IMAT3406 Fuzzy Logic and Knowledge Based Systems (AI)

1. IMAT3406 Fuzzy Logic and Knowledge Based Systems (AI) Knowledge Representation - II

2. Topics of Discussion • Discussion on lessons learned in the last week’s tutorial: Developing a KBS using the scripts and semantic networks • More knowledge representation techniques. • Lists • Decision Tables • Decision Trees • Rule-based production systems • Comparison of Different Knowledge Representations • Multiple Knowledge Representation

3. O-A-V triplet • Objects (O) may be physical or conceptual (e.g., house) • Attributes (A) are the characteristics of the objects (e.g., bedrooms). An object may have more than one attribute • Values (V) are the specific measures of the attributes (e.g., 4) • O-A-V triplets are used in frame & semantic networks • O-A-V triplets can also be used to show clear order and relationships by using a tree structure IS A SAM VP Object Attribute Value

4. Lists and decision tables & trees • Lists and Decision Trees are simple structures used for representing hierarchical knowledge, especially in symbolic AI. • Another method of knowledge representation is the decision table, which is organised in a spreadsheet format, using columns and rows. • Decision tables as a data structure is a popular choice for developers and heavily used in systems and applications such as databases.

5. Lists • Lists are simple structures used for representing hierarchical knowledge, especially in symbolic AI. • Lists are used to represent knowledge in which objects are grouped, categorised, or graded • For lists, objects are first divided into several sub-groups of similar items. Then, their relationships are shown by linking them together. For this, more than one list can be used and then combined • See (handouts) Fig 5.9: Lists Representing Hierarchical Knowledge

6. LISP for Lists • LISP, for list processing, is a programming language/shell to manipulate lists and symbolic data. • It is very popular in what is known as symbolic AI, where symbols are used to represent qualitative, rather than quantitative, features of objects, events or concepts. • Widely used for the AI works in the US & the UK. • It has similar advantages and disadvantages as Prolog. • LISP applications: • NASA’s The Box: A prototype of a next-generation computer-based training system designed to help pilots transition into modern glass cockpit aircraft. • More applications onhttp://www.lisp.org/table/applications.htm • For further details, see www.lisp.org

7. Decision Tables • Decision tables are organised in a spreadsheet format, using columns and rows • The tables are divided into two main parts: • Attributes • for each attribute, all possible values are listed • Conclusions • The different configurations of attributes are matched against the conclusions • See (handouts) Fig 5.10

8. Decision trees • Decision Trees as in the Lists are simple structures used for representing hierarchical knowledge, especially in symbolic AI. • Decision trees are related to tables and are used frequently in system analysis (in non-AI systems). • They couple search strategy with knowledge relationships. • A decision tree may be seen as a hierarchical semantic network bound by a series of rules • They are composed of two main components: • 1. Nodes representing goals • 2. Links representing decisions.

9. Decision trees • The root of the tree is on the left and the leaves on the right • All terminal nodes except the root node are instances of a primary goal. • Main advantage is that they can simplify the KA process. • Knowledge diagramming used in the decision trees is frequently more natural to experts than formal representation methods such as rules and frames • See example (handout) Fig 5.11 and Fig 5.12

10. Rule-based production systems • Rule-based production systems are probably the most popular and commonly used form of knowledge representation in AI. • Rules can be viewed, in some sense, as a simulation of the cognitive behaviour o human experts • Sometimes, especially in the context of robotics, we may refer to rule-based systems as behaviourist model. • Also we may refer to it as an expert system, especially in the context of information systems.

11. Rule-based production systems • The production systems are modular knowledge representation schemes that are finding increasing popularity in many AI applications. • The basic idea of these systems is that knowledge is presented as production rules in the form of condition-action pairs: IF this condition (or premise or antecedent) occurs, THEN some action (or result, or conclusion, or consequence) will (or should) occur.

12. Rule-based production systems There are three major types of rules used in the rule-based production systems: • Knowledge Declarative Rules state all the facts and relationships about a problem. They are a part of the knowledge base • (e.g.) IF inflation rate declines THEN the price of gold goes down • Inference Procedural Rules advise on how to solve a problem, given that certain facts are known. They are a part of the inference engine • (e.g.) IF the data needed is not in the system THEN request it from the user • Metarules (rules about rules) pertain to other rules (or even to themselves)

13. Rule-based production systems Advantages: Rules are easy to understand Inference and expectations are easily derived Modifications and maintenance are relatively easy Uncertainly is easily combined with rules Each rule is usually independent of all others Limitations: Complex knowledge requires many rules Limited to rules rather than looking for other types of representations Create difficulties in inference stage

14. CLIPS for the Rule-based production systems • In developing rule-based systems, we commonly use expert systems shells such as CLIPS • CLIPS was created in 1985 and is now widely used throughout the government, industry, and academia. • CLIPS is a productive development and delivery expert system tool which provides a complete environment for the construction of rule and/or object based expert systems • CLIPS uses the three types of rules • For further details including its key features, see http://www.ghg.net/clips/WhatIsCLIPS.html

15. Scheme Production rules Semantic Networks Frames Formal Logic Advantages Simple syntax, east to understand, highly modular & flexible Easy to follow hierarchy, to trace associations, flexible Expressive power, easy to set up slots, to create specialised procedures, to include default information and to detect missing components All and only valid consequences are asserted Disadvantages Hard to follow hierarchies, inefficient for large systems, not all types of knowledge can be expressed in the form of rules, poor at presenting structured descriptive knowledge Meaning attached to nodes might be ambiguous, different meanings to different users Difficult to program, lack of software Inefficient with large data sets, very slow with large knowledge bases Comparison of Different Knowledge Representations

16. Multiple Knowledge Representation • Each technique used for knowledge representation has advantages and disadvantages • No single KR method is ideally suited by itself for all three main tasks in developing a KBS • KR methods can be combined to complement each other • Some recent expert system shells use two or more KR schemes • KEE, Level5Object and Nexpert Object are some of the examples to a hybrid system where frame and production-rule languages were integrated to form a hybrid KR.

17. Conclusion • Different techniques of knowledge representation have been covered. • Each technique has advantage and disadvantage. • Type of knowledge and knowledge analysis indicate which technique to use • Multiple Knowledge Representation can be useful

18. References • Lecture notes part 2. • E. Turban, Expert Systems and Applied Artificial Intelligence. New York: Macmillan Publishing Company, 1992.

19. Next Steps • Next … • Inferencing.