# CMT117 Resit Assignment-Cardiff University UK.

Module Code: CMT117
Module Title: Knowledge Representation
Assessment Title: Resit Assessment
This assignment is worth 100% of the total marks available for this module. If coursework is submitted late (and where there are no extenuating circumstances):
1.If the assessment is submitted no later than 24 hours after the deadline, the mark for the assessment will be capped at the minimum pass mark;
2.If the assessment is submitted more than 24 hours after the deadline, a mark of 0 will be given for the assessment.
CMT117 Resit Assignment-Cardiff University UK.

Submission Instructions:
You are required to answer 4 multi-part questions. All submissions must be via learning central. Submit the following files in a single zip file,

Any deviation from the submission instructions above (including the number and types of files submitted) will lead to the marks being capped at 50%.

Your submissions will be checked for plagiarism. Your work must be your own and you must independently solve the problem and submit your own solution. Any other material or sources of information you use must be referenced. Code and text you submit will be compared with other submissions and various other sources on and off the Internet. Any substantial similarities of you submission to un referenced work or material not created by yourself will be subject to academic misconduct procedures. Marks will only be assigned for work you have done yourself (incl. finding and discussing material from references, but not the referenced work; there are no marks for code copied from elsewhere, but for either writing your own code or integrating and adapting code that you have not written).

Assignment
Answer all parts of the four questions below. Each question is worth 25 marks and the number of marks available for each question part is indicated.

CMT117 Resit Assignment-Cardiff University UK.

Question 1: Propositional Logic
a)Write down exactly one propositional contradiction such that the only propositional variable appearing in the sentence is p, and the only logical connectives are ¬ and . (Note your answer may include p and ¬ and as many times as you like).
b)Let L = {p, q} and let v be the valuation such that v(p) = T and v(q) = F. For each of the following sentences in SL, state whether v satisfies that sentence.

(c) Suppose we know the following facts about four people Michael, Jenny, James and Sarah:
– If Michael is rich, then either Jenny is not young or James is not tall
– If Jenny is young, then Sarah is hungry
– If Sarah is hungry and James is tall, then Michael is rich
– James is tall

(i) Using an appropriate choice of propositional variables, write each of these four facts as a sentence in propositional logic.

(ii) Can we deduce from these facts using propositional logic that Jenny is not young? Justify your answer.

(d) Let L = {s, t, u}. Determine whether there exists a derivation of u V t from s V u using the rules of Natural Deduction. Justify your answer.

(e) Describe one advantage and one disadvantage of using Horn Logic, rather than full propositional logic, as a language for Knowledge Representation. Your answer should use statements and results mentioned in the lecture notes to back up your claims.

CMT117 Resit Assignment-Cardiff University UK.

Question 2: Non monotonic Reasoning & Belief Revision

(i) Assume L = {p, q}. Show that Monotonicity fails for some rational consequence relation, and some specific choice of sentences A, B, C 2 SL. State clearly any Theorem from the module’s lecture notes that you rely on in your answer.

(ii) Show how Monotonicity can be derived from the set of rules given by the KLM rules plus Chain.

(each valuation is represented as a pair denoting the truth-values of p, q respectively,and the further to the left a valuation appears in the above table, the more normal it is deemed to be.) State whether the following conditionals hold in R. Justify your answers in each case.

(c) Switching now to belief revision, let L = {p, q, r} and let be the following plausibility order over the set of valuations:

(each valuation is represented as triple denoting the truth-values of p, q, r respectively, and the further to the left a valuation appears in the above table, the more plausible it is deemed to be.)

Question 3: First-Order Logic
1.Translate the sentences below from English into first-order logic. Use the signature S consisting of the binary predicate Talks, the binary predicate Listens and the constant symbol john.
(a) Not everybody talks to somebody.
(b) John listens to everybody who talks to somebody.
(c) John talks to everybody who does not listen to him.
(d) Nobody listens to anybody who does not listen to him.
(e) If everybody talks to somebody then nobody listens to anybody.

2.Translate into English the following sentences over the signature containing the unary predicate Man, the unary predicate Woman, the binary predicate Child Of, the binary predicate Knows and the function symbol father Of.

4.Describe, in up to 300 words, an application scenario in which using first-order logic as a formalism is better suited than propositional logic. Justify your answer.

The word limit is an upper limit, not a target length. Text longer than the word limit will be ignored. Your justification should be based on statements and results mentioned in the lecture notes to back up your claims. Note that based on does not mean copying from the lecture materials.

Question 4: Description Logics
1.Build a T Box capturing each of the following statements in a suitable concept inclusion using only the concept names Bicycle, Car, Device, Wheel, Human, Broken and the role names has Part, has and controls.
(a) Cars have between three and four wheels.
(b) Bicycles have exactly two wheels.
(c) A vehicle is controlled by exactly one human.
(d) A car with a broken part is broken.

2

Your justification should be based on statements and results mentioned in the lecture notes to back up your claims. Note that based on does not mean copying from the lecture materials.

Learning Outcomes Assessed
• Critically evaluate knowledge representation alternatives to solve a given task
• Formalize simple problems with a given knowledge representation approach
• Discuss the theoretical properties of different knowledge representation formalisms
• Explain the basic principles underlying common knowledge representation approaches
• Choose an appropriate knowledge representation approach to address the needs of a given application setting
• Compare how knowledge representation approaches influence the success of a given task
• Explain the nature, strengths and limitations of knowledge representation technique to an audience of non-specialists

Criteria for assessment
Credit will be awarded against the following criteria.