The main aim of incorporating AI into the Computer Science undergraduate curriculum is to provide students who are majoring in CS with an introduction to each of the sub fields of AI. A course in artificial intelligence is usually in the final year of study, i. e. the third year of a three year degree or the fourth year of a four year agree. This serves as a foundation for students who will continue with further studies in CS and AI, while at the same time arming students, who will move on to industry, with alternative methodologies that can be used to solve problems in various application domains. . 2 AI in the Instruction of Computer Science Courses Artificial intelligence plays an important role in the instruction of Computer Science courses. According to Kumar , AI courses are often used as a means of enabling students to develop advanced programming skills. Programming AI systems require students to draw on their knowledge of programming and data structures from the previous two years of their undergraduate degree, and optimize the amount of memory used while at the same time minimizing processing time when programming. [pic] 3 Characteristics of the AI Approach
This section discusses the AI approach in terms of four key values of AI: ambitious goals, introspective plausibility, computational elegance, and wide significance. These remarks apply specifically to classical AI in its pure form. 3. 1 Ambitious Goals and Bold Leaps AI speech research, like AI research more generally, is motivated not by what can be achieved soon, but by a long-term vision. For speech, this has been the idea of a system able to produce an optimal interpretation based on exhaustive processing of the input. Engineers prefer to set goals towards which progress can be measured.
AI speech research tends, since existing systems are nowhere near optimal, to seek breakthroughs and radically new perspectives. 3. 2 Introspective Plausibility AI speech research, like AI more generally, often makes recourse to introspection about human abilities. This subsection illustrates the use of introspection at four phases:
• When setting long-term goals
• When setting short-term goals
• For design
• When debugging. 3. 3 Computational Elegance AI speech research, like AI research more generally, postulates that knowledge is good and that more knowledge is better.
In order to bring more knowledge to bear on specific decisions, integration of knowledge sources is considered essential. For speech, this means, most typically, wanting to use the full inventory of higher-level knowledge, including knowledge of syntax, semantics, domain, task and current dialog state, at the earliest stages of recognition and understanding. 4. CONCLUSION It is evident from this paper that Artificial Intelligence plays a significant role in both the teaching of Computer Science and Computer Science research, and will continue to do so in the future.
Artificial intelligence forms a crucial component of both the Computer Science undergraduate and postgraduate curriculum and as such serves as a foundation for students wishing to pursue research in this domain at the Masters level. In the area of teaching AI, there is a lot of scope for research into the identification of learning difficulties experienced by students and the identification of instructional strategies to improve the teaching of AI. 5. REFERENCES  Computer Science Curriculum 2008: An Interim revision of CS 2001. ACM, IEEE Computer Society, ttp://www. acm. org/education/curricula/ComputerScience20 08. pdf.  Merzbacher, M. 2001. Open Artificial Intelligence – One Course for All. SIGCSE Bulletin-inroads, SIGCSE ’01 Proceedings, 33, 1 (March 2001), 110-113, ACM.  Congdon, C. , B. 2001. Machine Learning for the Masses. Intelligence: New Visions of AI in Practice, 12, 2 (Summer 2001), 15-16, ACM.  Lankewiz, L. , B. 2001. Undergraduate Research in Genetic Algorithms. SIGCSE Bulletin-inroads, SIGCSE ’01 Proceedings, 33, 1 (March 2001), 114-118, ACM.  Kumar, D. 2000. How Much Programming? What Kind?
Intelligence: New Visions of AI in Practice, 11, 4 (Winter 2000), 15-16, ACM. Alshawi, Hiyan & David Carter (1994). Training and Scaling Preference Functions for Disambiguation. Computational Linguistics, 20:635–448. Baggia, Paolo & Claudio Rullent (1993). Partial Parsing as a Robust Parsing Strategy. In 1993 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. II–123–126. Brooks, Rodney A. (1991a). Intelligence Without Reason. In Proceedings of the Twelfth International Joint Conference on Artificial Intelligence. ———————– [pic]