Artificial intelligence - computers
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bite-coursework-final-sd3012.doc
Module Code: |
SD3012 |
Module Name |
Artificial Intelligence |
Module Level |
3 |
Semester |
B |
Issue Date |
15 April 2014 |
Submission Date: |
8th May 2014 |
Weighting |
50% |
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Main Aims of the Module
I. Representation and reasoning paradigms used in AI in both theory and practice with careful attention to the underlying principles of logic, search, and probability.
II. Introduction to the underlying issues in cognitive emulation and to provide an opportunity for practical exercises in logic and probability.
Learning Outcomes for the Module
At the end of this Module students will be able to
· Demonstrate an understanding of search, logic based knowledge representation of issues in planning and learning.
· Be critically aware of the limitations of current symbolic AI paradigms.
· Develop and select appropriate search paradigms for advanced problems.
· Evaluate knowledge of Bayes' Rule and its use in Belief Networks and be able to solve problems concerning the updating of prior probabilities, and to construct belief networks for simple problems.
· Design and evaluate a simple agent system and associated ontology.
· Design, develop and implement a forward chaining knowledge based system including both ontology and rule based using a formalism such as CLIPS.
Assignment
To study intelligent systems and develop an algorithm for an intelligent system.
In this coursework you are required to study intelligent systems and develop an algorithm for an AI Agent based on forward chaining knowledge based system. You are required to identify a suitable ‘domain’ of your choice, acquire domain knowledge and convert the domain knowledge into a suitable rule structure. You should develop your algorithm by using appropriate knowledge representation and searching techniques used in the field of Artificial Intelligence. Your solution should demonstrate the application of Baye’s Network in establishing conditional dependency of the variables/parameters to handle uncertainties. The algorithm must be able to solve a given problem based on the facts and the rules stored in the working memory as a knowledge base for the given system. Evaluate the outcome of your algorithm by running at least two instances, formalized in CLIPS, through the system. The solution of the problem should be interpreted in the human language.
Project Report
Every student will be required to submit a report of 2000 words including the following information
I. A cover page with your name, your student ID, module title and code and the name of the project
II. Details of the work mentioned above.
III. As a conclusion, you will discuss the advantages and the drawbacks of your solution, and possible extensions for the application.
Note: No coursework would be accepted without the Turnitin report. (Maximum accepted level of similarity is 20%). The quotations referenced in the research should be taken from a range of authentic sources.
Generic assessment criteria
The best grades will be awarded for the student who will demonstrates a breadth and depth of substantive knowledge that is exceptional and informed by the highest level of scholarship. There will be evidence of excellent integration of the full range of appropriate principles, theories, evidences and techniques. The student should be able to demonstrate sound judgment within the given parameters and analyse the question critically.
The student should also demonstrate originality in the application of the knowledge and should use correct, relevant and preferably latest reference in support of their findings.
The report should be written in correct and fluent English language using recommended format with clear aims and objectives and a summary of the level of achievement of the objectives.
Note: The further a student’s work diverges from the ideal described above, the lower their resulting grade is likely to be.
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Detailed Marking Criteria |
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Learning Outcome |
70% + Marks
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60%-70% Marks
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50%-60% Marks |
35%-50% Marks
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Below 35% Marks
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Marks (Total 100) |
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· Full bibliography and appropriate referencing · Accurate and concise use of sources |
· Bibliography provided with omissions in referencing · Mostly accurate and concise use of sources |
· Satisfactory Bibliography with no major omissions in referencing · |
· A very limited bibliography and an inappropriate referencing · Limited references to reading; |
· Inadequate and incorrect referencing and bibliography · Poor or little referencing and irrelevant sources |
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· A wide range of recent, relevant and appropriate reading; · Strictly written in the recommended format and of an appropriate length; |
· A range of recent, relevant and appropriate reading but not as wide, recent or relevant as required to achieve all the learning outcomes. · Written in the recommended format and of an appropriate length with minor variations |
· Satisfactory level of reading of recent and relevant material · Overall written in standard format with appropriate style and length |
· Showing variance from the recommended format and may not follow appropriate length |
· Poor presentation, language mistakes and not in academic style · Not following the permissible length of the work |
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Demonstrate an understanding of search, logic based knowledge representation of issues in planning and learning.
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A complete overview of the topic with clear background of the aim, objectives and methodology. Excellent and a systematic understanding of key aspects of search, logic and knowledge representation. |
A complete overview of the topic with a good attempt to present background, aim and objectives of the study. A good understanding of key aspects of search, logic and knowledge representation |
Overview of the topic is presented but lacks in clarity in terms of the final outcome of the study and the methodology. Satisfactory understanding of key aspects of search, logic and knowledge representation |
Limited understanding of key aspects of search, logic and knowledge representation with unclear and inadequate overview of the topic |
Poor overview of the topic presented with little or no attempt to explain the aim, objectives and methodology of the study. Limited understanding of key aspects of search, logic and knowledge representation. |
25 |
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Be critically aware of the limitations of current symbolic AI paradigms
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Excellent ability to identify and comment upon AI paradigms with a full awareness of limitations of these paradigms |
A good ability to identify and comment upon the AI paradigms showing good awareness of limitations of these paradigms |
Satisfactory ability to identify and comment upon the AI paradigms showing good awareness of limitations of these paradigms |
Limited ability to describe and comment upon AI paradigms demonstrating a lack of awareness of limitations of these paradigms |
Poor or no ability to describe or comment upon AI paradigms |
7 |
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Develop and select appropriate search paradigms for advanced problems
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Identification of parameters and use of search techniques appropriate to develop the algorithm and determine its outcome. |
Some omissions in identification of parameters and use of search techniques appropriate to develop the algorithm and determine its outcome |
Some omissions in identification of parameters and use of search techniques resulting in some errors in the algorithm outcome |
Inaccuracies and major omissions in identification of parameters and use of search techniques resulting in some sizeable. |
Limited or no ability shown in the identification of parameters and use of search techniques appropriate to develop the algorithm and determine its outcome |
13 |
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Evaluate knowledge of Bayes' Rule and its use in Belief Networks and be able to solve problems concerning the updating of prior probabilities with and to construct belief networks for simple problems.
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Excellent integration of the full range of appropriate rules and methods to solve the problem
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A good integration of the full range of appropriate rules and methods to solve the problem |
Satisfactory integration of a range of appropriate rules and methods to solve the problem |
Limited integration of a range of appropriate rules and methods to solve the problem |
Fails to describe and integrate appropriate rules and methods to solve the problem.
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12 |
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Design and evaluate a simple agent system and associated ontology.
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Excellent understanding of different blocks and functioning of an intelligent system. Able to critically evaluate the system design. Correct logic, correct formation of the rules and completely functional algorithm.
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A good understanding of different blocks and functioning of an intelligent system. Some shortcomings in critical analysis of the system design Some inconsistencies in the logic and some mistakes in the rules.
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Satisfactory understanding of different blocks and functioning of an intelligent system Some shortcomings in the critical analysis of the system design. Some errors in the logic and rule formation.. |
Limited understanding of different blocks and functioning of an intelligent system Major shortcomings in the critical analysis of the system design. Major errors in the logic and rule formation. Algorithm not working as required. Algorithm not working as required |
Inadequate research and analysis of different blocks and functioning of an intelligent system. No or limited ability shown to develop a working algorithm.
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25 |
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Design, develop and implement a forward chaining knowledge based system including both ontology and rule based using a formalism such as CLIPS
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Excellent ability to employ accurately forward chaining rules in the development of the algorithm
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Good ability to employ forward chaining rules with minor inaccuracies |
Demonstrate satisfactory knowledge of forward chaining system but with a limited ability to employ it accurately |
Limited knowledge of forward chaining system and a limited ability to employ it accurately |
Limited knowledge of forward chaining system and no ability to employ the rules for practical purposes |
18 |
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Total |
100 |
BSc Technology & E-commerce
Coursework: Final Academic Year: 2013/2014
1
