Artificial Intelligence vs Traditional Computing - Assignment Example

Artificial Intelligence Table of Contents Table of Contents 2 Task 3 3. Knowledge Representation 5 4. Machine Learning 5 5. AI Future Directions – Research and Applications 6 Part 2 8 Task 2 8 1. Algorithm Elaboration 8 2. Using suitable programming language, program the algorithm given in part 2. Show the result of your program using example used in part 2 (or other suitable example). 10 Task 3. 15 1. Depth First Search Algorithm 15 2. Elaboration of the psudocode algorithm 16 References 19 Task 1 1. AI vs. Traditional Computing Artificial Intelligence (AI) can be describes as the brainpower, which is demonstrate by the software and machines. AI is a topic, which is specially used in the academic field for study in order to have a better understanding about the operations that can be performed by machines. This study has been mainly developing for the intelligence agents for their relative activities. AI is efficient in building a “never-ending thought”, which is collectively solves the problems by thinking every potential solution. The use of AI in any particular situation is to encompass huge data than the human can handle. AI can easily recognise the nature of image by doing rigorous low-level circulation on image. AI helps to apply the image recognition system in a repetitive biological research. AI is mainly regarded as a machine, which works and responses like a human. AI includes several processes such as learning, planning, speech recognition and problem solving (University of Toronto, n.d.). Traditional computing is a human-assisted computation in the field of computer science. Traditional computing is a process of outsourcing by the certain methods applied by the humans. Traditional computing is very costly in nature. In traditional computing, the process is followed with the inputs provided by a human. After the permission of a human, the computer will interpret and determine the data and accordingly, prepare as well as provide the solution. The mainframe computer is example of the traditional computer (University of Toronto, n.d.). In this regard, it can be identified that AI and traditional computing differs on the procedure of conduction computing services. 2. Rule Based & Expert Systems The rule based system is mainly used in AI, as an application of research. The rule based expert system is the part of computer science, which is used to store and operate the information. There are several features of the ruled based expert system that include user interface, explanation facility, agenda, working memory, interference engine and knowledge acquisition facility among others. The user interface system is mainly used at the time of designing the system. Explanation facility describes the user about the cognitive process of the system. Working memory is the database, which is used for storing and collection of data to be used for designing as well as implementing the expert system (Artificial Intelligence, 2010). The rule based expert system is modular in nature. This helps in summarizing the knowledge and assists in the growth of the expert system in a very informal way. The system also helps to build an explanation facility by processing the intellectual data. The explanation facility helps to build up a chain of cognitive data, which helps to interpret conclusion of the work. The interference engine will provide the knowledge regarding the importance of the rules as per the association towards work and explains the rules as per the priorities. The list that has been prepared by the interference engine is also recognised as agenda. The list of rules that has been prepared should satisfy with the facts of working memory. Conflict is also identified to arise at the time of data processing related to functioning. This conflict can be removed by applying approaches resembling first come first executes and by assigning default priorities (Artificial Intelligence, 2010). 3. Knowledge Representation Knowledge representation is part of the AI. The information about any particular field, which is helpful to solve the problems related to this area is known as knowledge. In addition, knowledge is also required for resolving the problems. In this context, the required knowledge is needed to be presented in a system for appropriate functioning. To express the process of designing a database for solving the various problems, it is mandatory to describe the process of knowledge representation. An agent uses the knowledge as a form of representation scheme. Various forms of knowledge can be identified by the knowledge representation and it can be referred as an internal representation of knowledge. The representation of knowledge, which is stored by an agent, is also identified as knowledge base. The representation scheme should be wider for expressing the knowledge, which will be needed to solve the problems. In this context, knowledge representation should be more compact, natural and supportable in nature for solving the problems. To check the accuracy of the knowledge representation, the relationship between representation and domain should be represented. Representation schemes design for particular purposes and these representation schemes can be expandable as per the requirement of the purpose. The representation schemes have been designed for study but it can be applicable for providing solution of numeral problems. Knowledge representation is efficient in describing the features of the problems and it is also able to give solution according to the problems (Poole & Mackworth, 2010). 4. Machine Learning Machine learning is the knowledge about computer science. It is the subfield of statistics and computer science. Rather than being the subfield of computer science, the machine learning is the part of the AI and optimization. Machine learning is mainly used for developing the computer programs. Machine learning is similar to data mining process. Machine learning is ascertained as useful in nature. By the technique of machine learning, the self-driving cars are developed. Machine learning is useful in accepting human genome and effective web search. Machine learning can be used in the recognition of the practical speech. It has been also recognised to be effective in the progress of AI in human level. Machine learning has been used for extracting and improving the data for better understanding. The function of machine learning is to identify the patterns of the data and adjust it as per the software design activity. The main purpose of machine learning is to make the systems more intelligent in nature by continuous advancement in the programming system. As data mining and machine learning belong to similar methodology, it can create confusion relating to the important of machine learning. Machine learning is determined as an effective procedure with the assistance of which machines are able to learn from the data inputted for performing similar tasks without instruction. Machine learning algorithm is also identified to be cheaper than manual programming as systems equipped with machine learning are able to perform operations efficiently by learning programs automatically. Machine learning is more comfortable in usage of data for handling problems as compared to manual programming (Domingos, n.d.). 5. AI Future Directions – Research and Applications AI is the area of study where experiments are conducted for developing computers or machines with human thinking capabilities. In this regard, different approaches that include classical approach and connectionist approach are conducted for having a better understanding of AI future directions. The classical approach is based on designing AI or symbolic reasoning. According to the classical approach, the idea and concept of the AI can be represented. A connectionist approach is mainly conducted for developing AI by the application of the genetic algorithm and neurons work procedures. According to the article published by the University of Toronto (n.d.), it can be determined that within 10 years, the level of AI will reach to human levels. AI will be able to communicate with human through voice. The future approaches of AI are identified to be used for developing some parts of human or animal brain in silicon. AI is supposed to work with the application of human intelligence in the machines. It can be expected that the robots will take the places of humans in the work field in near future. The improvement in AI will be proved very important factor for the imposition of machines, which is having human intelligence (University of Toronto, n.d.). Part 2 Task 2 1. Algorithm Elaboration Function depth_first_search; begin open:=[Start]; closed := [ ]; while open ≠ [ ] do begin remove leftmost state from open, call it X; if X is a goal then return SUCCESS else begin generate children of X; put X on closed; discard remaining children of X if already on open or closed put remaining children on left end of open end end; return FAIL end. The hypothetical graph related to this provided algorithm has been elaborated as under in a detailed manner: The functionality of the DFS Algorithm can be elaborated in a manner that it primarily scans all the child nodes of a node rather than scanning all the associated siblings. Taking this factor into consideration, the elaboration associated with this algorithm proceeds as follows. Here (A, B, C, D, E, F, G) represents all the states / nodes in a tree like fashion. Thus starting from A the algorithm will reach one of the extreme leaf nodes and then revert back for scanning the siblings. The sequential scanning has been elaborated within the graph by dotted lines in following color red (A, B, E, K, and S) blue (L and T) green (F and M) pink (C, G and N) orange (H and O) violet (P and U) navy blue (D, I and Q) and black (J and R). Depending on the graph, the values of the sequential iteration associated with the provided algorithm is as follows. Iteration 1.open = [A] and closed = [ ] Iteration 2.open = [B, C, D] and closed = [A] Iteration 3.open = [E, F, C, D] and closed = [B, A] Iteration 4.open = [K, L, F, C, D] and closed = [E, B, A] Iteration 5.open = [S, L, F, C, D] and closed = [K, E, B, A] Iteration 6.open = [L, F, C, D] and closed = [S, K, E, B, A] Iteration 7.open = [T, F, C, D] and closed = [L, S, K, E, B, A] 2. Using suitable programming language, program the algorithm given in part 2. Show the result of your program using example used in part 2 (or other suitable example). Considering C as the appropriate language the provided code has been designed to initiate a DFS algorithm. #include #include #include char stack [40]; char array [40]; int up=-1; int node_count; char depth_first_search (int); char d [40]; char Mat2 [40][40]; void display (); int pa=0; int main () { char v1; int L1=0; printf ("please input the total number of graph node count"); scanf ("%d",&n); printf ("Please enter the value of each node within the graph"); for (int i=0; i< node_count; i++) { scanf ("%s",&d [i]); } char k1=d [0]; printf ("Enter value in the adjacency matrix as Y or N\n"); printf ("\n Enter ‘Y’ if there is an edge between the two vertices or else enter N\n"); for (int i=0; i< node_count; i++) printf (" %c ",d [i]); for (int i=0;i< node_count; i++) { printf ("\n%c ",d [i]); for (int j=0; j< node_count; j++) { printf ("%c ", v1=getch()); Mat2[i][j]=v1; } printf ("\n\n"); } for (int i=0;i< node_count;i++) { L1=0; while (k1! =d [L1]) L1++; k1= depth_first_search (L1); } display (); getch (); } void display() { printf (" Depth First Search of Graph: "); for (int i=0; i< node_count; i++) printf ("%c ",array[i]); } void push (char value) { up=up+1; stack [up] =value; } char popup () { return stack[up]; } bool unVisit (char value2) { for (int i=0; i Read More