III/IV B.Tech, (Regular/Supplementary) DEGREE EXAMINATION

November, 2016 Fifth Semester Time: Three Hours Answer Question No.1 compulsorily. Answer ONE question from each unit. III/IV B.Tech, (Regular/Supplementary) DEGREE EXAMINATION 14CS506/A Computer Science &Engineering Artificial Intelligence Maximum : 60 Marks (1X12 = 12 Marks) (4X12=48 Marks) 1. Answer all questions (1X12=12 Marks) a) Define state space. State space is a set of legal positions. Starting at the initial state using the set of rules to move from one state to another. Attempting to end up in a goal state Problem solving = Searching for a goal state. b) Define Constraint Satisfaction Problem. Constraint Satisfaction Problem is a goal to discover some problem state using given set of constraints. c) What is heuristic search technique. Heuristic technique improves the efficiency of a search process, possibly by sacrificing claims of completeness or optimality.heuristics are like tour guide d) State the fundamental goal of Knowledge Representation. The fundamental goal is to represent knowledge in a manner that facilitates inferencing (i.e. drawing conclusions) from knowledge. e) What is ridge? Ridge is a special kind of local maxima.it is an area of search space that is higher than surrounding areas and that itself has a slope. f) What are the three levels in describing knowledge based agent? Knowledge Level Logical Level Implementation Level g) List the components of a script. Entry Conditions Results Props Track Roles Scenes h) What are the different types of planning? Goal Stack Planning Non Linear Planning Hierarchical Planning i) What is goal stack planning? Goal Stack Planning is used to handle interactive compound goals. Here the stack contains goals, operators. j) What is meant by learning? Learning is a an area of AI that focusses on processes of self-improvement. k) Write are the Applications of Expert System? MYCIN DENDRAL

DEVICE ADVISOR PROSPECTOR EMYCIN l) Give the classification of learning process. Learning by Induction Learning by taking advice Skill Refinement Problem Solving UNIT I 2. a) Explain Artificial Intelligence with suitable example. Artificial Intelligence: The study of how to make computers do things which at the moment people do better. Any Example b) Discuss in detail about the characteristics of various categories of problems. 1)Is the problem decomposable into a set of independent smaller or easier sub problems? At each step it checks to see whether the problem it is working on is immediately solvable. Is so, the answer is returned directly. If the problem is not easily solvable, the integrator checks to see whether it can decompose the problem into smaller problems. If it can, it creates those problems and calls itself recursively on them, using this technique of problem decomposition. 2)Can solution steps be ignored or at least undone if they prove unwise? It illustrates the differences between 3 important problems: Ignorable, in which solution steps can be ignored. Recoverable, in which solution steps can be undone. Irrecoverable, in which solution steps cannot be undone. 3)Is the problem universe predictable? For solving certain-outcome problems, open-loop approach will work fine since the result of an action can be predicted perfectly. For uncertain-outcome problems, planning can at best generate a sequence of operators that has a good probability of leading to a solution. 4)Is a good solution to the problem obvious without comparison to all other possible solutions? For a question we follow one path successfully to an answer, there is no reason to go back and see if some other path might also lead to a solution. 5)Is the desired solution a state of the world or a path to a state? A statement of the solution to the problem must be a sequence of operations that produce a final state. 6)Is a large amount of knowledge absolutely required to solve the problem, or is knowledge important only to constraint the search? A lot knowledge is important only to constrain the search for a solution and those for which a lot of knowledge is required even to be able to recognize a solution 7)Can a computer that is simply given the problem return the solution, or will the solution of the problem require interaction between the computer and a person? (OR) 3. a) Given an example of a problem for which breadth-first search would work better than depth-first search. BFS:

BFS starts traversal from the root node and then explore the search in the level by level manner i.e. as close as possible from the root node. It can be done with the help of queue i.e. FIFO implementation. This algorithm works in single stage. The visited vertices are removed from the queue and then displayed at once. BFS is useful in finding shortest path.bfs can be used to find the shortest distance between some starting node and the remaining nodes of the graph. DFS: DFS starts the traversal from the root node and explore the search as far as possible from the root node i.e. depth wise. It can be done with the help of Stack i.e. LIFO implementations. This algorithm works in two stages in the first stage the visited vertices are pushed onto the stack and later on when there is no vertex further to visit those are popped-off. DFS in not so useful in finding shortest path. It is used to perform a traversal of a general graph and the idea of DFS is to make a path as long as possible, and then go back (backtrack) to add branches also as long as possible. b) Explain the algorithm for steepest hill climbing. 1. Evaluate the initial state. If it is also a goal state, then return it and quit. Otherwise, continue with the initial state as the current state. 2. Loop until a solution is found or until a complete iteration produces no change to current state: a) Let SUCC be a state such that any possible successor of the current state will be better then SUCC. b) For each operator that applies to the current state do: i. Apply the operator and generate a new state. ii.evaluate the new state. If it is goal state, then return it and quit. If not, compare it to SUCC. If it is better, then set SUCC to this state. If it is not better, leave SUCC alone. c) If the SUCC is better than current state, then set current state to SUCC. UNIT II 4. a) Discuss various approaches in knowledge Representation Declarative Knowledge:It is the simplest way of storing facts by using a relational method where each fact about a set of objects is set out systematically in columns. This representation gives little opportunity for inference but it can be used as the knowledge basis for inference engines. A simple way to store facts. Each fact about a set of objects is set out systematically in columns. Little opportunity for inference. Knowledge basis for inference engines. Procedural Knowledge: The basic idea of procedural knowledge is to encode the knowledge in some procedures. These procedures may include small programs that know how to do specific things

and how to proceed. b) Briefly discuss about different matching techniques. INDEXING The one way to select applicable rules is to do a simple search through all the rules,comparing each ones predictions to the current state and extracting all the ones that match. But there 2 problems with the simple solutions. To solve very interesting problems,it will be necessary to use a large number of rules. Scanning through all of them at every step of the search would be hopelessly inefficient. It is not always immediately obvious whether a rules predictions of satisfied by a particular state. MATCHING WITH VARIABLES The temporal nature of data.rules usually do not alter the state description radically. Instead,a rule will typically add one or two elements,or perhaps delete one or two,but most of state description remains the same. Full matching is only pursued for candidates that could be affected by incoming or outgoing data. Structural similarity in rules.different rules may share a large number of preconditions. Persistence of variable binding consistence while all the individual preconditions of a rule might be met, there may be variable binding conflicts that prevent the rule from firing. Son(x,y) ^ son(y,z) -> grandparent(x,y) COMPLEX AND APPROPRIATE MATCHING A more complex matching process is required when the predictions of rule specify required properties that are not stated explicitly in description of the current state. In this case, a separate set of rules must be used to describe how some properties can be inferred from others. An even more complex matching process is required if rules should be applied if their predictions approximately match the current situation. CONFLICT RESOLUTION The result of the matching process is a list of rules whose antecedents have matched the current state description along with whatever variable binding where generated by the matching process. But sometimes it is useful to incorporate some of that decision making into the matching process. This is the phase of the matching process. This phase of the matching process is then call conflict resolution. preference based on the rule that matched. preference based on the object that matched. preference based on the state (OR) 5. a) Convert the following sentences into predicate logic i)marcus was a man ii) Everyone is loyal to someone iii)marcus was tried to assassinate Caesar I)Marcus was a man. Man(Marcus) II) Everyone is loyal to someone. x: y: loyalto(x, y) III)Marcus was tried to assassinate Caesar tryassassinate(marcus,caesar) b) Explain forward and Backward chaining with an example. Backward Chaining: Backward chaining rule systems,of which PROLOG as an example,are good for goal directed problem solving.

For Example:A query system would probably use Backward chaining to reason about and answer user questions. In PROLOG rules are restricted to HORN clauses.rules are matched with unification procedure.unification tries to find set of bindings for variables to equate a goal with head of some rules.rules in PROLOG program are matched in the order in which they appear. Forward Chaining: Instead of being directed by goals we sometimes want to be directed by incoming data. For Example:Suppose you sense heat near your hand.you are likely to jerk your hand away. While this could be constructed as a goal directed behavior,it is modelled more naturally by the recognize-act cycle characteristics of Forward chaining rule system.in forward rule chaining system,left side rules are matched with right side assertions into the state and the process repeats. UNIT III 6. a) What is a semantic Net? Draw and explain the semantic net for the following statement: Every dog has bitten the constable Semantic Nets: The information is stored by interconnecting nodes with labelled arcs Every dog has bitten the constable b) Explain about Goal stack Planning Basic Idea to handle interactive compound goals uses goal stacks, Here the stack contains : goals, operators -- ADD, DELETE and PREREQUISITE lists a database maintaining the current situation for each operator used. Consider the following where wish to proceed from the start to goal state. Fig. Goal Stack Planning Example We can describe the start state: ON(B, A) ONTABLE(A) ONTABLE(C) ONTABLE(D) ARMEMPTY and goal state: ON(C, A) ON(B,D) ONTABLE(A) ONTABLE(D) After solving the problem: 1. UNSTACK(B,A) 2. STACK (B,D) 3. PICKUP(C)

4. STACK (C,A) This method produces a plan using good Artificial Intelligence techniques such as heuristics to find matching goals and the A* algorithm to detect unpromising paths which can be discarded. (OR) 7. a) What is script? Write a script for writing an examination and Explain. A script is a structure that describes the stereotyped sequence of events in a particular context. Example: b) Describe the hierarchical planning with an Example. In order to solve a problem efficiently we have to eliminate some of the details Of the problem. But in this approach no details are eliminated from the operator. Hierarchial planning involves aapproach called hierarchy abstraction space In which preconditions at low level of abstracton are ignored.

ABSTRIPS to the problem solving as follows: First solve the problem completely,considering the pre conditions whose critically Value is highest possible. STRIPS then ignore the preconditions lower then peak criticality. Continue this process of considering low preconditions untill all the rules of Preconditions have been condsider. Ex:PUSH-THROUGH-DOOR. UNIT IV 8. a) What is rote learning? Specify the components of expert system. Rote Learning is basically memorisation. Saving knowledge so it can be used again. Components of Expert Systems: Representing and using domain knowledge. Expert system shells. Explanation. Knowledge Acquisition. b) Discuss the MYCIN Expert system focusing on the certainty factor used. MYClN 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 rules 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 subgoals whose success would enable the rules to be invoked. These subgoals are again matched against rules, and their preconditions are used to set up additional subgoals. Whenever a precondition describes a specific piece of clinical evidence, MYCIN uses that evidence if it already has access to it. Otherwise, it asks the user to provide the information. In order that MYCIN s requests for information will appear coherent to the user, the actual goals that MYCIN sets up are often more general than they need be to satisfy the preconditions of an individual rule. For example, if a precondition specifies that the identity of an organism is X, MYCIN will set up the goal infer identity. This approach also means that if another rule mentions the organism s identity, no further work will be required, since the identity will be known. (OR) 9. a) Explain inductive Learning with an Example. Classification is the process of assigning, to a particular input, the name of the class to

which it belongs. The classes from which the classification procedure can choose can be described in a variety of ways. Classification is an important component of many problem-sloving tasks. Consider a problem-sloving system that contains the following production rules: If : the current goal is to get from place A to place B, and there is a WALL separating the two places then : look for a DOORWAY in the WALL and go through it. Any one of the three examples. Examples: 1.Winston s Learning Program. 2.Version Spaces. 3.Decision Trees. b) Write notes on knowledge acquisition technique in Expert system. After the initial system is built, it must be iteratively refineduntil it approximates expertlevel performance. This process is expensive and time-consuming, so it isworthwhile to look for more automatic ways of constructing expert knowledge bases. While no totally automatic knowledge acquisition systems yet exist, there are many programs that interact with domain experts to extract expert knowledge efficiently. These programs provide support for the following activities: Entering knowledge. Maintaining knowledge base consistency. Ensuring knowledge base completeness MOLE [Eshelmam 1988] is a knowledge acquisition system for heuristic classification problems, such as diagnosing diseases. In particular, it is used in conjunction with the cover-and-differentiate problem-solving method. MOLE interacts with a domain expert to produce a knowledge base that a system called MOLE-p (for MOLE-performance) uses to solve problems. The acquisition proceeds through several steps: 1. Initial knowledge base construction. MOLE asks the expert to list common symptoms or complaints that might require diagnosis, For each symptom, MOLE prompts for a list of possible explanations. MOLE then iteratively seeks out higher a level explanations until it comes up with a set of ultimate causes. During this process, MOLE builds an influence network similar to the belief networks. Whenever an event has multiple explanations, MOLE tries to determine the conditions under which one explanation is correct. The expert provides covering knowledge, that is, the knowledge that a hypothesized event might be the cause of a certain symptom. MOLE then tries to infer anticipatory knowledge, which says that if the hypothesized event does occur, then the symptom will definitely appear. This knowledge allows the system to rule out certain hypotheses on the basis that specific symptoms are absent. 2. Refinement of the knowledge base. MOLE now tries to identify the weaknesses of the knowledgebase- One approach is to find holes and prompt the expert to fill them. It is difficult, in general, to know whether a knowledge base is complete, so instead MOLE lets the expert watch MOLE-p solving sample problems. Whenever MOLE-p makes an incorrect diagnosis, the expert adds new knowledge here are several ways in which MOLE-p can reach the wrong conclusion. It may incorrectly reject a hypothesis because it does not feel that the hypothesis is needed to explain any symptom. It may advance a hypothesis because it is needed to explain some otherwise inexplicable

hypothesis. Or it may lack differentiating knowledge for choosing between alternative hypotheses.