c. What are agents? Explain how they interact with environment. Ans:

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1 (Time: 2½ hours) Total Marks: 75 N. B.: (1) All questions are compulsory. (2) Make suitable assumptions wherever necessary and state the assumptions made. (3) Answers to the same question must be written together. (4) Numbers to the right indicate marks. (5) Draw neat labeled diagrams wherever necessary. (6) Use of Non-programmable calculators is allowed. 1. Attempt any three of the following: 15 a. What is Artificial Intelligence? State its applications Artificial Intelligence is the study of how to make computers do things which at the moment, people do better. Perception - Vision - Speech Natural Language - Understanding - Generation - Translation Common Sense Reasoning Robot Control Games - Chess - Backgammon - Checkers - Go Mathematics - Geometry - Logic - Integral Calculus b. Discuss Turing test with Artificial Intelligence approach. The Turing Test, proposed by Alan Turing (1950), was designed to provide a satisfactory operational definition of intelligence. Turing's test deliberately avoided direct physical interaction between the interrogator and the computer, because physical simulation of a person is unnecessary for intelligence. However, the so-called total Turing Test includes a video signal so that the interrogator can test the subject's perceptual abilities, as well as the opportunity for the interrogator to pass physical objects "through the hatch." To pass the total Turing Test, the computer will need - computer vision to perceive objects, and - robotics to manipulate objects and move about. c. What are agents? Explain how they interact with environment.

2 d. What is rational agent? Discuss in brief about rationality. A rational agent is one that does the right thing. What is rational at any given time depends on four things: The performance measure that defines the criterion of success. The agent's prior knowledge of the environment. The actions that the agent can perform. The agent's percept sequence to date e. Explain PEAS description of task environment for automated taxi. f. Give comparison between Full observable and partially observable agent. Fully observable vs. partially observable: If an agent's sensors give it access to the complete state of the environment at each point in time, then we say that the task environ-ment is fully observable. A task environment is effectively fully observable if the sensors detect all aspects that are relevant to the choice of action; relevance, in turn, depends on the performance measure. Fully observable environments are convenient because the agent need not maintain any internal state to keep track of the world. An environment might be partially observable because of noisy and inaccurate sensors or because parts of the state are simply missing from the sensor data for example, a vacuum agent with only a local dirt sensor cannot tell whether there is dirt in other squares, and an automated taxi cannot see what other drivers are thinking. If the agent has no sensors at all then the environment is unobservable. 2. Attempt any three of the following: 15 a. Discuss in brief the formulation of single state problem. 1. initial state e.g., at Arad 2. actions: Actions(s) returns applicabile actions in s: (Go(Sibiu), Go(Timisoara), Go(Zerind) 3. transition model - set of action state pairs (succesor function Result(s,a)): e.g., Result(In(Arad), Go(Zerind))=In(Zerind) 4. goal test - determines if whether a given state is a goal state explicit, e.g., xs = at Bucharest implicit, e.g., xs = checkmate

3 5. path cost (additive) - reflects agent s own performance measure e.g., sum of distances, number of actions executed, etc. c(s, a, s ) is the step cost, assumed to be 0 b. Give the outline of Breadth First Search algorithm. 1. Create a variable called NODE-LIST and set it to initial state. 2. Until a goal state is found or NODE-LIST is empty do: a. Remove the first element from NODE-LIST and call it E. if NODE-LIST was empty, quit. b. For each way that each rule can match the state described in E do: i. Apply the rule to generate a new state. ii. If the new state is goal state, quit and return this state. iii. Otherwise, add the new state to the end of NODE-LIST. exp. node OPEN list CLOSED list { S } {} S { A B C } {S} A { B C D E G } {S A} B { C D E G G' } {S A B} C { D E G G' G" } {S A B C} D { E G G' G" } {S A B C D} E { G G' G" } {S A B C D E} G { G' G" } {S A B C D E} Solution path found is S A G <-- this G also has cost 10 Number of nodes expanded (including goal node) = 7 c. Give the outline of tree search algorithm. function Tree-Search( problem, fringe) returns a solution, or failure fringe Insert(Make-Node(Initial-State[problem]), fringe) loop do if fringe is empty then return failure node Remove-Front(fringe) if Goal-Test(problem,State(node)) then return node fringe InsertAll(Expand(node, problem), fringe) function Expand( node, problem) returns a set of nodes successors the empty set for each action, result in Successor-Fn(problem,State[node]) do s a new Node Parent-Node[s] node; Action[s] action; State[s] result Path-Cost[s] Path-Cost[node] + Step-Cost(State[node], action, result) Depth[s] Depth[node] + 1 add s to successors return successors d. Explain the mechanism of genetic algorithm.

4 e. Explain how transition model is used for sensing in vacuum cleaner problem. 1.Prediction stage is the same as for sensorless problems: given the action a in belief state b, the predicted belief state is ˆb = PREDICT(b, a) 2. Observation prediction stage determines the set of percepts o that could be observed in the predicted belief state: POSSIBLE PERCEPTS(ˆ b) = {o : o = PERCEPT(s) and s ˆb} 3. Update stage determines, for each possible percept, the belief state that would result from the percept. The new belief state bo is just the set of states in ˆb that could have produced the percept: bo = UPDATE(ˆ b, o) = {s : o = PERCEPT(s) and s ˆb} the updated belief state bo can be no larger than the predicted belief state ˆb -the belief states for the different possible percepts will be disjoint, forming a partition of the original predicted belief state (for deterministic sensing). f. Give the illustration of 8 queen problem using hill climbing algorithm. function Hill-Climbing( problem) returns a state that is a local maximum inputs: problem, a problem local variables: current, a node neighbor, a node current Make-Node(Initial-State[problem]) loop do neighbor a highest-valued successor of current if Value[neighbor] Value[current] then return State[current] current neighbor end gets stuck (86%, 3 steps), succeeds (14%, 4 steps) 100 consecutive sideways moves - 94% (21 steps for success, 64 for failure) Variants: stochastic hill climbing, first choice hill climbing 3. Attempt any three of the following: 15 a. Explain the working mechanism of min-max algorithm.

5 The minimax algorithm performs a complete depth-first exploration of the game tree. If the maximum depth of the tree is m and there are b legal moves at each point, then the time complexity of the minimax algorithm is 0(bm). The space complexity is 0(bm.) for an algorithm that generates all actions at once, or 0(m) for an algorithm that generates actions one at a time. choose move to position with highest minimax value = best achievable payoff against best play E.g., 2-ply game: function Minimax-Decision(state) returns an action inputs: state, current state in game return the a in Actions(state) maximizing Min-Value(Result(a, state)) function Max-Value(state) returns a utility value if Terminal-Test(state) then return Utility(state) v = for a, s in Successors(state) do v Max(v, Min-Value(s)) return v function Min-Value(state) returns a utility value if Terminal-Test(state) then return Utility(state) v = for a, s in Successors(state) do v = Min(v, Max-Value(s)) return v b. Explain in brief about resolution theorem. c. Write a note on Kriegspiel s Partially observable chess. In deterministic partially observable games, uncertainty about the state of the board arises entirely from lack of access to the choices made by the opponent. The rules of Kriegspiel are as follows: White and Black each see a board containing only their own pieces. A referee, who can see all the pieces, adjudicates the game and period-ically makes

6 announcements that are heard by both players. On his turn, White proposes to the referee any move that would be legal if there were no black pieces. If the move is in fact not legal (because of the black pieces), the referee announces 'illegal." In this case, White may keep proposing moves until a legal one is found and learns more about the location of Black's pieces in the process. Once a legal move is proposed, the referee announces one or more of the following: "Capture on square X" if there is a capture, and "Check by D" if the black king is in chock, where D is the direction of the check, and can be one of "Knight," "Rank," Tile," "Long diagonal," or "Short diagonal." (In case of discovered check, the ref-eree may make two "Check" announcements.) If Black is checkmated or stalemated, the referee says so; otherwise, it is Black's turn to move. d. Explain in brief about knowledge base agent. The central component of a knowledge-based agent is its knowledge base, or KB. function KB-Agent( percept) returns an action static: KB, a knowledge base t, a counter, initially 0, indicating time Tell(KB,Make-Percept-Sentence( percept, t)) action Ask(KB,Make-Action-Query(t)) Tell(KB,Make-Action-Sentence(action, t)) t t + 1 return action e. Explain the syntax for propositional logic. The proposition symbols P1, P2 etc are sentences If S is a sentence, S is a sentence (negation) If S1 and S2 are sentences, S1 S2 is a sentence (conjunction) If S1 and S2 are sentences, S1 S2 is a sentence (disjunction) If S1 and S2 are sentences, S1 S2 is a sentence (implication) If S1 and S2 are sentences, S1 S2 is a sentence (biconditional) f. Write a note on Wumpus world problem. The wumpus world is a cave consisting of rooms connected by passageways. Lurking somewhere in the cave is the terrible wumpus, a beast that eats anyone who entities its room. The wumpus can be shot by an agent, but the agent has only one arrow. The only mitigating feature of this bleak environment is the possibility of finding a heap of gold. Although the wumpus world is rather tame by modem computer game standards, it illustrates some important points about intelligence. The precise definition of the task environment is given: Performance measure: for climbing out of the cave with the gold, 1000 for falling into a pit or being eaten by the wumpus, 1 for each action taken and 10 for using up the arrow. The game ends either when the agent dies or when the agent climbs out of the cave. Environment: A 4 x 4 grid of rooms. The agent always starts in the square labeled [1,1], facing to the right. The locations of the gold and the wumpus are chosen ran-domly, with a uniform distribution, from the squares other than the start square. In addition, each square other than the start can he a pit, with probability 0.2. Actuators: The agent can move Forward, Turn Left by 90, or Turnftight by 90. The agent dies a miserable death if it enters a square containing a pit or a live wumpus. (it is safe, albeit smelly, to enter a square with a dead wumpus.) If an agent tries to move forward and bumps into a wall, then the agent does not move. The action Grab can be used to pick up the gold if it is in the same square as the agent. The action Shoot can be used to fire an arrow in a straight line in the direction the agent is facing. The arrow continues until it either hits (or hence kills) the wumpus or hits a wall. The agent has only one arrow, so only the first Shoot action has any effect. Finally_ the action Climb can be used to climb out of the cave, but only from square [1,1]. Sensors: The agent has five sensors. each of which gives a single bit of information:

7 In the square containing the wumpus and in the directly (not diagonally) adjacent squares, the agent will perceive a Stench. In the squares directly adjacent to a pit, the agent will perceive a Breeze. In the square where the gold is, the agent will perceive a Glitter. When an agent walks into a wall, it will perceive a Bump. When the wumpus is killed, it emits a woeful Scream that can be perceived any-where in the cave. 4. Attempt any three of the following: 15 a. What is first order logic? Discuss the different elements used in first order logic. First-order logic can also express facts about same or all of the objects in the universe. Whereas propositional logic assumes world contains facts, first-order logic (like natural language) assumes the world contains Objects: people, houses, numbers, theories, Klaus Iohannis, colors, base-ball games, wars, centuries. Relations: red, round, bogus, prime, multistoried, brother of, bigger than, inside, part of, has color, occurred after, owns, comes between, Functions: father of, best friend, third inning of, one more than, end of b. Explain universal and existential quantifier with suitable example. <variables> <sentence> Everyone in this room is smart: x At(x,D21) Smart(x) x P is true in a model m iff P is true with x being each possible object in the model. <variables> <sentence> Someone at Stanford is smart: x At(x, Stanford) Smart(x) x P is true in a model m iff P is true with x being some possible object in the model. c. Convert the following natural sentences into FOL form: i. Virat is cricketer. Virat(cricketer) ii. All batsman are cricketers. For-all(x): batsman(x) -> cricketer(x) iii. Everybody speaks some language For-all(x) Exist(y): Person(x) V language(y) -> speaks(x,y) iv. Every car has wheel. (forall (x) (if (Car x) (exists (y) wheel-of (x y))) v. Everybody loves somebody some time. (forall (x) (exists (y) -> loves-sometime(x y))). d. What is knowledge engineering? Write the steps for its execution. Knowledge engineer: investigates a particular domain, learns what concepts are important, creates a formal representation of the objects and relations in the domain Knowledge engineering steps 1. Identify the task. - list compentency questions that the KB will respond 2. Assemble the relevant knowledge. - knowledge acuisition 3. Decide on a vocabulary of predicates, functions, and constants translate the important domain-level concepts into logic-level names - ontology 4. Encode general knowledge about the domain - axioms for all vocabulary terms 5. Encode a description of the specific problem instance - atomic sentences about instances of concepts in the ontology

8 6. Pose queries to the inference procedure and get answers 7. Debug the knowledge base e. Give comparison between forward chaining and backward chaining. FORWARD CHAINING Forward chaining starts with some initial information and work forward, attempting to match that information with a rule. Once a fact has been matched to the IF part of the rule, the rule is fired. The action could produce new knowledge or a new fact that is stored in the knowledge base. This new fact may then be used to search out the next appropriate rule. This searching and matching process continues until a final conclusion rule is fired. BACKWARD CHAINING Backward chaining starts with a fact in the database, but this time it is the hypothesis. The rule interpreter then begins examining the THEN parts of rules for a match. The inference engine searches for an evidence to support the hypothesis originally stated. If a match is found, the database is updated recording the conditions or premises that the rule stated as necessary for supporting the matched conclusion. The chaining process continues with the system repeatedly attempting to match the right hand side of the rule against the current status of the system. The corresponding IF sides of the rules matched are used to generate new intermediate hypotheses or goal states which are recorded in the database. Again this backward chaining continues until the hypothesis is proved. f. Explain in brief about unification. Lified inference rules require finding substitutions that make different logical expressions look identical. This process is called unification and is a key component of all first-order inference algorithms. We can get the inference immediately if we can find a substitution θ such that King(x) and Greedy(x) match King(John) and Greedy(y) θ = {x/john, y/john} works Unify(α, β) = θ if αθ =βθ 5. Attempt any three of the following: 15 a. What is planning? Explain STRIPS operators with suitable example. Action: Buy(x) Have(x) At(p) Sells(p,x)

9 Buy(x) Precondition: At(p), Sells(p, x) Effect: Have(x) [Note: this abstracts away many important details!] Restricted language efficient algorithm Precondition: conjunction of positive literals Effect: conjunction of literals A complete set of STRIPS operators can be translated into a set of successor-state axioms b. Explain in brief about partially ordered plan. Partially ordered collection of steps with Start step has the initial state description as its effect Finish step has the goal description as its precondition causal links from outcome of one step to precondition of another temporal ordering between pairs of steps Open condition = precondition of a step not yet causally linked A plan is complete iff every precondition is achieved A precondition is achieved iff it is the effect of an earlier step and no possibly intervening step undoes it. c. Explain in brief about hierarchical planning.

10 d. Write a note on mutex relation. Mutex actions: Inconsistent effects: one action negates an effect of the other: Eat(Cake) and the persistence of Have(Cake) have inconsistent effects because they disagree on the effect Have(Cake). Interference: one of the effects of one action is the negation of a precondition of the other. For example Eat(Cake) interferes with the persistence of Have(Cake) by negating its precondition. Competing needs: one of the preconditions of one action is mutually exclusive with a precondition of the other, e.g., Bake(Cake) and Eat(Cake) are mutex - they compete on the value of the Have(Cake) precondition. Mutex literals: Negated literals: one is the negation of the other Inconsistent support: Have(Cake) and Eaten(Cake) are mutex in S1 because the only way of achieving Have(Cake), is mutex with the only way of achieving Eaten(Cake) e. What is semantic network? Show the semantic representation with suitable example. f. Write a note on Event calculus.

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