Unit-5 solved - airtificial intelligence and expert systems PDF

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ARTIFICIAL INTELLIGENCEUNIT- VEXPERT SYSTEMS100% Theory Answer Key YEAR/SEM: III/VI SYLLABUS: Expert systems – Architecture of expert systems, Roles of expert systems – Knowledge Acquisition – Meta knowledge, Heuristics. Typical expert systems – MYCIN, DART, XOON, Expert systems shells. **2 Marks Ex...

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ARTIFICIAL INTELLIGENCE UNIT- V EXPERT SYSTEMS 100% Theory Answer Key YEAR/SEM: III/VI SYLLABUS: Expert systems – Architecture of expert systems, Roles of expert systems – Knowledge Acquisition – Meta knowledge, Heuristics. Typical expert systems – MYCIN, DART, XOON, Expert systems shells. 2 Marks Expert Systems 1. What is meant by Expert systems? (C313.6, K)  An expert system is an interactive computer-based decision tool that uses both facts and heuristics to solve difficult decision making problems, based on knowledge acquired from an expert.  An expert system is a model and associated procedure that exhibits, within a specific domain, a degree of expertise in problem solving that is comparable to that of a human expert.  Inference engine + Knowledge = Expert system 2. Mention the components and interfaces of expert systems. (C313.7, U) Components and interfaces of expert systems are:  Knowledge base: A declarative representation of the expertise, often in IF THEN rules.  Working Storage: The data which is specific to a problem being solved.  Inference engine: The code at the core of the system which derives recommendations from the knowledge base and problem specific data in working storage.  User interface: The code that controls the dialog between the user and the system 3. Draw the components of Expert system. (C313.7, S)

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Figure: Components of Expert System Architecture of Expert Systems: 4. Draw the Expert system architecture diagram. (C313.7, S) Knowledge base

Working memory Communication module

User interface

Graphics + software

Inference engine

Figure: Architecture of Expert system  The user interacts with the system through auser interfacewhich may use menus, natural language or any other style of interaction).  Aninference engineis used to reason with both theexpert knowledge and data specific to the particular problem being solved.  Knowledge base editorhelp the expert or knowledge engineer to easily update and check the knowledge base 5. Write a short note on Expert system architecture. (C313.6, K)  An ES is specific to one problem domain. However, it is not for domain modeling but for problem solving. The expert system consists of a knowledge base, a working memory, an inference engine, system analysis, graphic and other software and user interface.  Knowledge base consists of declarative knowledge that is facts about the domain and procedural knowledge that are heuristic rules from the domain. The working memory is the active set of knowledge base.  Inference engine is the problem solving module. It also gives justification (explanation) for the advice from the ES. Communication module helps in interaction between other modules and also provides user developer interfaces. 6. Compare and contrast between expert system and software system. (C313.6, U) Expert system Software system Page 2

They use knowledge base

They use database

Lesser data is kept together

Large data is kept separately

They use reasoning mechanism and heuristics

They use algorithms

They use inference engine

They use compilers

They are developed by knowledge engineers

They are developed by software engineers

Eg: DENDRAL, MYCIN

Eg: C/C++, Java

7. What is meant by knowledge base? (C313.6, K)  A knowledge base (KB) is a technology used to store complex structured and unstructured information used by a computer system. The initial use of the term was in connection with expert systems which were the first knowledge-based systems.  It contains domain-specific and high-quality knowledge. Knowledge is required to exhibit intelligence. The success of any Expert system majorly depends upon the collection of highly accurate and precise knowledge 8. Define semantic nets. (C313.6, K) Semantic network is a knowledge representation schema that captures knowledge as a graph. The nodes denote objects or concepts, their properties and corresponding values. The arcs denote relationships between the nodes. Both nodes and arcs are generally labeled (arcs have weights). Symbols of semantic nets: Node (A concept)

A Relationship

Figure: Semantic nets diagram 9. Mention some nodes that can be represented by semantic nets. (C313.6, U) Nodes of semantic nets can represent:  Concepts  Objects  Events  Features  Time 10. What are frames? (C313.6, K) Frames are record-like structures that have slots & slot – values for an entity. Using frames, the knowledge about an object/event can be stored together in the KB as a unit. A slot in a frame,  Specify a characteristic of the entity which the frame represents  Contains information as attribute-value pairs, default values etc. An example for frame: (Tweety Page 3

(Species (value bird)) (Color (value yellow)) (Activity (value fly))) 11. Mention some major capabilities of using frames. (C313.6, U) The following are the major capabilities of frames:  Ability to clearly document information about a domain model (e.g., a plant’s machines and their associated attributes)  Related ability to constrain the allowable values that an attribute can take on  Modularity of information, permitting ease of system expansion and maintenance  Readability and consistency  Syntax for referencing domain objects in the rules  Platform for building a graphic interface with object graphics  Mechanism that allows the scope of facts considered during forward or backward chaining to be restricted  Access to a mechanism that supports the inheritance of information down a class hierarchy 12. Write down the basic elements of frames. (C313.6, K) A frame includes two basic elements: slots and facets. A slot is a set of attributes that describe the object represented by the frame. For example, in the automobile frame, there are weight and engine slots. Each slot contains one or more facets. The facets (sometimes called subslots) describe some knowledge or procedural information about the attribute in the slot. Facets can take many forms, including the following:  Values: These facets describe attributes such as blue, red, and yellow for a color slot.  Default: This facet is used if the slot is empty (i.e., without any description).  Range: Range indicates what kind of information can appear in a slot (e.g., integer numbers only, two decimal points, 0 to 100).  If added: This facet contains procedural information or attachments. It specifies an action to be taken when a value in the slot is added (or modified). Such procedural attachments are called daemons Roles of Expert Systems: 13. Write down the roles of individuals who interact with the Expert system. (C313.6, K) The roles of individuals who interact with the Expert system are as follows:  Domain expert: The individuals who currently are experts in solving the problems, here the system is intended to solve.  Knowledge engineer: The individual who encodes the expert’s knowledge in a declarative form that can be used by the expert system.  User: The individual who will be consulting with the system to get advice which would have been provided by the expert. 14. What is meant by script? Give an example. (C313.6, K) A script is a data structure used to represent a sequence of events. It has been used to Page 4

1) Interpret, understand and reason about stories, 2) Understand and reason about observed events 3) Reason about observed actions 4) Plan actions to accomplish tasks. It is compose of a Scene, Props, The Actors, Events and Acts. Example of a restaurant script: Scene: A restaurant with an entrance and tables. Actors: The diners, servers, chef and Maitred'Hotel. Props: The table setting, menu, table, chair. Acts: Entry, Seating, Ordering a meal, Serving a meal, Eating the meal, requesting the check, paying, leaving. 15. How script is used to develop structured knowledge representation? (C313.6, A) Scripts are used for interpreting stories. A script is composed of 1) A scene A restaurant with an entrance and tables. 2) Props (objects manipulated in the script) The table setting, menu, table, chair. 3) The actors (agents that can change the state of the world) The diners, servers, chef and Maitred'Hotel. 4) Events  Relationship between actor and act. 5) Acts: A set of actions by the actors  Entry, Seating, Ordering a meal, Serving a meal, Eating the meal In each scene, one or more actors perform actions. The actors act with the props. The script can be represented as a tree or network of states, driven by events. As with Frames, scripts drive interpretation by telling the system what to look for and where to look next. The script can predict events 16. Mention some characteristics of Expert Systems. (C313.6, U) Characteristics of Expert Systems:  High performance  Understandable  Reliable  Highly responsive  Domain specific  Cost effective 17. Define inference engine. (C313.6, K) An Inference Engine is a tool from artificial intelligence. The first inference engines were components of expert systems. The typical expert system consisted of a knowledge base and an inference engine. The knowledge base stored facts about the world. The inference engine applied logical rules to the knowledge base and deduced new knowledge. This process would iterate as each new fact in the knowledge base could trigger additional rules in the inference engine 18. Mention the two modes of inference engine. (C313.6, U)

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An inference engine interprets and evaluates the facts in the knowledge base in order to provide an answer. Typical tasks for expert systems involve classification, diagnosis.Inference engines work primarily in one of two modes:  Forward chaining  Backward chaining Forward chaining starts with the known facts and asserts new facts. Backward chaining starts with goals, and works backward to determine what facts must be asserted so that the goals can be achieved 19. Give an example for rule base of a simple vehicle recognizer. (C313.6, A) The following example might be the rule base of a simple vehicle recognizer. R1: If ?x has wings then ?x is a plane R2: If ?x flies then ?x is a plane R3: If ?x runs on tracks then ?x is a train-or-tram R4: If ?x is a plane ?x can take off vertically ?x has rotors then ?x is a helicopter R5: If ?x is a train-or-tram ?x stays underground then ?x is a subway car R6: If ?x is a helicopter ?x made in South Africa then ?x is a Rooivalk 20. Enumerate the rule based forward chaining algorithm. (C313.6, S) With a rule base, knowledge can be developed by either data-driven or goal-driven search. In the former, also known as forward chaining, one has a supply of facts and repeatedly applies legal moves or rules to produce new facts to get (hopefully) to the goal. Forward-Chaining: while (no new assertion made) and (unresolved) for each rule (and for each possible binding) try to support rule’s conditions from known facts if all supported then assert consequent 21. Write down the algorithm procedure for forward chaining. (C313.6, S) Forward chaining starts from the known initial state and proceeds in the forward direction to achieve the goal. The inference engine searches the knowledge base with the given information for rules whose precedence matches the given current state. The basic steps are; (i) The system is given one or more conditions

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(ii) The system searches the rules in the knowledge base for each condition. Those rules that correspond to the condition in IF part are selected. (iii) Each rule can generate new conditions from the conclusions of the invoked THEN part, which in turn are again added to the existing ones (iv) The added conditions, if any will be processed again (step ii). The session ends if there are no new conditions 22. Write down the algorithm procedure for backward chaining. (C313.6, S) The system selects a goal state and reasons in the backward direction. The initial state condition is established for the goal to be true. If the given initial state conditions matches with the established ones, then the goal is the solution. Otherwise, the system selects another goal and the process is repeated. The basic steps are: (i) Select a goal state and rules whose THEN portion has the goal state as conclusion (ii) Establish sub goals to be satisfied for the goal state to be true, from the IF portion of the selected rules. (iii) Establish initial conditions necessary to satisfy all the sub goals. (iv)Check whether the given initial state matches with the established ones. If so, then the goal is one solution. If not, select another goal state. 23. Explain the rule based backward chaining algorithm. (C313.6, S) In backward chaining, one repeatedly considers the possible final rules that produce the goal and from these creates successive sub goals. A rule might have several variables in it. The particular choice of substitution is known as the binding. Backward-Chaining: while (no untried hypothesis) and (unresolved) for each hypothesis for each rule with hypothesis as consequent try to support rule’s conditions from known facts or via recursion (trying all possible bindings) if all supported then assert consequent 24. Compare and contrast ignorance and uncertainty in expert system. (C313.6, U) Ignorance Uncertainty Ignorance is not knowing something that Uncertainty is where something is not is knowable. knowable. It is inherent in the situation. Ignorance can come from (a) the limited Uncertainty can come from (a) poor knowledge of human expert (b) inexact knowledge of human expert (b) Irrelevant data or (c) incomplete data (which forces data. a premature decision). To deal with Ignorance (a) Knowledge To deal with Uncertainty also (a) Knowledge revision (b) Default assumptions (c) revision (b) Default assumptions (c) Fuzzy Fuzzy logic logic 25. Mention some techniques that used to handle ignorance and uncertainty. (C313.6, U) Page 7

To deal with uncertainty and ignorance, we need to keep track not only of our knowledge. Here are some techniques we might use:  Knowledge revision: remember how a fact was deduced and be able to scrap it if supporting facts are overturned.  Default assumptions: to handle incomplete data  Fuzzy logic: Consider the question “Is a greyhound fast?” One might know the exact speed of a greyhound, but does that constitute fast? So we have a fuzzy fact: we get a “likelihood” of being true (a value between 0 and 1). In this system, AND becomes min, and OR becomes max. Fuzzy logic has had considerable success in systems for control and signal processing 26. Define the term abduction. (C313.6, K) The term abduction was coined by Peirce (1839-1914) to differentiate this type of reasoning from deduction, which involves determining what logically follows from a set of axioms, and induction, which involves inferring general relationships from examples. Abduction is a form of reasoning where assumptions are made to explain observations. p → q, q proves p Abduction is related to the analysis of backward chaining and implication. Abduction is a mathematically justifiable, practical, and reasonable way to generate hypotheses. Abduction is another name for a fallacious argument. It is not guaranteed to work. 27. A ready-made ES is selling for $5,000. Developing one will cost you $50,000. A ready-made suit will cost you $100, and a tailored one will cost you $500. Develop an analogy between the two situations and describe the markets for the ready-made and customized products. (C313.6, A)  A tailored suit will fit better. On the other hand, it will be costlier, and it will take longer to make. People who buy the ready-made suit are those who need a suit quickly, or those for whom the perfect fit is not so important (or those that cannot afford expensive suits). Ready-made expert systems can be put into use very quickly. However, there is not going to be a perfect (or sometimes even a good) fit between the system and the problem.  Thus, the advice generated may be of a little value to the user. However, there are many situations for which "quick and dirty" advice is sufficient, and this is where ready-made ES will flourish. Furthermore, like humans who cannot afford tailored suits and, therefore, are forced to buy ready-made ones, organizations, too, (e.g., in developing countries, small businesses) may be better off to use some advice than not use expertise at all.  These systems will start advising people on personal matters and then move to organizational advice of a general nature (such as taxes, regulations, and accounting). 28. Given the current status of the Web, Explain how it is changing the availability of ES and how it is being used to embed expertise in other systems. (C313.6, A) The Web is allowing us to deploy ES directly on it, to provide expertise to users by running directly on the Web. For example, Exsys WREN is a Web Runtime Engine for Exsys (try it out). Help desk software, essentially ES, are running on the Web pages of many technical support groups. Ginesys Corp. has such software as a part of its K-Vision ES shell. Furthermore, anytime expertise is required for customer support (e.g., in advertising to help a customer choose a product to order or use), it can be provided over the Web by an ES. 29. Write down the steps to build an Expert system. (C313.6, K) Page 8

The following steps to build an Expert system are; 1. Problem selection & Prototype construction: One must decide and narrow the problem, and assess the suitability of an expert system. Then one chooses knowledge representation schemes and the inference mechanism(s). After that one can implement the prototype, and obtain feedback from experts. 2. Formalization & Implementation: One draws up a detailed plan. At the same time, one revises the prototype and revisits the inference and representation decisions. After which one can construct the core knowledge base. 3. Evaluation & Evolution: Perhaps a modified Turing test for evaluation: see if the system can pass itself off as an expert in the domain 30. Mention the five stages of Expert system development. (C313.6, U) Hayes – Roth and Lenat (1983) has recommended five stages in the development of ES (i) Identification – determining characteristics of the problem (ii) Conceptualization – finding concepts to represent the knowledge (iii) Formalization – designing structures to organize knowledge (iv) Implementation – formulating rules embodying the knowledge (v) Testing – validating the rules A good coordination between the knowledge engineer and the expert is necessary. 31. How does Expert system differ from conventional programs? (C313.6, S) Expert systems differ from the conventional computer programs in the following aspects: (i) Expert systems are knowledge intensive programs (ii) Expert systems are highly interactive (iii) Expert systems mimic human experts in decision making and reasoning process (iv) Expert systems divide expert knowledge into number of separate rules (v) Expert systems are user friendly and intelligent 32. Write a short note on Expert system tools. (C313.6, U)  Language: A translator of commands written in a specific syntax. An expert system language will also provide an inference engine to execute the statement of the language. (Eg. LISP is not a language but PROLOG is a Language)  Shells: A special purpose tool designed for certain types of applications in which the user must only supply the knowledge base. (Eg. EMYCIN)  Tools: A language + utility programs to facilitate the development debugging, and delivery of application programs 33. What do you mean by inductive learning? (C313.6, K) (Nov/Dec 2014) Inductive learning is the process of acquiring generalized knowledge from examples or instances of some class. This form of learning is accomplished through inductive inference, the process of learning from a part to a whole, from particular instances to generalizations or from the individual to the universal. It is a powerful form of learning which we humans do almost effortlessly. Even though it is not a valid form of inference, it appears to work well much of the time 34. List out some real time example...