CSC563 Artificial Intelligence Programming Paradigms Assignment Malaysia
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Artificial Intelligence Programming aims at creating programs that behave like human beings; logic programming includes four stages – declarative (declarations), procedural(procedures) analyzed using advanced meta-programming techniques, and finally comes up with a series of test scenarios for each program in a language called Prolog.
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The use of task lists helps programmers to debug complex programs through the use of decision tables, which are required in general programming when there is a need to apply more than one rule or criteria simultaneously.. Although many aspects of artificial intelligence have been put into practice already, problems such as congestion and pollution still exist. The most important part of any programming language is the compiler that transforms high-level languages into low-level machine-specific language for execution on a specific processor or computer, which makes them fast and efficient.
Compression is a process by which data or information is reduced in size. Compression can be used to reduce the size of files, such as video, music, and text files. In most cases, decompressing is required to restore the original data from its compressed form.
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At the end of the course, students should be able to:
- Describe and differentiate four problem-solving paradigms of Logic Programming.
- Explain and apply logic programming techniques to handle AI Computer problems.
- Trace and develop programs using AI programming language.
Various other services related to CSC563 are:-
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Assignment Brief 1: Describe and differentiate four problem-solving paradigms of Logic Programming.
1) Deduction – Consists of finding a key that will, with certainty, open the lock. For example: if A is greater than B and B is less than C then A must be greater than C
2) Heuristic Search – The use of a plausible procedure to produce reasonable approximations without necessarily guaranteeing an answer. Example: approximation or non-exact measurement
3) Progression – The analysis from one phase to the next phase by exhaustive correlation
4) Inversion – Reversing the order of evaluation in multiple stages.
Logic Programming is an approach to programming which relies on propositions as data objects and pieces of reasoning as its fundamental operation. Rather than using mathematical formulas, it uses sentences expressed in ordinary language which are true or false. It was developed to support a variety of knowledge-based systems and artificial intelligence applications, where the program is a network that consists of facts and rules.
The Knowledge Base consists of facts represented as clauses in first-order predicate logic language mixed with natural language terms.
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Assignment Brief 2: Explain and apply logic programming techniques to handle AI Computer problems.
Logic programming is a field of computer science that deals with rule-based inference. This, in essence, means the conversion of logical statements into operations on abstract objects called “facts” and “rules.” Logic programs are able to find conclusions without any sort of hypothesis testing.
For example, let’s say we want to determine which animal is faster: a zebra or giraffe. Both can run at 40 km/h (about 25 mph), but when they vary speed the zebra slows down earlier than the giraffe; therefore it will be slower overall in a race where they start and end at 100 km/h (about 60 mph). We would express this in logic like: Speed(Zebra) is less than Speed(Giraffe) . Then we would call the program, and it would tell us that the zebra is faster.
One convenient facet of logic programming is that you can easily experiment with different ways to express things in its language (Lisp), check whether your program works, and change it accordingly until you get something correct. At this point, you can save your program as a file, which you can then execute whenever you want.
Assignment Brief 3: Trace and develop a program using AI programming language.
Designing and developing a computer program may be one of the most difficult tasks for software engineers, especially if working in an unfamiliar programming language. The process usually starts with designing the algorithm and moving on to implementation and debugging before release to production.
What’s more challenging is that design and implementation require different strong backgrounds in math, physics, logic, and algorithms. This means depending on your programming language, you might need to learn new concepts before creating or modifying code. In addition to this mental burden on developers, switching between programming languages is time-consuming as each takes some getting used to due to their different syntaxes.
It is difficult to trace and present a specific program in AI programming language due to the vast number of algorithms.
The overarching goal is inference, which can require forward reasoning, optimization, or control among other things. AI systems are typically broken into three major groups based on what approach is used when working with them: symbolic AI (based on using symbols), statistical AI (using statistics), and analogical AI (analogy).
Using the AI programming language of machine learning, a trace and develop program can be developed to theoretically access all of the machine’s “life cycle” once activated. This process starts by mapping out the machine lifecycle objectives. In order for this type of programming to work, you need to know what is going on from start to finish. The next step mirrors that requirement which includes assembling sensors and actuators in various places and for various purposes so they can detect and react appropriately with its surrounding environment throughout its life cycle.
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