Software and Knowledge Engineering Lecturer: Deirdre Lawless DT228/3 Software and Knowledge Engineering Lecturer: Deirdre Lawless

Data, Information, Knowledge, Wisdom is raw. simply exists and has no significance beyond its existence (in and of itself). Information data that has been given meaning by way of relational connection. "meaning" can be useful, but does not have to be. Knowledge the appropriate collection of information, such that it's intent is to be useful. Understanding... cognitive and analytical. It is the process by which you can take knowledge and synthesize new knowledge from the previously held knowledge. The difference between understanding and knowledge is the difference between "learning" and "memorizing". People who have understanding can undertake useful actions Wisdom... an extrapolative and non-deterministic, non-probabilistic process. It calls upon all the previous levels of consciousness, and specifically upon special types of human programming (moral, ethical codes, etc.). For example, elementary school children memorize, or amass knowledge of, the "times table". They can tell you that "2 x 2 = 4" because they have amassed that knowledge (it being included in the times table). But when asked what is "1267 x 300", they can not respond correctly because that entry is not in their times table. To correctly answer such a question requires a true cognitive and analytical ability that is only encompassed in the next level... understanding. Wisdom is therefore, the process by which we also discern, or judge, between right and wrong, good and bad. I personally believe that computers do not have, and will never have the ability to posses wisdom. Wisdom is a uniquely human state, or as I see it, wisdom requires one to have a soul, for it resides as much in the heart as in the mind. And a soul is something machines will never possess (or perhaps I should reword that to say, a soul is something that, in general, will never possess a machine). because they can synthesize new knowledge, or in some cases, at least new information, from what is previously known (and understood). That is, understanding can build upon currently held information, knowledge and understanding itself. In computer parlance, AI systems possess understanding in the sense that they are able to synthesize new knowledge from previously stored information and knowledge. It beckons to give us understanding about which there has previously been no understanding, and in doing so, goes far beyond understanding itself. It is the essence of philosophical probing. it asks questions to which there is no (easily-achievable) answer, and in some cases, to which there can be no humanly-known answer period

Data, Information, Knowledge, Wisdom Examples Data represents a fact or statement of event without relation to other things. Ex: It is raining. Information embodies the understanding of a relationship of some sort, possibly cause and effect. Ex: The temperature dropped 15 degrees and then it started raining. Knowledge represents a pattern that connects and generally provides a high level of predictability as to what is described or what will happen next. Ex: If the humidity is very high and the temperature drops substantially the atmospheres is often unlikely to be able to hold the moisture so it rains. Wisdom embodies more of an understanding of fundamental principles embodied within the knowledge that are essentially the basis for the knowledge being what it is. Wisdom is essentially systemic. Ex: It rains because it rains. And this encompasses an understanding of all the interactions that happen between raining, evaporation, air currents, temperature gradients, changes, and raining.

Transition

Knowledge Abugt dbesbt regtc uatn s uitrzt. ubtxte pstye ysote anet sser extess ibxtedstes bet3 ibtes otesb tapbesct ehracts Does this mean anything to you ?

Knowledge I have a box. The box is 3' wide, 3' deep, and 6' high. The box is very heavy. The box has a door on the front of it. When I open the box it has food in it. It is colder inside the box than it is outside. You usually find the box in the kitchen. There is a smaller compartment inside the box with ice in it. When you open the door the light comes on. When you move this box you usually find lots of dirt underneath it. Junk has a real habit of collecting on top of this box. What is it? At some point in the sequence you connected with the pattern and understood it was a description of a refrigerator. From that point on each statement only added confirmation to your understanding.

What is Knowledge Management ? An approach based on the central role of knowledge in organisations Objective to manage and support knowledge work and to maximise the added value of knowledge for the organisation Aims: identifying and analysing knowledge and knowledge work developing procedures and systems for generating, storing, distributing and using knowledge in the organisation.

What is Knowledge Management About? improving the ability to acquire knowledge, improving the quality of knowledge, and using knowledge to its greatest advantage

Objective of KM To create added value for the organisation at three distinct levels: Improvement of existing business processes what can we do better Development of new products and services what can we do more Improving the strategic position, aimed at: Developing unique knowledge Applying knowledge to innovative products and services Strengthening the competitive position Safeguarding the organisation’s continuity Improving flexibility Creating an attractive work environment Making the organisation independent of the individual employee’s knowledge

How can computers help? Share knowledge Discover Knowledge Assist people About both people and technology Knowledge not just stored in a knowledge base but constructed through co-operation with a person using that knowledge base

Knowledge Engineering KE is an engineering discipline that involves integrating knowledge into computer systems in order to solve complex problems normally requiring a high level of human expertise. At present, it refers to the building, maintaining and development of knowledge-based systems

Knowledge Engineering Or it refers to transferring human knowledge into some form of knowledge based system (KBS) Five steps Acquisition Obtaining knowledge from various sources human experts, documents, existing computer systems etc Validation Check knowledge acquired using test cases Representation Producing a map of knowledge and encoding into some sort of knowledge base Inferencing Forming links in the knowledge so that a KBS can make a decision or provide advice Explanation and justification Allow a KBS to show how it reached a conclusion

Knowledge Engineer Person who translates knowledge relating to an area of expertise into the knowledge base which supports a KBS

Types of Knowledge Procedural Conceptual How to E.g. I Know How To Drive A Car Processes, Tasks, Activities And conditions under which tasks are performed And sequence of tasks Conceptual I know that … About ways in which things (concepts) are related to each other and their properties

Types of Knowledge Explicit Tacit Knowledge at the forefront of a person’s brain Thought about in a deliberate, conscious way Concerned with basic tasks, basic relationships between concepts, basic properties of concepts Not difficult to explain Tacit Deep, embedded knowledge At the back of a person’s brain Built from experience rather than being taught Gain when practice Leads to activities which seem to require no conscious thought at all

Types of Knowledge How to interview an expert How to boil an egg Basic, Explicit Knowledge Deep, Tacit Knowledge Conceptual Knowledge Procedural Knowledge How to boil an egg E=mc2 How to interview an expert The properties of knowledge The position of keys on a keyboard How to tie a shoelace How to Boil An Egg Simple task easily explained How to tie a shoelace Requires demonstration with commentary E=mc2 Simply relates concepts The position of keys on a keyboard Most people know this sub-conciously but few conciously Taken from Knowledge Acquisition in Practice A Step By Step Guide, Millton, Springer-Verlag

Eliciting Knowledge Most knowledge is in the heads of experts Experts have vast amounts of knowledge Experts have a lot of tacit knowledge They don't know all that they know and use Tacit knowledge is hard (impossible) to describe Experts are very busy and valuable people Each expert doesn't know everything 17

Knowledge Acquisition/Knowledge Engineering Knowledge Representation is about representing some knowledge First need to determine what that knowledge is the process of Knowledge Acquisition and Elicitation non-trivial process The information is often locked away in the heads of domain experts The experts themselves may not be aware of the implicit conceptual models that they use Have to draw out and make explicit all the known knowns, unknown knowns, etc…. 18

Knowledge Acquisition Capturing knowledge about a subject domain From experts And other sources Using this to create a store of knowledge Usable by many different applications, users and benefits Does not have to be a database Can be a knowledge web, ontology, knowledge document etc

Difficulties of knowledge acquisition Experts find it difficult to Express their knowledge in a manner fully comprehensible to the knowledge engineer Know exactly what the engineer wants Give the right level of detail Present ideas in a clear and logical order Explain all the jargon and terminology of the subject domain Recall everything relevant to the project Avoid drifting into talking about irrelevant things

Difficulties of knowledge acquisition Engineers find it difficult to Understand everything the expert says Note down everything the expert says Keep the expert talking about relevant issues Maintain high level of concentration needed Check they have fully understood what has been said

Difficulties of Knowledge Acquisition Arise due to human cognition and communication Humans are good at communication and performing complex activities Not good at communicating complex activities to those not from the same subject areas

Knowledge Acquisition Bottleneck Nothing happens until knowledge is acquired Sources of knowledge are unreliable Domain experts provide incomplete, even incorrect knowledge Domain experts may not be able to articulate their knowledge Knowledge bases are hard to build Computational knowledge representations are complex Techniques Limited range Ignorance Experts poor appreciation of different types ignorance Expertise need to organise knowledge into higher level units 23

What is a Knowledge Based System ? Use knowledge to solve problems Exercise knowledge to solve problems Knowledge used is that possessed by people knowledgeable in the domain Cause-and-effect Heuristics Etc Definition: A computerised system that uses domain knowledge to arrive at a solution to a problem within that domain. The solution is essentially the same as one concluded by a person knowledgeable about the domain when confronted with the same problem. 24

What is a Knowledge Based System ? Computer system that is programmed to imitate or assist with human problem-solving By means of artificial intelligence And reference to a database containing human knowledge on a particular subject. Core components are the knowledge base and the inference mechanisms. Typical Architecture a knowledge base (where the knowledge is stored) Data plus more, an inferencing engine or reasoning engine, a working memory where the initial data and intermediate results are stored 25

Knowledge based systems Use highly specific domain knowledge Heuristic nature of knowledge rather than algorithmic Human ability Separation of knowledge from how it is used Knowledge of how to infer something 26

Knowledge Based Systems Development Team Expert System Development Team Project Manager Domain Expert Knowledge Engineer Programmer Expert System Knowledge is provided by a human expert and we want to put that into a computer Need the computer to act as an intelligent assistant in some specific domain of expertise or to solve a problem that would otherwise have to be solved by an expert Most popular are rule-based End-User

Intelligence ? the ability to comprehend; to understand and profit from experience Intelligence is a general mental capability that involves the ability to reason, plan, solve problems, think abstractly, comprehend ideas and language, and learn. In psychology, the study of intelligence is related to the study of personality but is not the same as creativity, personality, character, or wisdom. 29

Intelligent System ? 30

Artificial Intelligence Definition ? Science that provides computers with the ability to represent and manipulate symbols so that they can be used to solve problems not easily solved through algorithmic methods Most methods founded on realization that intelligence is tightly coupled with knowledge Knowledge is associated with symbols that are manipulated Human intelligence ? Definition ? Human intelligence ? – innate ability to learn and manipulate knowledge in order to communicate or solve a problem 31

What is Artificial Intelligence ? Agreement that it is concerned with two things Studying human thought processes Representing these processes via machines Computers Robots Artificial Intelligence is behaviour by a machine which if performed by a human would be considered intelligent “Artificial Intelligence is the study of how to make computers do things at which, at the moment, people are better” (Rich & Knight 1991) So what is the main thing you take out of these definitions ? How do you get machines to behave intelligently if you don’t understand intelligence ? What it intelligence ? Also that the range of applications will expand over time – at the moment people are better 32

Typical problems addressed by KBS Type of problem – influences out choice of the tool for building an intelligent system Something to detect faults in an electrical circuit and guide user through diagnosis Domain knowledge can often be represented as production rules and this a rule-based expert system could be the right candidate for solution Choice of tool will also depend on the form and content of the solution Systems build for diagnosis often require an explanation facility to enable them to justify their solutions This is an essential component of an expert system but not of a neural network Neural nets would be a good choice for classification and clustering problems where the result is often more important than understanding the reasoning process Next step is to identify the participants Knowledge engineer, domain expert Then specify the objectives – gain competitive edge, improve decision making, reduce labour costs 33

Knowledge Representation Programming language is a means of representing knowledge Procedural knowledge “how to” Knowledge about how to perform some task Declarative knowledge “what is “ Procedural knowledge doesn’t have to be encoded as cryptically as it is in a programming language Can represent it as a set of rules 34

Rule-based reasoning One can often represent the expertise that someone uses to do an expert task as rules. A rule means a structure which has an if component and a then component.

Other Examples of Rules if - the leaves are dry, brittle and discoloured then - the plant has been attacked by red spider mite if - the customer closes the account then - delete the customer from the database

Rules The statement, or set of statements, after the word if represents some pattern which you may observe. The statement, or set of statements, after the word then represents some conclusion that you can draw, or some action that you should take. IF some condition(s) exists THEN perform some action(s) IF-THEN Test-Action Production rules or just rules Set of such rules = production system Test-action if left side holds true produce the right side 37

Rule-Based Systems A rule-based system, therefore identifies a pattern and draws conclusions about what it means OR identifies a pattern and advises what should be done about it OR identifies a pattern and takes appropriate action.

Rule-based system model Long Term Memory Production rule Short Term Memory Fact Interpreter (Inference engine) Conclusion Production System Model Based on idea that humans solve problems by applying their knowledge(expressed as rules) to a given problem represented by problem-specific information. These facts exist in short term memory If you put enough information in rule base that it can perform interesting, complex task at same performance level as a human have a rule-based expert system

Knowledge Representation Rules represent Relations Recommendations Directives Strategies Rules can represent relations, recommendations, directives, strategies and heuristics Relation: IF fuel tank is empty then car is dead Recommendation: If the season is autumn And the sky is cloudy And the forecast is drizzle Then the adivce is take an umberella Directive If the car is dead And the fuel tank is empty Then the action is refuel the car Strategy Then the action is check the fuel tank step 1 is complete If step1 is complete Then check the battery step 2 is complete Heuristic (rule of thumb) A heuristic is a short cut decision strategy that leads to a reasonable decision in most cases but may lead to an incredibly bad one If the spill is liquid And the spill ph is < 0 And the spill smell is vinegar Then the spill material is acetic acid 40

Knowledge Representation…Relations IF fuel tank is empty then car is dead.

Recommendation If the season is autumn And the sky is cloudy And the forecast is drizzle Then the adivce is take an umberella

Directive If the car is dead And the fuel tank is empty Then the action is refuel the car

Strategy If the car is dead Then the action is check the fuel tank step 1 is complete If step1 is complete And the fuel tank is empty Then check the battery step 2 is complete

Class Exercise : Rule-Based System for Tic-Tac-Toe What rules do we need ? Rules may have tests that are satisfied at the same time – need some mechanism for selecting right rule 45