1. Artificial Intelligence – CS364 Knowledge Engineering Lectures on Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005 Dr Bogdan L. Vrusias b.vrusias@surrey.ac.uk

2. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 20052 Contents Definitions Basic Process of Knowledge Engineering Case Studies

3. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 20053 Definition Davis’ law: “For every tool there is a task perfectly suited to it”. But… It would be too optimistic to assume that for every task there is a tool perfectly suited to it. The process of building intelligent knowledge-based systems is called knowledge engineering.

4. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 20054 Process of Knowledge Engineering

5. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 20055 Phase 1: Problem assessment Determine the problem’s characteristics. Identify the main participants in the project. Specify the project’s objectives. Determine the resources needed for building the system.

6. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 20056 Phase 1: Problem assessment

7. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 20057 Phase 2: Data and Knowledge Acquisition Collect and analyse data and knowledge. Make key concepts of the system design more explicit. Deal with issue of: –Incompatible data –Inconsistent data –Missing data

8. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 20058 Phase 3: Development of a Prototype System Choose a tool for building an intelligent system. Transform data and represent knowledge. Design and implement a prototype system. Test the prototype with test cases.

9. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 20059 What is a prototype? A prototype system is defined as a small version of the final system. It is designed to test how well we understand the problem – to make sure that the problem-solving strategy, the tool selected for building a system, and techniques for representing acquired data and knowledge are adequate to the task. It also provides us with an opportunity to persuade the sceptics and, in many cases, to actively engage the domain expert in the system’s development.

10. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200510 What is a test case? A test case is a problem successfully solved in the past for which input data and an output solution are known. During testing, the system is presented with the same input data and its solution is compared with the original solution.

11. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200511 Phase 4: Development of a Complete System Prepare a detailed design for a full-scale system. Collect additional data and knowledge. Develop the user interface. Implement the complete system.

12. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200512 Phase 5: Evaluation and Revision of the System Evaluate the system against the performance criteria. Revise the system as necessary. To evaluate an intelligent system is, in fact, to assure that the system performs the intended task to the user’s satisfaction. A formal evaluation of the system is normally accomplished with the test cases. The system’s performance is compared against the performance criteria that were agreed upon at the end of the prototyping phase.

13. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200513 Phase 6:Integration and Maintenance Make arrangements for technology transfer. Establish an effective maintenance program.

14. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200514 Will an Expert System Work for my Problem? The Phone Call Rule: “Any problem that can be solved by your in-house expert in a 10-30 minute phone call can be developed as an expert system”.

15. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200515 Case Study 1: Diagnostic Expert System Diagnostic expert systems are relatively easy to develop: Most diagnostic problems have a finite list of possible solutions, Involve a rather limited amount of well-formalised knowledge, and Often take a human expert a short time (say, an hour) to solve.

16. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200516 Case Study 1: Diagnostic Expert System

17. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200517 Choosing an Expert System Development Tool Tools range from high-level programming languages such as LISP, PROLOG, OPS, C and Java, to expert system shells. High-level programming languages offer a greater flexibility, but they require high-level programming skills. Shells provide us with the built-in inference engine, explanation facilities and the user interface. We do not need any programming skills to use a shell – we enter rules in English in the shell’s knowledge base.

18. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200518 Choosing an Expert System Shell When selecting an expert system shell, we consider: –how the shell represents knowledge (rules or frames); –what inference mechanism it uses (forward or backward chaining); –whether the shell supports inexact reasoning and if so what technique it uses (Bayesian reasoning, certainty factors or fuzzy logic); –whether the shell has an “open” architecture allowing access to external data files and programs; –how the user will interact with the expert system (graphical user interface, hypertext).

19. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200519 Case study 2: Classification Expert System Classification problems can be handled well by both expert systems and neural networks. As an example, we will build an expert system to identify different classes of sail boats. We start with collecting some information about mast structures and sail plans of different sailing vessels. Each boat can be uniquely identified by its sail plans.

20. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200520 Case study 2: Classification Expert System

21. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200521 Case study 2: Classification Expert System

22. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200522 Classification and Certainty Factors Although solving real-world classification problems often involves inexact and incomplete data, we still can use the expert system approach. However, we need to deal with uncertainties. The certainty factors theory can manage incrementally acquired evidence, as well as information with different degrees of belief.

23. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200523 Classification and Certainty Factors

24. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200524 Will a Fuzzy Expert System Work for my Problem? If you cannot define a set of exact rules for each possible situation, then use fuzzy logic. While certainty factors and Bayesian probabilities are concerned with the imprecision associated with the outcome of a well-defined event, fuzzy logic concentrates on the imprecision of the event itself. Inherently imprecise properties of the problem make it a good candidate for fuzzy technology.

25. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200525 Case study 3: Decision-support Fuzzy Systems Although, most fuzzy technology applications are still reported in control and engineering, an even larger potential exists in business and finance. Decisions in these areas are often based on human intuition, common sense and experience, rather than on the availability and precision of data. Fuzzy technology provides us with a means of coping with the “soft criteria” and “fuzzy data” that are often used in business and finance.

26. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200526 Case study 3: Decision-support Fuzzy Systems Mortgage application assessment is a typical problem to which decision-support fuzzy systems can be successfully applied. Assessment of a mortgage application is normally based on evaluating the market value and location of the house, the applicant’s assets and income, and the repayment plan, which is decided by the applicant’s income and bank’s interest charges.

27. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200527 Case study 3: Decision-support Fuzzy Systems

28. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200528 Will a Neural Network Work for my Problem? Neural networks represent a class of very powerful, general-purpose tools that have been successfully applied to prediction, classification and clustering problems. They are used in a variety of areas, from speech and character recognition to detecting fraudulent transactions, from medical diagnosis of heart attacks to process control and robotics, from predicting foreign exchange rates to detecting and identifying radar targets.

29. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200529 Case study 4: Character Recognition Neural Networks Recognition of both printed and handwritten characters is a typical domain where neural networks have been successfully applied. Optical character recognition systems were among the first commercial applications of neural networks.

30. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200530 Case study 4: Character Recognition Neural Networks

31. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200531 Case study 5: Prediction Neural Networks As an example, we consider a problem of predicting the market value of a given house based on the knowledge of the sales prices of similar houses. In this problem, the inputs (the house location, living area, number of bedrooms, number of bathrooms, land size, type of heating system, etc.) are well-defined, and even standardised for sharing the housing market information between different real estate agencies. The output is also well-defined – we know what we are trying to predict. The features of recently sold houses and their sales prices are examples, which we use for training the neural network.

32. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200532 Case study 5: Prediction Neural Networks

33. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200533 Validating the Results To validate results, we use a set of examples never seen by the network. Before training, all the available data are randomly divided into a training set and a test set. Once the training phase is complete, the network’s ability to generalise is tested against examples of the test set.

34. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200534 Case study 6: Classification Neural Networks with Competitive Learning As an example, we will consider an iris plant classification problem. Suppose, we are given a data set with several variables but we have no idea how to separate it into different classes because we cannot find any unique or distinctive features in the data.

35. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200535 Case study 6: Classification Neural Networks with Competitive Learning Neural networks can discover significant features in input patterns and learn how to separate input data into different classes. A neural network with competitive learning is a suitable tool to accomplish this task. The competitive learning rule enables a single-layer neural network to combine similar input data into groups or clusters. This process is called clustering. Each cluster is represented by a single output.

36. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200536 Case study 6: Classification Neural Networks with Competitive Learning For this case study, we will use a data set of 150 elements that contains three classes of iris plants – setosa, versicolor and virginica. Each plant in the data set is represented by four variables: sepal length, sepal width, petal length and petal width. The sepal length ranges between 4.3 and 7.9 cm, sepal width between 2.0 and 4.4 cm, petal length between 1.0 and 6.9 cm, and petal width between 0.1 and 2.5 cm.

37. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200537 Case study 6: Classification Neural Networks with Competitive Learning

38. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200538 Case study 6: Classification Neural Networks with Competitive Learning The next step is to generate training and test sets from the available data. The 150-element Iris data is randomly divided into a training set of 100 elements and a test set of 50 elements. Now we can train the competitive neural network to divide input vectors into three classes.

39. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200539 Case study 6: Classification Neural Networks with Competitive Learning

40. Artificial Intelligence – CS364 Knowledge Engineering 08 th November 2005Bogdan L. Vrusias © 200540 Closing Questions??? Remarks??? Comments!!! Evaluation!