Lovely Professional University 2011 B.Tech Computer Science and Engineering Artificial intelligence and logic programming assignment 1 solution - Question Paper

Ans1- Intelligence- Intelligence is the computational part of the ability to achieve goals in the world. Varying types and degrees of intelligence occur in people, many animals and a few machines.
Artificial Intelligence- It is the science and engineering of making intelligent machines, especially intelligent computer programs.
? It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.
? AI is the study of how to make computers just like humans. That means how to make computers to do things that people do better.
Agent- 1 who acts for, or in the place of, another, by authority from him; 1 intrusted with the business of another; a substitute; a deputy; a factor. An intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.[3]
We have to judge every IS as a separate case. If they comply with the main functions and processes of the brain, then we can say that they really think. Example-Humanoid Robots
It is not enough to just write a computer program that is an intelligent system and runs within a computer with an output on a screen. We need to build complete systems that act in our human environment. It seems, that to be useful, such a program should have an intelligence somewhat like a human one. And for that it needs humanlike senses and limbs, so it can have similar experiences and create concepts for actions somewhat similar to our human ones.

Ans2- Artificial Intelligence is the use of programs to enable machines to perform tasks which humans perform using their intelligence whereas Common Sense is the beliefs or propositions that in their opinion they consider would in most people's experience, knowledge held by people "in common". Commonsense thinking is truly more complex than many of the intellectual accomplishments that attract more attention and respect, because the mental skills we call "expertise" often engage large amounts of knowledge but usually employ only a few kinds of representations. In contrast, common sense involves many types of representations and thus requires a larger range of various skills.
In other words, giving computers common sense is the opposite of the issue artificial intelligence researchers have traditionally faced. Rather than it being a issue of how to provide computers a great deal of knowledge about how to think about a few particular area such as chess playing or circuit design, to provide computers common sense we must program them with knowledge about many various areas: physical knowledge of how objects behave, social knowledge of how people interact, sensory knowledge of how things look and taste, psychological knowledge about the way people think, and more. Furthermore, every of these various facets of life requires its own specialized methods of reasoning about them. Giving computers common sense is not about figuring how to make a few particular method of reasoning work over a particular kind of knowledge, it is about how to make systems that are abundant with many kinds of knowledge and many ways of thinking about various things

Ans3- Yes, Reflex actions are intelligent. In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes and acts upon an environment (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it is rational). Intelligent agents may also learn or use knowledge to achieve their goals. They may be very simple or very complex: a reflex machine such as a thermostat is an intelligent agent, as is a human being, as is a community of human beings working together towards a goal.
If an agent decides upon and executes an action in response to a sensor input without consultation of its world, then this can be considered a reflex response. Humans flinch if they touch something very hot, regardless of the particular social situation they are in, and this is clearly a reflex action. Similarly, chess agents are programmed with lookup tables for openings and endings, so that they do not have to do any processing to select the accurate move, they simply look it up. In timed chess matches, this type of reflex action might save vital seconds to be used in more difficult situations later.
Ans4- 8-puzzle problem-
For now - we just want to establish a few ordering to the possible moves (the values of our heuristic does not matter as long as it ranks the moves).
• Later - we will worry about the true values returned by the heuristic function.
Number of tiles in the accurate position
– The higher the number the better.
– Easy to calculate (fast and takes little memory).
– Probably the simplest possible heuristic.
a different approach
• Number of tiles in the incorrect position.
– This can also be considered a lower bound on the number of moves from a solution!
– The "best" move is the 1 with the least number returned by the heuristic.
– Is this heuristic more than a heuristic (is it always correct?).
• provided any two states, does it always order them properly with respect to the minimum number of moves away from a solution?




Before beginning to tell how to reach from the initial state to the goal state, we have to solve a sub issue which is "choosing the goal state to be reached". As it's mentioned in the eight puzzle issue page, the game has 2 possible arrangements. We have to select 1 of the goal states to be reached because only 1 of them is reachable from the provided initial state. Sounds interesting? Lets see why the 8-puzzle states are divided into 2 disjoint sets, such that no state in 1 set can be transformed into a state in the other set by any number of moves.



Goal State A Goal State B
First we start with the definition of the order of counting from the upper left corner to the lower right corner as shown in the figure beneath.


Order of Counting
This definition is provided because we need to determine the number of smaller digits which are coming after a chosen tile. Its a little bit trick to tell with words. So lets have an example.


Counting Example 1
In this example above we see the tiles which comes right after tile #8 and smaller than 8, in yellow. So if we count the yellow tiles, we get 6. We will apply this counting for every tile in the board. But 1st lets have a different example to make things crystal clear.


Counting Example 2
This time we count the tiles which comes right after tile #6 and smaller than 6, in yellow. As a outcome we get 2. Now we made things clear on how to count. Now we will do this counting for every tile in the board.


Counting the Board
In the figure beneath you see that counting for tile #1 is skipped. That's because the outcome is always 0 (1 is the smallest tile). Counting for every tile has been done and then the outcomes are summed. Finally we get 11 as the outcome.
Now I believe that most of you have the ques. "So, what?". The ans is simple. As you can imagine the outcome is always either even or odd. And this will be the key to solve the issue of "choosing the goal state to be reached". Lets call the outcome as N.
If N is odd we can only reach to the Goal State A as shown beneath. If N is even we can only reach to the Goal State B as shown beneath.

Of course this is not an arbitrary selection. There is a logic behind it and here is the proof. First, sliding the blank along a row does not change the row number and not the internal order of the tiles, i.e. N (and thus also Nmod2) is conserved. Second, sliding the blank ranging from rows does not change Nmod2 either. You can try these things on paper and understand the idea behind it more clearly.
Now we have determined which goal state to reach, so we can begin a search. The search will be an uninformed search which means searching for the goal without knowing in which direction it is. We have three options for the search algorithm in this case. These are:
• Breadth-first search algorithm
• Depth-first search algorithm
• Iterative deepening search algorithm

When we chose any of this algorithms to apply, we will begin with the initial state as the beginning node and search the possible movements of the blank. every time we move the blank to a different position (create a new board a arrangement) we create a new node. If the node we have is the identical as goal state then we finish the search.
The 3 algorithms provided above differs on the option of the search path (node to node). beneath you will obtain summarized descriptions
Breadth-first search algorithm: obtains the solution that is nearest (in the graph) to the begin node that means it always expands the shallowest node
Depth-first search algorithm: begins at the root (selecting a few node as the root in the graph case) and explores as far as possible along every branch before backtracking.

Ans5- An automated travel planning system (10) is given. The system (10) includes a database (12) which stores info relating to every individual traveler and business entity customer of a travel agency and also info relating to the travel agency itself. This info may include, for example, data concerning the frequent flyer/renter programs in which a traveler participates, smoking and seating preferences for a traveler, preferred travel vendors of a business entity, restrictions on fare classes (e.g., business or coach classes only) imposed by a business entity on its employees, and promotions available to a travel agency. When travel request info is received from a specific customer (individual and/or business entity) of the travel agency, the system automatically retrieves info relating to the customer from the database and also info relating to the travel agency. The system (10) preferably uses the retrieved info to determine a travel plan that is satisfactory to the individual customer who is traveling, the business traveler customer which employs the individual, and the travel agency.

Ans6- Yes, an expert system be a suitable technique for implementing an automated travel agent. A system, usually built using a set of rules, that uses expert knowledge to solve issues and discuss phenomenon such as symptoms is the expert system. Rules and charts are used to represent the logic of expert system.
The subsequent general points about expert systems and their architecture have been outlined:
1. The sequence of steps taken to reach a conclusion is dynamically synthesized with every new case. The sequence is not explicitly programmed at the time that the system is built.
2. Expert systems can process multiple values for any issue parameter. This permits more than 1 line of reasoning to be pursued and the outcomes of incomplete (not fully determined) reasoning to be presented.
3. issue solving is accomplished by applying specific knowledge rather than specific technique. This is a key idea in expert systems technology. It reflects the belief that human experts do not process their knowledge differently from others, but they do possess various knowledge. With this philosophy, when 1 obtains that their expert system does not produce the desired results, work starts to expand the knowledge base, not to re-program the procedures.

Earning:   Approval pending.