Test-1 Solution Date: 25-02-2015 Marks: 50 Subject & Code: Artificial Intelligence and Agent technology (14SCS24) Name of faculty: Dr.Srikanta Murthy. K Sec: II Sem M.Tech Time: 8.30 to 10.00AM Q1). What is AI? Explain the term rationality with respect to AI Ans. AI is the study of how to make computers do things which, at the moment, people do better. But it provides a good outline of what constitutes artificial intelligence and it avoids the philosophical issues that dominate attempts to define the meaning of either artificial or intelligence. A System is rational if it performs Right Things based on the information provided to it. Thought processes & reasoning Human performance Thinking humanly Acting humanly Thinking rationally Acting rationally Ideal concept of intelligence Behavior Thinking Humanly: (The cognitive modeling approach) Most of the time it is a black box where we are not clear about our thought process. One has to know functioning of brain and its mechanism for possessing information. It is an area of cognitive science.
The stimuli are converted into mental representation. Cognitive processes manipulate representation to build new representations that are used to generate actions. Acting Humanly: (The Turing test approach) The overall behavior of the system should be human like. It could be achieved by observation. The Turing test proposed by Alan Turing was designed to provide a satisfactory optimal or operational definition of intelligence. Natural language processing. Knowledge representation. Automated reasoning. Machine learning. Thinking Rationally: (The laws of thought approach) Such systems rely on logic rather than human to measure correctness. For thinking rationally or logically, logic formulas and theories are used for synthesizing outcomes. For example, given John is a human and all humans are mortal then one can conclude logically that John is mortal Not all intelligent behaviors are mediated by logical deliberation. Acting Humanly: (The rational agent approach) An agent is just something that acts. Computer agents are not just programs. Computer agents acts(or)operates under autonomous control, perceive from their environment, adapts to change etc., Rational agents that acts to achive the best outcome.
Making correct inferences is a part of being a rational agent. A way to act rationally is to reason logically. Q2). Describe a well-defined problem? Explain the same for 8 queen problem Ans. A well-defined problem can be described by: Initial state: that the agent starts in. Operator or successor function: for any state x returns s(x), the set of states reachable from x with one action. State space: all states reachable from initial by any sequence of actions. Path: sequence through state space. Path cost: function that assigns a cost to a path. Cost of a path is the sum of costs of individual actions along the path. Goal test: test to determine if at goal state. The step cost: of taking action a to go from state x to state y is denoted by c(x,a,y). Solution to the problem is a path from the initial state to a goal state. Optimal solution has the lowest path cost among all solutions. The goal of 8-queens problem is to place 8-queens on the chessboard such that no queen attacks any other. (Queen moves Row or Column or Diagonal)
Fig.1 In the above Fig.1 8-Queens are on the board and none attacked. We can say this as a Goal State or Final State. There are many possible ways of arranging 8-Queens, Fig.1 is one among them. An Incremental formulation involves for 8-queens problem, each action adds a queen to the state. A complete-state formulation starts with all 8 queens on the board and moves them around. In either case the path cost is of no interest because only the final state counts. States: Any arrangement of 0 to 8 queens on board is a state. Initial state: No queen on the board. Successor function: Add a queen to any empty square. Goal Test: 8 queens are on the board, none attacked.
Q3). What are problem characteristics? Explain the DFS and BFS Ans. The problem characteristics are: 1. Is the problem decomposable into a set of nearly independent smaller or easier sub problems? 2. Can the solution steps be ignored or at least undone if they prove unwise? 3. Is the problem's universe predictable? 4. Is a good solution to the problem obvious without comparison to all other possible solutions? 5. Is the desired solution a state of the world or a path to a state? 6. Is a large amount of knowledge absolutely required to solve this problem or is knowledge important only to constrain the search? 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? ALGORITHM: BREADTH-FIRST SEARCH 1. Create a variable called NODE-LIST and set it to the initial state. 2. Loop until the goal state is found or NODE-LIST is empty. a. Remove the first element, say E, from the NODE-LIST. If NODE- LIST was empty then 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 the goal state, quit and return this state. iii) Otherwise add this state to the end of NODE-LIST ADVANTAGES OF BREADTH-FIRST SEARCH 1. Breadth first search will never get trapped exploring the useless path forever. 2. If there is a solution, BFS will definitely find it out. 3. If there is more than one solution then BFS can find the minimal one that requires less number of steps. DISADVANTAGES OF BREADTH-FIRST SEARCH 1. The main drawback of Breadth first search is its memory requirement. Since each level of the tree must be saved in order to generate the next level, and the amount
of memory is proportional to the number of nodes stored, the space complexity of BFS is O(b d ). As a result, BFS is severely space-bound in practice so will exhaust the memory available on typical computers in a matter of minutes. 2. If the solution is farther away from the root, breath first search will consume lot of time. ALGORITHM: DEPTH FIRST SEARCH 1. If the initial state is a goal state, quit and return success. 2. Otherwise, loop until success or failure is signaled. a. Generate a state, say E, and let it be the successor of the initial state. If there is no successor, signal failure. b. Call Depth-First Search with E as the initial state. c. If success is returned, signal success. Otherwise continue in this loop. ADVANTAGES OF DEPTH-FIRST SEARCH The advantage of depth-first Search is that memory requirement is only linear with respect to the search graph. This is in contrast with breadth-first search which requires more space. The reason is that the algorithm only needs to store a stack of nodes on the path from the root to the current node. The time complexity of a depth-first Search to depth d is O(b^d) since it generates the same set of nodes as breadth-first search, but simply in a different order. Thus practically depth-first search is time-limited rather than space-limited. If depth-first search finds solution without exploring much in a path then the time and space it takes will be very less. DISADVANTAGES OF DEPTH-FIRST SEARCH The disadvantage of Depth-First Search is that there is a possibility that it may go down the left-most path forever. Even a finite graph can generate an infinite tree. Depth-First Search is not guaranteed to find the solution. And there is no guarantee to find a minimal solution, if more than one solution exists.
Q4). Explain production systems. Write the production rules for water-jug problem Ans. Knowledge representation formalism consists of collections of condition-action rules (Production Rules or Operators), a database which is modified in accordance with the rules, and a Production System Interpreter which controls the operation of the rules i.e., the 'control mechanism' of a Production System, determining the order in which Production Rules are fired. A system that uses this form of knowledge representation is called a production system. Components of an AI production system are: 1. A set of production rules each consisting of a left side the applicability of the rule and the right side the operations to be performed; 2. One or more knowledge bases containing the required information for each task; 3. A control strategy that specifies the order in which the rules will be compared to the database and ways of resolving conflict; 4. Rule applier Control Strategy or Control Structure. Production Rules for water-jug: S.No Condition Action Explanation 1 (x, y) If x<4 2 (x, y) If y<3 3 (x, y) If x>0 4 (x, y) If y>0 5 (x, y) If x>0 (4, y) (x, 3) (x-d, y) (x, y-d) (0, y) Fill the 4-gallon jug. Fill the 3-gallon jug. Pour some water out of the 4-gallon jug Pour some water out of the 3-gallon jug Empty the 4-gallon jug on the ground
6 (x, y) If y>0 7 (x, y) If x+y 4 & y>0 8 (x, y) If x+y 3 & x>0 (x, 0) (4, y-(4-x)) (x-(3-y), 3) Empty the 3-gallon jug on the ground Pour water from the 3-gallon jug into the 4-gallon jug until the 4-gallon jug is full Pour water from the 4-gallon jug into the 3-gallon jug until the 3-gallon jug is full Pour all the water from the 3-gallon jug 9 (x, y) (x+y, 0) If x+y 4 & y>0 into the 4-gallon jug 10 (x, y) (0, x+y) Pour all the water from the 4-gallon jug If x+y 3 & x>0 into the 3-gallon jug 11 (0, 2) (2, 0) Pour the 2-gallons from the 3-gallon jug into the 4-gallon jug 12 (2, y) (0, y) Empty the 2-gallons in the 4-gallon jug on the ground Q5). What is an Agent? What does Task Environment means? Give PEAS description for an automated taxi. Ans. "An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors." Agent Sensors Environment? Actuators
Example: A human agent has eyes, ears, and other organs for sensors and hands, legs, mouth, and other body parts for actuators. A robotic agent might have cameras and infrared range finders for sensors and various motors for actuators. A software agent receives keystrokes, file contents, and network packets as sensory inputs and acts on the environment by displaying on the screen, writing files, and sending network packets. Vacuum-Cleaner World This world has two locations only A & B. The vacuum agent perceives where it is( in A or B) and whether there is dirt or not. Percepts: location and contents, e.g., [A,Dirty] Actions: Left, Right, Suck, NoOp. Simplest agent function: if current location( say A) is dirty then suck else move to location B In the simple vacuum-cleaner agent, we had to specify the performance measure, the environment, and the agent s actuators and sensors. By grouping these we can call as Task Environment. Task Environments are the problems and rational agents are solution to it. Properties of task environment: Fully observable vs. partially observable Deterministic vs. stochastic Episodic vs. sequential Static vs. dynamic
Discrete vs. continuous Single agent vs. multi-agent PEAS description for automated taxi: Agent Type Taxi driver Performance Measure Safe, fast, legal, comfortable trip, maximize profits Environment Actuators Sensors Roads, other traffic, pedestrians, customers Steering, accelerator, break, signal, horn, display Cameras, sonar, speedometer, GPS, odometer, accelerometer, engine, sensors, keyboard