to perform fwd & Bckward

Chaining • TOOL: EMYCIN

• Frame based method• Use frame hierarchies for inheritance & procedural

attachment.• Tool: SRL

• PROCEDURE ORIENTED METHOD• Uses nested subroutines to organize and

control program executionTool:-LISP

OBJECT ORIENTED METHODUsess items called objects that communicate

with one another via messages.Tool: SMALL TALK

• LOGIC BASED METHOD• Uses predicate calculus to structure the program

and guide execution• Tool:- PROLOG

• Access-oriented• Uses probes that trigger new computations when

data are changed or read• Tool:- LOOPS

Phases of Expert System

• Identification phase• The first step in the identification phase,

Identify problem, is similar to the problem definition phase in the traditional systems development life cycle. The objective is to identify, characterize, and define the problems the system will be expected to solve and then partition the problem into appropriate sub-tasks.

• Once the problem is defined, the resources necessary for acquiring knowledge, implementing the system, and testing the system are identified. Typical resources include knowledge, time, computing facilities, and money. Because expert systems are expensive and creating one takes considerable time, a feasibility study is often conducted before work progresses beyond this point.

• In addition to identifying resources, the expert system analysts and/or designers also identify the system’s goals and objectives. It is helpful to identify and explicitly document the goals because certain design approaches, such as heuristic search, breadth search, depth search, and reasoning are goal-driven.

Conceptualization phase• The central task of the conceptualization phase is to

diagram the system’s key concepts and relations to define a conceptual base for a prototype system. Key objectives include separating the inference engine from the problem domain, factoring (analyzing) the problem into meta-problems, identifying the system’s key concepts and relations, and testing those concepts and relations by challenging them (with specific examples of problem-solving activities) to ensure that they cover every general case. Many of the tools and techniques described in Part II are used in this phase.

Formalization Phase

• The formalization phase involves mapping key concepts, sub-problems, and information flow characteristics isolated during conceptualization into more formal representations based on various knowledge engineering and problem solving tools and knowledge representation frameworks . The key objectives are to identify the solution space (a domain with a collection of all possible solutions), the hypothesis space (the hypothetical solution space), the underlying model, and the characteristics of the data.

System design phase

• During the system design phase (sometimes called the logical design phase) the analyst and/or designer specifies how the system will meet the requirements identified during the previous three phases. Typically, the reports and other outputs the systems must produce are defined first. This phase is similar to the design stage in the traditional systems development life cycle

System development phase

• A prototype expert system is created during the system development (or physical design) stage. This stage is similar to the development stage in the traditional system development life cycle.

Testing and evaluation phase

• During this phase, the prototype system is evaluated. This phase parallels the testing stage in the traditional system development life cycle.

• Prototype revision phase• An expert system evolves over time, calling for

almost constant revision, a trait expert systems share with most prototypes. Based on the results of the testing/evaluation phase, concepts and relations are refined, the solution space, the model, and the data characteristics are reformalized, and the system is redesigned

EXPERT SYSTEMS BUILDING TOOLS: DEFINITIONS

• expert system tool, or shell, is a software development environment containing the basic components of expert systems. Associated with a shell is a prescribed method for building applications by configuring and instantiating these components. The core components of expert systems are the knowledge base and the reasoning engine.