Biyani's Think Tank Concept based notes
AI MSc-IT
Ms Rashmi Deptt. of IT Biyani Girls College, Jaipur
2 Published by :
Think Tanks Biyani Group of Colleges
Concept & Copyright :
Biyani Shikshan Samiti Sector-3, Vidhyadhar Nagar, Jaipur-302 023 (Rajasthan) Ph : 0141-2338371, 2338591-95 Fax : 0141-2338007 E-mail :
[email protected] Website :www.gurukpo.com; www.biyanicolleges.org
Edition : 2012
While every effort is taken to avoid errors or omissions in this Publication, any mistake or omission that may have crept in is not intentional. It may be taken note of that neither the publisher nor the author will be responsible for any damage or loss of any kind arising to anyone in any manner on account of such errors and omissions.
Leaser Type Setted by : Biyani College Printing Department
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Preface
I
am glad to present this book, especially designed to serve the needs of the
students. The book has been written keeping in mind the general weakness in understanding the fundamental concepts of the topics. The book is self-explanatory and adopts the “Teach Yourself” style. It is based on question-answer pattern. The language of book is quite easy and understandable based on scientific approach. Any further improvement in the contents of the book by making corrections, omission and inclusion is keen to be achieved based on suggestions from the readers for which the author shall be obliged. I acknowledge special thanks to Mr. Rajeev Biyani, Chairman & Dr. Sanjay Biyani, Director (Acad.) Biyani Group of Colleges, who are the backbones and main concept provider and also have been constant source of motivation throughout this endeavour. They played an active role in coordinating the various stages of this endeavour and spearheaded the publishing work. I look forward to receiving valuable suggestions from professors of various educational institutions, other faculty members and students for improvement of the quality of the book. The reader may feel free to send in their comments and suggestions to the under mentioned address. Author
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Artificial Intelligence and Expert System Prerequisite: System Software, Operating System, Data and File Structure. Introduction of Artificial Intellignce: Simulation of so called intelligent behavior, in different areas. Problem solving: Games, natural language, question answering, visual perception, learning, Aim-oriented (heuristic) algorithm versus solution guaranteed algorithms. Understanding Natural Languages: Parsing techniques. Context free and transformational grammars, transition nets, augmented transition nets, Fillmore's grammars. Shank's conceptual dependency, grammar-free analyzers, sentence generation, translation. Knowledge Representation: First-Order predicate calculus Horn's clauses, The Language PROLOG, semantic nets, Partitioned rules, knowledge base, the inference system, forward and backward deduction. Expert Systems: Existing system (DENDRAL MYCIN): Domain exploration, metaKnowledge, expertise transfer, self-explanining systems machine perception, line finding, interpretation semantics and models, object identification, speech recognition. Books
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Unit1
Introduction Q1. Ans
What is AI? “Artificial intelligence is the study of how to make computers do things which, at the moment, people can do better.” Artificial Intelligence is the study of human intelligence such that it can be replicated artificially.
Q.2 . What are the different applications of AI? Ans a) Game playing b) Speech recognition c) Understanding natural language d) Computer vision e) Expert system f) Fuzzy logic system Q.3. Ans
What are the different problems involved in AI? a) b) c) d) e) f) g) h)
Deduction, Reasoning, Problem solving Knowledge representation Planning Learning Natural Language Processing Motion and manipulation PerceptionCreativity
Q.4. Explain Depth-First Search Ans 1. Set L to be a list of the initial nodes in the problem.
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2. If L is empty, fail otherwise pick the first node n from L 3. If n is a goal state, quit and return path from initial node. 4. Otherwise remove n from L and add to the front of L all of n's children. Label each child with its path from initial node. Return to 2. Q.5. Ans
Explain Breadth-First Search . 1. Set L to be a list of the initial nodes in the problem. 2. If L is empty, fail otherwise pick the first node n from L 3. If n is a goal state, quit and return path from initial node. 4. Otherwise remove n from L and add to the end of L all of n's children. Label each child with its path from initial node. Return to 2.
Q6 . Ans
What is meant by heuristic search technique?What are different heuristic search techniques. It is a search technique that relies on the estimate provided by the heuristic function. Heuristic search techniques make use of domain specific information 1. Generate and Test Search :In the Generate and Test Search A. Generate a possible solution with generating a path from Start state B. Then test to see if this is actually a solution by comparing the chosen point or the endpoint of the chosen path to the set of acceptable goal states. C. If the solution has found then “quit”. Otherwise, return to step “A” The Generate and Test algorithm is a depth-first-serach procedure since complete solution must be generate before they can be tested. It also operate by generating solution randomly, but there is no guarantee that a solution will ever be found 2. Hill Climbing Search :-
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Hill Climbing is an optimization technique which improve the solution by considering a neighboring configuration. It is an iterative algorithm that starts with an arbitrary solution to a problem, then attempts to find a better solution by incrementally changing a single element of the solution. If the change produces a better solution, an incremental change is made to the new solution, repeating until no further improvements can be found. A. Evaluate the initial state – If it also goal state, then return it and quit. Otherwise, continue with the initial state as the current state. B. Loop until a solution is found or until there are no new operators left to be applied in the current state: 1. Select an operator that has not yet been applied to the current state and applied in the current state; 2. Evaluate the new state. (i)
if it is a goal state, then return it and quit.
(ii)
If it is not a goal state but it is better than the current state, then make it current state.
(iii)
If it is not better than the current state, then continue in the loop.
This technique is slightly differ from generate-and-test technique in which feedback from the test procedure is used to decide which direction to move in the search space. The Key difference between this algorithm and the one we gave for generate-and-test is the use of an evaluation function as a way to inject taskspecific knowledge into the current process. For Example - : If you want to climb on hill on a foggy day, how you will climb – you will search to pick a stone which is above you height at your upward side(picking of stone is right, left, up are solutions and examining is testing , getting the right choice which takes you upward is better current state) 3. Best First Search / Greedy Best-First Search :-
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As we already know that heuristics always has information about the problem and its parameter. In the Best-First Search it follow a single path at a time but it has capability to switch path whenever some competing path looks more promising than the current one does. The Best-First-Search is an instance of the general Tree-Search or Graph-Search algorithm in which a Node is selected for expansion based on an evaluation function f(n). The evaluation function measures distance to the Goal and the Node with the lowest evaluation is selected for expansion first. The Key component in Best-First algorithm is a heuristic function h(n) , which is the estimated cost of his cheapest path from n to a goal node. The heuristic function h(n) are the most common form in which addition knowledge of the problem is imparted to the search algorithm At the each step of the Best-First Search process, we select the most promising of the nodes we have generated so far. This is done by applying an appropriate heuristic function to each of them. We then expand the chosen node by using the rules to generate its successor. If one of them is a solution, we can quit. If not, all those new nodes are added to the set of new nodes generated so far. Again the most promising node is selected and the process continue, if the solution not found, then that branch will start to look less promising then one of the top-level branches that has been ignored. At this point, the now more promising, previously ignored branch will be explored. But the old branch not forgotten. To implement it we will need to use two list of nodes OPEN – nodes that have been generated and have had the heuristic function applied to them but which have yet to be examined. OPEN is actually a priority queue in which the elements with the highest priority are those with the most promising value of the heuristic function. CLOSE – nodes that have already been examined. We need to keep these nodes in memory to check whenever a node is generated it already examined or not.
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OPEN = initial state while OPEN != null do 1. Pick the best node on open. 2. Create open's successors 3. For each successor do: a. If it has not been generated before: evaluate it, add it to OPEN, and record its parent b. Otherwise: change the parent if this new path is better than previous one. done
Let’s consider the following example to find the path from Start to Goal in the graph below
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>>> First add the Start node to the fringe
>>> Visit the Start Node and add its neighbors to the fringe
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>>> Visit the Node “A” and add its neighbors to the fringe
Now there is a choice on which node to visit next. Because we are using greedy best-first search, the node with the lowest heuristic is used. If this solution was
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implemented, a priority queue would be used for the fringe, so it would always return the node with the lowest heuristic. Since node D has the lowest heuristic value, we visit at that node and add its neighbors to the fringe.
Now, since node E has the lowest heuristic in the fringe, it is visited at and its neighbors are added to the fringe.
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Finally, since the Goal is in the priority queue with a heuristic of 0, it is visited and a path to the goal is found. The path found from Start to Goal is: Start -> A -> D -> E -> Goal. In this case, it was the optimal path, but only because the heuristic values were fairly accurate.
Q7. Ans
Explain Best First Search . The best first search allows us to switch between paths thus gaining the benefit of both approaches. At each step the most promising node is chosen. If one of the nodes chosen generates nodes that are less promising it is possible to choose another at the same level and in effect the search changes from depth to breadth. If on analysis these are no better then this previously unexpanded node and branch is not forgotten and the search method reverts to the descendants of the first choice and proceeds, backtracking as it were.
Best First Search Algorithm: 1. Start with OPEN holding the initial state 2. Pick the best node on OPEN
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3. Generate its successors 4. For each successor Do o If it has not been generated before evaluate it add it to OPEN and record its parent o If it has been generated before change the parent if this new path is better and in that case update the cost of getting to any successor nodes 5. If a goal is found or no more nodes left in OPEN, quit, else return to 2.
Q.8. What are the achievements of AI? Ans. The achievements of AI are as follows: 1. Deep thought is an international grand master chess player. 2. Sphinx can recognize continuous speech without training for each speaker. It operates in nearreal time using a vocabulary of 1000 words and has 94% word accuracy. 3. Navlab is a truck that can drive along a road at 55 KMPH in normal traffic. 4. Carlton and United Breweries use an AI planning system to plan production of their beer. 5. Robots are used regularly in manufacturing. 6. Natural language interface to databases can be obtained on a PC. 7. Machine Learning methods have been used to build expert systems. 8. Expert systems are used regularly in finance, medicine, manufacturing, and agriculture Q9 .
Comment on “Best-First is a combination of depth first and breadth first searches. Ans. Depth first is good because a solution can be found without computing all nods and breadth firstis good because it does not get trapped in dead ends. The best first search allows us to switchbetween paths thus gaining the benefit of both approaches. At each step the most promising node ischosen. If one of the nodes chosen generates nodes that are less promising it is possible to chooseanother at the same level and in effect the search changes from depth to breadth. If on analysis theseare no better than this previously unexpanded node and branch is not forgotten and the searchmethod reverts to the descendants of the first choice and proceeds, backtracking as it were.
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Q.10 . Write goals of A.I? Ans Traditionally there are four possible Goal of A.I. and have been followed and the approaches are 1. Cognitive Science Approach :- Cognitive means Process of Understanding and In this approach we aspect that system should think like a human beings. It just not focus on behavior and I/O, but to produce a sequence of steps of the reasoning process, similar to the steps followed by a human in solving the same task. 2. Low of thought Approach :- It focus on logical thought rather than emotions to make decisions. It inference mechanisms that are probably correct and guarantee an optimal solution. As it tells about the system of logical rules and procedure for final decision. 3. Turning Test Approach :- The art of creating machines that perform tesk/function utilizing the same intelligence when they perform by the people hence it is the study of, how to make computer to things in the same manner as human beings do better. It focus on action not on intelligent behavior. It’s don’t bother on how to get result be focus on result should be similar to what human result are. 4. Rational agent Approach :- An Agent is one who act upon and a rational agent is one who that act so as to achieve best outcome or if there is uncertainty in the result then achieve at least best expected outcome. Moreover it concern with that system should at least produce sufficient outcome if could not produce optimum output in all case. Q.11. Explain problem solving. Ans Problem Solving:- Problem solving is the process of generating solution from observed or given data (Inputs). It is however not always possible to use direct methods. Instead, problem solving often use indirect or model based methods. In the A.I. most of real word problems can be solved by searching for a solution. To build a system to solve a particular problem, we need to Define the problem precisely – find input situation as well as final situation for acceptable solution to the problem. Analyze the problem – find few important feature that may have impact on the appropriateness of various possible technique for solving the problem. Isolate and represent task knowledge necessary to solve the problem.
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Choose the best problem solving technique(s) and apply to the particular problem Q12. What is Natural Language Processing? Ans Natural Language Processing (NLP) is an area of research and application that explores how computers can be used to understand and manipulate natural language text or speech to do useful things. NLP researchers aim to gather knowledge on how human beings understand and use language so that appropriate tools and techniques can be developed to make computer systems understand and manipulate natural languages to perform the desired tasks. The foundations of NLP lie in a number of disciplines, viz. computer and information sciences, linguistics, mathematics, electrical and electronic engineering, artificial intelligence and robotics, psychology, etc. Applications of NLP include a number of fields of studies, such as machine translation, natural language text processing and summarization, user interfaces, multilingual and cross language information retrieval (CLIR), speech recognition, artificial intelligence and expert systems, and so on. Q13. What is the Turing test? Ans Alan Turing's 1950 article Computing Machinery and Intelligence discussed conditions for considering a machine to be intelligent. He argued that if the machine could successfully pretend to be human to a knowledgeable observer then you certainly should consider it intelligent. This test would satisfy most people but not all philosophers. The observer could interact with the machine and a human by teletype (to avoid requiring that the machine imitate the appearance or voice of the person), and the human would try to persuade the observer that it was human and the machine would try to fool the observer. The Turing test is a one-sided test. A machine that passes the test should certainly be considered intelligent, but a machine could still be considered intelligent without knowing enough about humans to imitate a human. Q.14. Isn't AI about simulating human intelligence? Ans Sometimes but not always or even usually. On the one hand, we can learn something about how to make machines solve problems by observing other people or just by observing our own methods. On the other hand, most work in AI involves studying the problems the world presents to intelligence rather than
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studying people or animals. AI researchers are free to use methods that are not observed in people or that involve much more computing than people can do. Q.15. Are computers the right kind of machine to be made intelligent? Ans Computers can be programmed to simulate any kind of machine. Many researchers invented non-computer machines, hoping that they would be intelligent in different ways than the computer programs could be. However, they usually simulate their invented machines on a computer and come to doubt that the new machine is worth building. Because many billions of dollars that have been spent in making computers faster and faster, another kind of machine would have to be very fast to perform better than a program on a computer simulating the machine. Q.16. Explain term simulation? Ans Simulation A simulation is a system that is constructed to work, in some ways, analogously to another system of interest. The constructed system is usually made simpler than the original system so that only the aspects of interest are mirrored. Simulations are commonly used to learn more about the behavior of the original system, when the original system is not available for manipulation. It may not be available because of cost or safety reasons, or it may not be built yet and the purpose of learning about it is to design it better. If the purpose of learning is to train novices, then cost, safety, or convenience are likely to be the reasons to work on a simulated system. The simulation may be a computer simulation (perhaps a realistic one of a nuclear power station's control room, or a mathematical one such as a spreadsheet for "what-if" analysis of a company's business); or it may be a small-scale physical model (such as a small-scale bridge, or a pilot chemical plant). Q17. Explain Cognitive Science. Ans Artificial intelligence can be defined as the mimicking of human thought to perform useful tasks, such as solving complex problems. This creation of new paradigms, algorithms, and techniques requires continued involvement in the human mind, the inspiration of AI. To that end, AI software designers team with
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cognitive psychologists and use cognitive science concepts, especially in knowledge elicitation and system design
Multiple Choice Questions 1.
Weak A.I. is the a) Study of mental fecilities through the use of mental methods implemented on a computer b) Set of computer program c) All of the above d) None of the above Ans: a
2.
Cognitive science is a a) Combination of AI and Phychology b) Combination of AI and medicine c) Combination of AI and Sociology d) None of the above Ans: a
3.
A.I can be defined as a) A branch of computer science concerned with creating computer systems exhibiting intelligence b) A branch of computer science dealing with graph theory c) A branch of computer science used for creating a databases d) None of the above Ans :a
4.
What is the term used for describing the judgmental or commonsense part of problem solving? a) Heuristic b) Critical c) Value based d) None of the above Ans: a
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5.
Which kind of planning consists of successive representations of different levels of a plan? a) Hierachial planning b) Non-hierachial planning c) All of the above and d) None of the above Ans: a
6.
What was originally called the "imitation game" by its creator? a) LISP b) Turning test c) Cybernetics d) None of the above Ans: b
7.
If a robot can alter its own trajectory in response to external conditions it is considered to be a) Intelligient b) Mobile c) Open loop d) None Ans: a
8.
An A.I technique that allows computers to understand association and relationship between objects and events called a) Heuristic processing b) Cogiitive science c) Pattern matching d) none Ans: c
9.
The field that investigate the machanics of human intelligence is a) Coginitive science b) Sociology c) Psychology d) Sociology e) None Ans: a
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10.
Natural language processing is divided in to subfields of a) Symbolic and numeric b) Time and motion c) Understanding and generation d) None Ans:c
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Father of A.I a) Alan Turning b) Fisher Ada c) Allen Newell d) none Ans:a
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Area of A.I. that investigate methods of facilitating communication between people and computers is a) Natural language processing b) Symbolic processing c) Robotics d) none Ans: a
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Which approach to speech regonition avoids the problem caused by the differences in the way words words are prounced accordibng to context a) isolated word recognition b) continous speech regonition c) speaker-dependent d) none Ans:a
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Field of A.I. that covers finger print identification , handwriting recognition, wheather forecasting is a) pattern recognition b) image processing c) fuzzy logic d) parsing program Ans:a
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15.
16.
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Which is true regarding BFS? (a) BFS will get trapped exploring a single path (b) The entire tree so far been generated must be stored in BFS (c) BFS is not guaranteed to find a solution, if exists (d) BFS is nothing but Binary First Search (e) BFS is one type of sorting. Ans : (b) What is a heuristic function? (a) A function to solve mathematical problems (b) A function which takes parameters of type string and returns an integer value (c) A function whose return type is nothing (d) A function which returns an object (e) A function that maps from problem state descriptions to measures of desirability. Ans : (e) The traveling salesman problem involves n cities with paths connecting the cities. The time taken for traversing through all the cities, without knowing in advance the length of a minimum tour, is (a) O(n) (b) O(n2) (c) O(n!) (d) O(n/2) (e) O(2n). Ans : (c)
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Unit 2
Knowledge Representation Q.1. Ans
What is Knowledge?Explain natural level processing. Knowledge is a general term. Knowledge is a progression that starts with data which is of limited utility. knowledge--1. In artificial intelligence, symbolic information used by a domain expert to solve problems. 2. Facts and relationships used to solve problems. Natural Language Processing English is an example of a natural language, a computer language. For a computer to process a natural language, it would have to mimic what a human does. That is, the computer would have to recognize the sequence of words spoken by a person or another computer, understand the syntax or grammar of the words (i.e., do a syntactical analysis), and then extract the meaning of the words. A limited amount of meaning can be derived from a sequence of words taken out of context (i.e., by semantic analysis); but much more of the meaning depends on the context in which the words are spoken (e.g., who spoke them, under what circumstances, with what tone, and what else was said, particularly before the words), which would require a pragmatic analysis to extract. To date, natural language processing is poorly developed and computers are not yet able to even approach the ability of humans to extract meaning from natural languages; yet there are already valuable practical applications of the technology.
Q.2 Ans
What is Knowledge Representation and Reasoning (KR or KRR): A subarea of Artificial Intelligence concerned with understanding, designing, and implementing ways of representing information in computers, and using that information to derive new information based on it. KR is more concerned
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with belief than knowledge". Given that an agent (human or computer) has certain beliefs, what else is reasonable for it to believe, and how is it reasonable for it to act, regardless of whether those beliefs are true and justified. Q.3. Ans
What is logic? Explain different types of logic used in knowledge represention.. Logic is the study of correct reasoning.It is a collection of rules we use when doing logical reasoning. Human reasoning has been observed over centuries from at least the times of Greeks, and patterns appearing in reasoning have been extracted, abstracted, and streamlined. The foundation of the logic we are going to learn here was laid down by a British mathematician George Boole in the middle of the 19th century, and it was further developed and used in an attempt to derive all of mathematics by Gottlob Frege, a German mathematician, towards the end of the 19th century. A British philosopher/mathematician, Bertrand Russell, found a flaw in basic assumptions in Frege's attempt but he, together with Alfred Whitehead, developed Frege's work further and repaired the damage. The logic we study today is more or less along this line. In logic we are interested in true or false of statements, and how the truth/falsehood of a statement can be determined from other statements. However, instead of dealing with individual specific statements, we are going to use symbols to represent arbitrary statements so that the results can be used in many similar but different cases. The formalization also promotes the clarity of thought and eliminates mistakes. There are various types of logic such as logic of sentences (propositional logic), logic of objects (predicate logic), logic involving uncertainties, logic dealing with fuzziness, temporal logic etc. Here we are going to be concerned with propositional logic and predicate logic, which are fundamental to all types of logic. Propositional logic is a logic at the sentential level. The smallest unit we deal with in propositional logic is a sentence. We do not go inside individual sentences and analyze or discuss their meanings. We are going to be interested only in true or false of sentences, and major concern is whether or not the truth or falsehood of a certain sentence follows from those of a set of sentences, and if so, how. Thus sentences considered in this logic are not arbitrary sentences but are the ones that are true or false. This kind of sentences are called propositions.
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Q.4. Ans
Explain Horn Clause. A Horn clause is a clause containing at most one positive literal. Examples of a Horn Clause [￢Child, ￢Mail,Boy]
Q5.
Explain the process of Skolemization. How is this accomplished? Give suitable examples insupport of your answer.
Ans. Skolemization is the process of removing existential quantifiers by elimination. In the simpletranslate into P (A), where A is a constant that does not appear elsewhere in the KB. But thereis the added complication that some of the existential quantifiers, even though move left, may still benested inside a universal quantifier.Skolemization is accomplished as follows: If the first (leftmost) quantifier in an expression is an existential quantifier, replace alloccurrences of the variable it quantifier with an arbitrary constant not appearingelsewhere and delete the quantifier. The same procedure should be followed for all otherexistential quantifiers not preceded by a universal quantifier, in each case, using differentconstant symbols in the substitution. For each existential quantifier that is preceded by one or more universal quantifiers (iswithin the scope of one or more universal quantifiers) replace all occurrences of theexistentially quantified variable by a function symbol not appearing elsewhere in theexpression. The argument assigned to the function should match all the variablesappearing in each universal quantifier which preceded the existential quantifier. Thisexistential quantifier should then be deleted. The same procedure should be repeated foreach remaining existential quantifier using a different function symbol and choosingfunction arguments that correspond to all universally quantified variables that precede theexistentially quantified variable being replaced.Example of Skolemization Consider “Everyone has a heart”: Λ Has (x, y) If we just replaced y with a constant, H, we would get,
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Λ Has (x, H) Which says that everyone has the same heart H.? We need to say that the heart they have is notnecessarily shared, that is, it can be found by applying to each person a function that maps from personto heart: Λ Has (x, F(x)) Where F is a function name that does not appear elsewhere in the KB. F is called a SkolemFunction. In general, the existentially quantified variable is replaced by a term that consists of a SkolemFunction applied to all the variables universally quantified outside the existential quantifier in question.Skolemization eliminates all existentially quantified variables, so we are now free to drop the universalquantifiers, because any variable must be universally quantified. Q.6. Comment on “Heuristics are fallible.” Ans. Heuristics are fallible because they rely on limited information, they may lead to a suboptimalsolution or to a dead end.Heuristics is a rule of thumb or judgmental technique that leads to a solution some of the timebut provides no guarantee of success. It may in fact end in failure. Heuristics plays an important rolein search strategies because of the exponential nature of most problems. They help to reduce thenumber of alternatives from an exponential number to a polynomial number and, thereby, obtain asolution to a tolerable amount of time. When exhaustive search is impractical, it is necessary tocompromise for a constrained search which eliminates many paths but offers the promise of successsome of the time. Here, success may be considered to be finding an optimal solution a fair proportionof the time or just finding good solutions much of the time.
Q7. Ans
Requirements of KR languages 1. At the implementational level, the main concern is efficiency (space and time). 2. At the logical level, one is concerned with two things: the syntax (Frege’s compositionality principle, which states that the meaning of a compound expression should be derivable from the meanings of its parts) and the soundness of the inference rules.
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3. At the epistemological level, there are four main concerns: If there exists a natural way of organising knowledge, the KR language has to respect this; the KR language has to be modular; attention to the granularity (=size of the information chunks) is important; and the language should support the actual primitives of the conceptual level. 4. At the conceptual level, one is concerned with how concisely one can represent particular pieces of knowledge. Q8. Ans
What are semantic nets? There are two types of primitives: nodes and links. Nodes correspond to objects, or classes of objects, in the world, whereas links correspond to relationships between these objects. They are often labeled to indicate their meaning. No information is stored in a node as such; this is done with the links associating nodes. Quillians original work was taken up by psychologists, but also work done in linguistics was important for the development of the semantic net approach. Representing information in semantic nets Quillian distinguished between type and token nodes, corresponding to the two ways in which a word can occur in a dictionary (as entry or as part of an explanation). Quillian distinguished furthermore between five link types: A is 1. subclass of B 2. B modifies A 3. Conjunction of a number of nodes 4. Disjunction of a number of nodes 5. B is subject of the relation A which has as object C (see p. 118 for a figure with an example) Quillian did not have problems with the circularity caused by the fact that he did not set up primitives. Others took this primitive word approach (see further). A second important source was the development of Case Grammar (Fillmore). He distinguished between a surface structure and a deep structure in each sentence, related by a number of transformations. The deep structure has a modality (tense, mood) and a proposition (a verb with a number of cases, i.e., roles to be filled in by other parts of the sentence).
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Inferencing in semantic nets The basic inference mechanism consists of following links between nodes. There are two types of strategies: · Spreading of activation (or intersection search) · Inheritance. The first type tries to find a concept that is related to the words to be related by the inference. Each node can have an activation tag during this process. When a new node N is reached following a link from A or from a certain C that can be reached from A, three things can happen: 1. There is already an activation tag mentioning word B associated with N: in this case, N is a basis for comparison. 2. There is already an activation tag mentioning word A: no new tag is created 3. There is no tag: an activation tag is created. Also here, the problem of control returns. In Quillians’ approach, this is solved by bidirectional search (first from A, then B, then A and so on). But there are other possibilities. Not all associative network representations use spreading of activation: in frames, for example, this is never used. But inheritance is. Inheritance consists in deriving properties of subclasses from superclasses. Some researchers do not distinguish between Inst links between tokens and their types and the Sub link between classes and their superclasses. This is a mistake; you have to. Q9. Ans
What is Prolog ? Prolog (programming in logic) is a logic-based programming language: programs correspond to sets of logical formulas and the Prolog interpreter uses logical methods to resolve queries. • Prolog is a declarative language: you specify what problem you want to solve rather than how to solve it. • Prolog is very useful in some problem areas, such as artificial intelligence, natural language processing, databases, . . . , but pretty useless in others, such as for instance graphics or numerical algorithms.
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Q10. Explain Backtracking. Ans Backtracking: During goal execution Prolog keeps track of choicepoints. If a particular path turns out to be a failure, it jumps back to the most recent choicepoint and tries the next alternative. This process is known as backtracking. Q11. Explain Traveling Salesman problem? Ans The traveling salesman problem is too complex to be solved via exhaustive search for large values of N. The nearest neighbor heuristic work well most of the time, but with some arrangements of cities it does not find the shortest path. Consider the following two graphs. In the first, nearest neighbor will find the shortest path. In the second it does not:
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Example: Consider the game of tic-tac-toe. Even if we use symmetry to reduce the search space of redundant moves, the number of possible paths through the search space is something like 12 x 7! = 60480. That is a measure of the amount of work that would have to be done by a brute-force search.
Simple heuristic for tic-tac-toe: Move to the square in which X has the most winning lines. Using this rule, we can see that a corner square has heuristic value of 3, a side square has a heuristic value of 2, but the center square has a heuristic value of 4. So we can prune the left and right branches of the search tree. This removes 2/3 of the search space on the first move. If we apply the heuristic at each level of the search, we will remove most of the states from consideration thereby greatly improving the efficiency of the search.
Multiple Choice Questions 1.
Knowledge can be defined as a) Combination of state and facts b) Gathering of new information c) Possession of facts and principles gathered
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d) None of the above Ans:c 2.
Horn clause is a clause having a) At most one positive literal b) All literal as +ve literal c) At most one –ve literal d) None of the above Ans: a
3.
A.I. programming language which solves problems with a form of symbolic logic a) PROLOG b) C c) VB d) JAVA Ans: a
4.
In a rule based system, procedural domain knowledge in the form of a) Production rule b) Rule interpreters c) Meta rule d) Common rule Ans: a
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The simulation approach in regard to A.I includes all of the following problem EXCEPT: Among the following which is not a horn clause? a) P b) pVq c) p→q d) p→ q e) All the above. Ans : (d)
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Knowledge may be I. Declarative. II. Procedural.
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III. Non-procedural. (a) Only (I) above (b) Only (II) above (c) Only (III) above (d) Both (I) and (II) above Ans : (a) Reason : Idempotency Law is P V P = P 7.
What is the goal of artificial intelligence? (a) To solve real-world problems (b) To solve artificial problems (c) To explain various sorts of intelligence (d) To extract scientific causes (e) To restrict problems. Ans:c
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An algorithm is complete if (a) It terminates with a solution when one exists (b) It starts with a solution (c) It does not terminate with a solution (d) It has a loop (e) It has a decision parameter. Ans : (a) Reason : An Algorithm is complete if It terminates with a solution when one exists.
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In language understanding, the levels of knowledge that does not include (a) Phonological (b) Syntactic (c) Semantic (d) Logical (e) Empirical. Ans : (e) Reason : In language understanding, the levels of knowledge that does not include empirical knowledge
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10.
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What is a heuristic function? (a) A function to solve mathematical problems (b) A function which takes parameters of type string and returns an integer value (c) A function whose return type is nothing (d) A function which returns an object (e) A function that maps from problem state descriptions to measures of desirability. Ans : (e)
Default reasoning is another type of (a) Monotonic reasoning (b) Analogical reasoning (c) Bitonic reasoning (d) Non-monotonic reasoning (e) Closed world assumption. Ans : (d)
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Unit 3
Expert System Q.1 Ans
What is Expert System and its advantage? Expert system is an A.I. program that has expert-level knowledge about a particular domain and knows how to use its as knowledge-based systems and knowledge-based expert systems. The expert’s knowledge about solving the given specific problems is called knowledge domain of the expert. Advantages of Expert Systems Availability: Expert systems are availabe easily due to mass production software. Cheaper: The cost of providing expertise is not expensive. Reduced danger: They can be used in any risky environments where humans cannot work with. Permanence: The knowledge will last long indefinitely. Multiple expertise: It can be designed to have knowledge of many experts. Explanation: They are capable of explaining in detail the reasoning that led to a conclusion. Fast response: They can respond at great speed due to the inherent adavantages of computers over humans. Unemotional and repsonse at all times: Unlike humans, they do not get tense, fatigue or panic and work steadily during emergency situations.
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Q2. Ans
Explain MYCIN? Mycin is a program that diagnoses infectious diseases. It reasons backward from its goal of determining the cause of a patient illness. It attempts to solve its goal of recommending a therapy for a particular patient by first finding the cause of the patient’s illness. It uses its production rule4s to reason backward from goals to clinical observations. To solve the top-level diagnostic goal, it looks for rules whose right sides suggest diseases. It then uses the left sides of those rules (the preconditions) to set up sub goals whose success would enable the rules to be invoked . these sub goals are again matched against rules, and their preconditions are used to set up additional sub goals. Mycin is a well known rule based deduction system. Its expertise lies in the domain of bacterial Infections. Physicians usually must begin antibiotic treatment for patient who have bacterial infections without knowledge exactly which organism is the culprit. There is no time to wait for definitive laboratory culture evidence, which accumulates too slowly. For the desperately sick, therapy must begin at once – not 2 days from can either prescribe a broad – spectrum drug that covers all possibilities , or she can prescribed a better, disease – specific drug. Mycin helps the physician to prescribe disease – specific drugs. Mycin in-forms it self about particular cases by requesting information from the physician about a patient’s symptoms, general condition. History, and laboratory – test results that can be obtained easily and quickly. At each point, the question mycin asks is determined by Mycin’s current hypothesis and the answers to all previous questions.
Q.3. Ans
Explain DENDRAL. DENDRAL DENDRAL is a program that analyses organic compounds to determine their structure. It is one of the early example of a successful AI program . It uses a strategy called plan-generate-test in which a planning process that used constraint-satisfaction techniques, creates lists of recommended and contraindicated substructures.
Q.4.
Explain Parameter required in building of any Expert System:
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(1) Meta-knowledge: Meta-knowledge is knowledge about knowledge. or we can say thatmeta-knowledge is systematic problem and domain –independent knowledge which performs or enables operation in another more or less specific domain dependentknowledge in different domain of human activities.Meta knowledge can be considered as a fundamental conceptual instrument in suchresearch and scientific domains as, knowledge engineering, knowledge management andothers dealing with study & operations on knowledge that we know. (2) Expertise Transfer : the objective of expertise transfer is to transfer expertise from one expert system to a computer system. these process evolved four activities:(a)Knowledge acquisition( from expert or other sources)(b)Knowledge representation( in the computer)(c)Knowledge inferencing.(d)Knowledge transfer to user. (3) Domain Exploration : in general Domain knowledge in the knowledge which is validand directly used for preselected domain of human or autonomous computer activity.different specialist and expert use and develop their own domain knowledge. Domainrefer to the knowledge that is part of the world the system knows about .this includeobject description, relationship and other relevant concepts.
Q.5. Ans
Explain Self Explaining System. The more interesting feature of expert system is their ability toexplain themselves, is known as self explaining system. Most system has the self explainingfacility that means why it asked certain question, how it arrived its answers. Most of theseanswers are provided by explanation module. It provides the used with an explanation of reasoning process when requested.Expert system contains many modules to make it operate (Ex: memory). One of the module isself explaining module. This module is very much required in medical expert systems. Themodule explains how a conclusion is arrived about a patient and what its basics assumptions for deriving that conclusion.
Q6. Ans
Characterestic of expert system Characteristics of Expert system (comparison between the Expert systemand conventional computer system)
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1. Expert system use knowledge rather than data to control the solution process. Much of the knowledge used in heuristic in nature rather than algorithm. 2. The knowledge is encoded and manipulate as an entity separate from the control programsuch as if not compiled together with the control program itself. In some cases, it is possible to use different knowledge bases with the same control program to producedifferent types of expert system such system are known as “Expert system shells”. 3. Expert systems are capable of explaining how a particular conclusion was drawn and whyrequested information is needed during a conclusion. 4. Expert system use symbolic representation for knowledge (rules, networks or frames) and perform their inference through symbolic computations that closely resemble manipulateof manual language. Q7. Ans
Advantage of Expert system: 1. Expert systems do not forget, but human expert may forget. 2. An expert system needs the symbolic representation. 3. It reduces the risk of doing business. 4. It provides the permanent documentation of the decision process.5.An Expert system can review all the transactions but human expert system can onlyreview a sample.6.Expert system able to deal with uncertainty. 5. Expert systems are not focus on abstract. It delivers the answer to goal oriented of the interview
Q.9 Ans
Disadvantage of Expert system (or limitations of Expert system): 1. Expert system has no any common sense. 2. Expert system cannot respond creatively to unusual situation. 3. Expert system must be explicitly updates while any changes in environments. 4. Expert system is currently dependent on symbolic input. 5. Measuring the performance of an expert system is difficult because we do not know howto quantify the use of knowledge.6.An Expert system has access to highly specific domain knowledge.
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Multiple Choice Questions 1.
2.
Mycin developed for a) Blood pressure b) Underground minerals c) Meningitis and infectious blood desease d) None of the above Ans: c
PROLOG is an AI programming language which solves problems with a form of symbolic logic known as predicate calculus. It was developed in 1972 at the University of Marseilles by a team of specialists. Can you name the person who headed this team? a) Alain Colmerauer b) Nicklaus Wirth c) Seymour Papert d) None of the above Ans: a 3.
Travelling salesman problem can be solved using a) Hill climbing b) Means and analysis c) Constraint satisfaction d) None of the above Ans: b
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An expert system differs from a database program in that only an expert system:
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a) Contains declarative knowledge b) Contains procedural knowledge c) Features the retrieval of stored information d) Expects users to draw their own conclusion Ans: b 5.
Which one is NOT the advantage of Neural Network a) Excellent for pattern recognition b) Excellent classifiers c) Handles noisy data well d) None of the given Ans: d
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Decision trees give us disjunctions of conjunctions, that is, they have the form: (A AND B) _______ (C AND D). a) OR b) AND c) XOR d) None of the given Ans: a
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Default reasoning is another type of (a) Monotonic reasoning (b) Analogical reasoning (c) Bitonic reasoning (d) Non-monotonic reasoning (e) Closed world assumption. Ans : (d) Consider a good system for the representation of knowledge in a particular domain. What property should it possess? (a) Representational Adequacy (b) Inferential Adequacy (c) Inferential Efficiency (d) Acquisitional Efficiency (e) All the above.
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Ans: (e) 9. Perception involves a) Hitting b) Boxing c) Dancing d) Sights,rounds,smell and touch Ans : d 10.
Name of the computer programm that contains expertise in particular domain is called an a) Expert system b) Personel information c) Human logic d) None Ans:a
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Component of expert system a) Inference engine b) User interface c) Knowledge base d) None of the above e) All of the above Ans:e
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Key Terms User Interface: Provides the means for dialog between the user and system. Explanation facility : Provides the user with Explanations of how a conclusion was reached or why a piece of knowledge is needed. They also need to be convinced that the solution isappropriate and applicable in their circumstances. Inference Engine accepts user input quarries and response to questions through the user interface and uses this dynamic information together with the static knowledge (the rules andfacts) stored in the knowledge base. The inference process is carried out recursively in three stages (I) match (II) select (III) execute.During the match stage, the contents of working memory are compared to facts and rulescontained in the knowledge base. Knowledge base contains facts and rules about some specialized knowledge domain. Learning module implies that an organize or machine must be able to adapt to new situations.The job of Knowledge engineer is to extract the knowledge from the expert and other sourceslike book, journals, article etc. Adaptive Interface A computer interface that automatically and dynamically adapts to the needs and competence of each individual user of the software. Agents Agents are software programs that are capable of autonomous, flexible, purposeful and reasoning action in pursuit of one or more goals. They are designed to take timely action in response to external stimuli from their environment on behalf of a human. When multiple agents are being used together in a system, individual agents are expected to interact together as appropriate to achieve the goals of the overall system. Also called autonomous agents, assistants, brokers, bots, droids, intelligent agents, software agents. AI Languages and Tools: AI software has different requirements from other, conventional software. Therefore, specific languages for AI software have been
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developed. These include LISP, Prolog, and Smalltalk. While these languages often reduce the time to develop an artificial intelligence application, they can lengthen the time to execute the application. Therefore, much AI software is now written in languages such as C++ and Java, which typically increases development time, but shortens execution time. Also, to reduce the cost of AI software, a range of commercial software development tools have also been developed. Stottler Henke has developed its own proprietary tools for some of the specialized applications it is experienced in creating. Algorithm : An algorithm is a set of instructions that explain how to solve a problem. It is usually first stated in English and arithmetic, and from this, a programmer can translate it into executable code (that is, code to be run on a computer). Applications of Artificial Intelligence : The actual and potential applications are virtually endless. Reviewing Stottler Henke's work will give you some idea of the range. In general, AI applications are used to increase the productivity of knowledge workers by intelligently automating their tasks; or to make technical products of all kinds easier to use for both workers and consumers by intelligent automation of different aspects of the functionality of complex products. Associative Memories : Associative memories work by recalling information in response to an information cue. Associative memories can be autoassociative or heteroassociative. Autoassociative memories recall the same information that is used as a cue, which can be useful to complete a partial pattern. Heteroassociative memories are useful as a memory. Human long-term memory is thought to be associative because of the way in which one thought retrieved from it leads to another. When we want to store a new item of information in our long term memory it typically takes us 8 seconds to store an item that can't be associated with a pre-stored item, but only one or two seconds, if there is an existed information structure with which to associate the new item. Autonomous Agents A piece of AI software that automatically performs a task on a human's behalf, or even on the behalf of another piece of AI software, so together they accomplish a useful task for a person somewhere. They are capable of independent action in dynamic, unpredictable environments. "Autonomous agent" is a trendy term that is sometimes
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reserved for AI software used in conjunction with the Internet (for example, AI software that acts as your assistance in intelligently managing your e-mail). Autonomous agents present the best hope from gaining additional utility from computing facilities. Over the past few years the term "agent" has been used very loosely. Our definition of a software agent is: "an intelligent software application with the authorization and capability to sense its environment and work in a goal directed manner." Generally, the term "agent" implies "intelligence", meaning the level of complexity of the tasks involved approaches that which would previously have required human intervention. Backtracking A control method used to search backwards for solutions Clauses Either a Prolog fact or rule. Cognitive Science : Cognitive Science, as a discipline, is concerned with learning how animals (and machines) acquire knowledge, represent that knowledge, and how they manipulate those representations.. Computer Vision :Making sense of what we see is usually easy for humans, but very hard for computers. Practical vision systems to date are limited to working in tightly controlled environments. Synonym: machine vision Domain : An overworked word for AI people. "Domain" can mean a variety of things including a subject area, field of knowledge, an industry, a specific job, an area of activity, a sphere of influence, or a range of interest, e.g., chemistry, medical diagnosis, putting out fires, operating a nuclear power plant, planning a wedding, diagnosing faults in a car. Generally, a domain is a system in which a particular set of rules, facts, or assumptions operates. Humans can usually easily figure out what's meant from the context in which "domain" is used; computers could probably not figure out what a human means when he or she says "domain."
Domain Expert The person who knows how to perform an activity within the domain, and whose knowledge is to be the subject of an expert system. This person's or persons' knowledge and method of work are observed, recorded, and entered into a knowledge base for use
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by an expert system. The domain expert's knowledge may be supplemented by written knowledge contained in operating manuals, standards, specifications, computer programs, etc., that are used by the experts. Synonym: subject-matter expert (SME). Extension Language A general-purpose programming language accessible to the users of the application created with that language. LISP dialects (including Scheme) are often suitable extension languages. Expert System An expert system encapsulates the specialist knowledge gained from a human expert (such as a bond trader or a loan underwriter) and applies that knowledge automatically to make decisions. For example, the knowledge of doctors about how to diagnose a disease can be encapsulated in software. The process of acquiring the knowledge from the experts and their documentation and successfully incorporating it in the software is called knowledge engineering, and requires considerable skill to perform successfully. Applications include customer service and helpdesk support, computer or network troubleshooting, regulatory tracking, autocorrect features in word processors, document generation such as tax forms, and scheduling. Game Theory Game theory is a branch of mathematics that seeks to model decision making in conflict situations. Heuristics A term describing an exploratory method of attacking a problem in which the solution is obtained by successive evaluations of progress toward the final results. Inference Engine The part of an expert system responsible for drawing new conclusions from the current data and rules. The inference engine is a portion of the reusable part of an expert system (along with the user interface, a knowledge base editor, and an explanation system), that will work with different sets of case-specific data and knowledge bases. Knowledge-based Representations The form or structure of databases and knowledge bases for expert and other intelligent systems, so that the information and solutions provided by a system are both accurate and complete. Usually involves a logically-based language capable of both syntactic and semantic representation of time, events, actions, processes, and entities. Knowledge
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representation languages include Lisp, Prolog, Smalltalk, OPS-5, and KL-ONE. Structures include rules, scripts, frames, endorsements, and semantic networks. Knowledge knowledge--1. In artificial intelligence, symbolic information used by a domain expert to solve problems. 2. Facts and relationships used to solve problems. Knowledge-based Systems Usually a synonym for expert system, though some think of expert systems as knowledge-based systems that are designed to work on practical, real-world problems. Knowledge Elicitation Synonym: knowledge acquisition.
Knowledge Engineering Knowledge engineering is the process of collecting knowledge from human experts in a form suitable for designing and implementing an expert system. The person conducting knowledge engineering is called a knowledge engineer. Knowledge Representation Knowledge representation is one of the two basic techniques of artificial intelligence, the other is the capability to search for end points from a starting point. The way in which knowledge is represented has a powerful effect on the prospects for a computer or person to draw conclusions or make inferences from that knowledge. Consider the representation of numbers that we wish to add. Which is easier, adding 10 + 50 in Arabic numerals, or adding X plus L in Roman numerals? Consider also the use of algebraic symbols in solving problems for unknown numerical quantities, compared with trying to do the same problems just with words and numbers. LISP LISP (short for list processing language), a computer language, was invented by John McCarthy, one of the pioneers of artificial intelligence. The language is ideal for representing knowledge (e.g., If a fire alarm is ringing, then there is a fire) from which inferences are to be drawn. Machine Learning:
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Machine learning refers to the ability of computers to automatically acquire new knowledge, learning from, for example, past cases or experience, from the computer's own experiences, or from exploration. machine code--An operation code that a machine is designed to recognize. Natural Language Processing The study of strategies for computer programs to recognize and understand language in spoken and written form.
Neural Networks Neural networks are an approach to machine learning which developed out of attempts to model the processing that occurs within the neurons of the brain. Procedural Language The traditional programming that is based on algorithms or a logical step-by-step process for solving a problem. Proposition An expression about an object which can have either a true or false value. Propositional Calculus The formal logic system used to define the true or false values of objects. Pattern Recognition 1.The recognition of forms, shapes, or configurations by automatic means. A subfield of artificial intelligence. 2. The use of a computer to identify patterns.. 3. The use of statistical techniques and templates to process and classify patterns of data Plan Recognition The goal of plan recognition is to interpret an agent's intentions by ascribing goals and plans to it based on partial observation of its behavior up to the current time. Divining the agent's underlying plan can be useful for many purposes including: interpreting the agent's past behavior, predicting the agent's future behavior, or acting to collaborate with (or thwart) the agent. Robotics is the branch of technology that deals with the design, construction, operation, structural disposition, manufacture and application of robots. Rule A clause that defines the relationship or relationships between facts and objects.
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Relevance Feedback Relevance feedback methods are used in information retrieval systems to improve the results produced from a particular query by modifying the query based on the user's reaction to the initial retrieved documents. Specifically, the user's judgments of the relevance or non-relevance of some of the documents retrieved are used to add new terms to the query and to reweight query terms. For example, if all the documents that the user judges as relevant contain a particular term, then that term may be a good one to add to the original query.
Rule-based System An expert system based on IF-THEN rules for representing knowledge. Scheme Langauge A LISP dialect often used within computer science curricula and programming language research. Speech Recognition The ability of a computer to understand spoken words for the purpose of receiving commands and data input from the speaker. Source Code Symbolic coding in its original form before being processed by a computer. Simulation A simulation is a system that is constructed to work, in some ways, analogously to another system of interest. The constructed system is usually made simpler than the original system so that only the aspects of interest are mirrored. Simulations are commonly used to learn more about the behavior of the original system, when the original system is not available for manipulation. It may not be available because of cost or safety reasons, or it may not be built yet and the purpose of learning about it is to design it better. If the purpose of learning is to train novices, then cost, safety, or convenience are likely to be the reasons to work on a simulated system. The simulation may be a computer simulation (perhaps a realistic one of a nuclear power station's control room, or a mathematical one such as a spreadsheet for "what-if" analysis of a company's business); or it may be a small-scale physical model (such as a small-scale bridge, or a pilot chemical plant). Turing Test--A game to determine whether a computer might be considered to possess intelligence, developed by British mathematician Alan Turing. Participants include two
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respondents (a computer and a human) and a human examiner who tries to determine which of the unseen respondents is the human. According to this test, intelligence and the ability to think would be demonstrated by the computer's success in fooling the examiner. Unification The pattern matching technique used by Prolog to match goals and subgoals in a program.
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Abbreviations AI : QA: IR: IE: NLP: XML: AIML: ALICE: PNAMBIC:
Artificial Intelligence Question Answering Information Retrieval Information Extraction Natural Language Processing Extensible Markup Language Artificial Intelligence Markup Language Artificial Linguistic Internet Computer Entity Pay No Attention to that Man Behind the Curtain
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Bibliography 1. Charniak, E.: Introcuction of Artificial Intelligence, Narosa Publishing House. 2. Winton. P.H. : LISP, Narosa Publishing House. 3. Marcellus: Expert System Programming in TURBO PROLOG PrenticeHall Inc. 1989. 4. Clark, K. L. & McCabe, F.G.: Micro-Prolog Prentice-Hall Inc. 1987 5. Elaine rich & Kevin Knight: Artificial Intelligence and Expert System, PHI.
