Copyright 2004 R. Weber ISYS 370 Fall 2004 Professor: Dr. Rosina Weber

Copyright 2004 R. Weber Data, information, knowledge and knowledge representation ISYS 370 Dr. R. Weber

Copyright 2004 R. Weber Decision Making and Problem Solving gathering informationof alternate strategiesthe best strategyimplementmonitor

Copyright 2004 R. Weber Decision Making and Problem Solving information knowledge knowledge ??

Copyright 2004 R. Weber What is knowledge? procedural knowledge declarative knowledge What is computational knowledge?

Copyright 2004 R. Weber Knowledge representation formalisms frames rules cases semantic nets neural nets

Copyright 2004 R. Weber Let’s play 20 questions?

Copyright 2004 R. Weber Frame of ? Name: ? (goal) Activity: ?, yes Profession: ?, yes Age: ?, no Financial status?, no Marital status?, yes Legal status:

Copyright 2004 R. Weber Frames one representation formalism commonly used in expert systems represents declarative knowledge first introduced by M. Minski in 1975, – A frame is a data-structure for representing a stereotyped situation, like being in a certain kind of living room, or going to a child's birthday party. Attached to each frame are several kinds of information. Some of this information is about how to use the frame. Some is about what one can expect to happen next. Some is about what to do if these expectations are not confirmed.

Copyright 2004 R. Weber (production) rules A logic sequence of an antecedent (premise, condition) and a consequence (conclusion, action). Both antecedent and conclusion are, in essence, facts. The antecedent attempts to verify if the fact is true or false, when the fact composing the antecedent is true, the conclusion is triggered. The antecedent can be composed of several facts connected through operators such as and, or, and not. Conclusions usually change or assign values to attributes of an object, call methods or trigger other rules.

Copyright 2004 R. Weber Concepts, Objects and Facts An object is a basic entity that can be instantiated. A concept tells something about the object. A concept can be represented as an abstraction of an object when several objects can be grouped under the same concept ; or a concept can be an attribute, when it tells something exclusively about this object or due to the analysis it is not worthy to represent it as an abstraction. When an object is associated to a valued attribute, it is a fact. A fact can be either true or false (Durkin, 1994). So, you can describe concepts in a computer program by communicating only via Y/N or T/F statements.

Copyright 2004 R. Weber Cases, similarity functions Forms of knowledge representation used in case-based reasoning systems A description of an experience can be used as a knowledge representation formalism A case has to describe the problem and the solution The description should be such that the engine is capable of solving similar problems given a case

Copyright 2004 R. Weber Semantic Networks used in logic-based expert systems directed graphs where nodes represent objects and arcs represent relationships between objects and attributes Quillian, 1968 used to represent static elements of a representation such as the class, the instances and its features cannot represent all magnitude of data ( meal varying from sandwich to 20 course meal)

Copyright 2004 R. Weber Neural Networks inputs and outputs are represented numerically a matrix of weights learns the input/output behavior weights in the matrix are information the learned matrix (for facts in the same category as the inputs) represents knowledge

Copyright 2004 R. Weber Expert Systems INSYS 370 Artificial Intelligence for Information Systems College of Information Science and Technology Professor Rosina Weber

Copyright 2004 R. Weber Expert Systems ES are a methodology to develop computer programs that manipulates expertise in a knowledge base to solve expert problems in specific and restricted domains.

Copyright 2004 R. Weber Expert Systems Computer systems that can perform expert tasks. (general, vague) A methodology that manipulates explicit knowledge with an inference engine to perform AI tasks.

Copyright 2004 R. Weber the concept knowledge base (e.g.,frames and methods) knowledge base (e.g.,frames and methods) expert problem inference engine (agenda) inference engine (agenda) expert solution knowledge reasoning

Copyright 2004 R. Weber The complete methodology knowledge base (e.g.,frames and methods) knowledge base (e.g.,frames and methods) explanation general knowledge user I n t e r f a c e user I n t e r f a c e expert problem expert solution inference engine (agenda) inference engine (agenda) working memory ( short-term mem/information ) working memory ( short-term mem/information ) Knowledge acquisition

Copyright 2004 R. Weber (production) rules frames (concepts, objects, facts) rules and frames methods object-oriented semantic nets logic knowledge base (e.g.,frames and methods) knowledge base (e.g.,frames and methods) How do expert systems represent knowledge and reasoning? Representation formalisms

Copyright 2004 R. Weber Inference Engines Forward chaining –Analysis, many different results Backward chaining –Limited number of possible outputs inference engine (agenda) inference engine (agenda)

Copyright 2004 R. Weber ES: types Rule-Based Expert Systems –backward-chaining or forward-chaining Frame-Based Expert Systems Hybrid Expert Systems (rules + frames) Object-Oriented Expert Systems Task performers, int. assistants, int. tutors

Copyright 2004 R. Weber ES requirements high quality: system must perform equally or better than a human expert response time should be adequate to the problem it solves reliable: not prone to crashes & errors explanation capability should be present with the purpose of justification and verification of correctness (p. 9,10 for explanation styles) flexible: supported by good maintenance methods

Copyright 2004 R. Weber Expert Systems: history began 1965 at Stanford DENDRAL: a system that uses heuristics to generate structures of data to perform chemical analysis of the Martian soil and works as well as an expert chemist; the first program recognized to have succeeded due to the knowledge it contained instead of complex search techniques;

Copyright 2004 R. Weber Necessary grounds for computer understanding Ability to represent knowledge and reason with it. Perceive equivalences and analogies between two different representations of the same entity/situation. Learning and reorganizing new knowledge. –From Peter Jackson (1998) Introduction to Expert systems. Addison-Wesley third edition. Chapter 2, page 27.

Copyright 2004 R. Weber When do we need ES? ES are indicated to solve expert problems in restricted domains without an efficient algorithmic solution Is there an alternative method? Ill-structured problems Is the domain well-bounded? How available is the source of knowledge? Is the approach to the problem heuristic?

Copyright 2004 R. Weber ES: domain areas agriculture, business, chemistry, communications, computer systems, education, electronics, engineering, environment, geology, law, manufacturing, mathematics, medicine, mining, power systems, simulation, transportation, etc.

Copyright 2004 R. Weber ES and AI tasks From: Durkin, J. (1994). Expert Systems: design and development. Prentice-Hall, Inc., New Jersey.

Copyright 2004 R. Weber + Expert Systems: development source of expertise knowledge engineer knowledge base inference procedures knowledge acquisition knowledge representation books documents humans facts KNOWLEDGE ENGINEERING

Copyright 2004 R. Weber + Knowledge engineering source of expertise knowledge engineer Knowledge based system knowledge acquisition knowledge representation books documents humans facts KNOWLEDGE ENGINEERING

Copyright 2004 R. Weber Shell KAPPA-PC Let’s see an example of a frame-based expert system Example

Copyright 2004 R. Weber advantages (i) Permanence of knowledge - Expert systems do not forget or retire or quit, but human experts may Breadth - One ES can (and should) entail knowledge learned from an unlimited number of human experts. Reproducibility - Many copies of an expert system can be made, but training new human experts is time-consuming and expensive. Timeliness - Fraud and/or errors can be prevented. Information is available sooner for decision making Entry barriers and differentiation - An ES can differentiate a product or can help create entry barriers for potential competitors

Copyright 2004 R. Weber advantages (ii) Cost savings & efficiency - can increase throughput and decrease costs, e.g., wages, minimize loan loss, reduce customer support effort Although expert systems may be expensive to build and maintain, they are inexpensive to operate Development and maintenance costs can be spread over many users The overall cost can be quite reasonable when compared to expensive and scarce human experts If there is a maze of rules (e.g. tax and auditing), then the expert system can "unravel" the maze

Copyright 2004 R. Weber advantages (iii) Documentation - An expert system can provide permanent documentation of the decision process Increased availability: the mass production of expertise Completeness - An expert system can review all the transactions, a human expert can only review a sample; an ES solution will always be complete and deterministic Consistency - With expert systems similar transactions handled in the same way. Humans are influenced by recency effects and primacy effects (early information dominates the judgment).

Copyright 2004 R. Weber advantages (iv) Reduced danger: ES can be used in any environment Reliability: ES will keep working properly regardless of of external conditions that may cause stress to humans Explanation: ES can trace back their reasoning providing justification, increasing the confidence that the correct decision was made Indirect advantage is that the development of an ES requires that knowledge and processes are verified for correctness, completeness, and consistency.

Copyright 2004 R. Weber disadvantages (i) Common sense - In addition to a great deal of technical knowledge, human experts have common sense. To program common sense in an ES, you must acquire and represent rules. Creativity - Human experts can respond creatively to unusual situations, expert systems cannot. Learning - Human experts automatically adapt to changing environments; expert systems must be explicitly updated.

Copyright 2004 R. Weber disadvantages (ii) Complexity and interrelations of rules grow exponentially as more rules are added. Sensory Experience - Human experts have available to them a wide range of sensory experience; expert systems are currently dependent on symbolic input. Degradation - Expert systems are not good at recognizing when no answer exists or when the problem is outside their area of expertise. So, ES may provide a solution that is not optimal like one that is optimal

Copyright 2004 R. Weber disadvantages (iii) High knowledge engineering requirements: In many real world domains, the amount of knowledge necessary to cover an expert problem is abundant making ES development time-consuming and complex Knowledge acquisition bottleneck Difficulty to deal with imprecision (I.e., incompleteness,, uncertainty, ignorance, ambiguity) Advantages & Disadvantages partially obtained from O’Leary, D. webpage

Copyright 2004 R. Weber Some resources slides about advantages and disadvantages are adapted from Introduction to Artificial Intelligence and Expert Systems Copyright 1993, 1994, 1995 by Carol E. Brown and Daniel E. O'Leary (available online at: m#5-AD Interrante,L.D. & Biegel,J.E.. Design of knowledge- based systems: matching representations with application requirements. Computers and Engineering, v.19, n.1-4, p.92-96, n