please see PDF, no hard coding allowed
Microsoft Word - Exercise 2.docx 1 Changelog Evidence first constructor: Use byte instead of String for the type of the parameter “type”): public Evidence (String description, byte type, byte unambiguity, byte credibility, byte completeness, byte conclusiveness) In the Evidence and Hypothesis classes, the method computeProbability signature is: public byte computeProbability() The probability displayed in the printFullDescription() example is 3 instead of 5. Use type casting from float to byte when computing the probability using the 4 factors (unambiguity, credibility, completeness, conclusiveness). Examples of tests provided at the end of the document. Other minor typos fixed. See text in red. Description Now that we've seen the syntax for creating our own classes, in this exercise we'll practice creating some simple classes with associated fields and methods. Let's assume we're developing a simple evidence-based reasoning program. You will need to model Problems, Hypotheses, Relevance, items of Evidence, and implement a simple program to combine those elements to make logical Reasoning. There is no template provided for this exercise, so make sure that you read the instructions carefully to determine how your code should be structured. Briefly, you will be making five classes in their respective .java files. Remember: source files in java should be named exactly the same as the class they represent and have the .java extension. Make sure to include all the fields and methods asked for, paying particular attention to the access modifiers, return types, capitalization, and parameter order and type. In this exercise, these elements will be provided to you. However, in future assignments, you may be required to deduce some or all of them from the context of the problem. Overview 1. Create the Java classes Problem, Hypothesis, Evidence, Relevance. The class Reasoning is provided to you. 2. Include all the fields and methods described below. 3. Test your code for correctness. 4. Prepare the assignment for submission and submit the java files through Gradescope. 2 Rules 1. You may not import any extra functionality besides the default. For example, System and Math are imported by default and thus may be used, whereas something like ArrayList must be explicitly imported so it is disallowed. 2. The main method will not be tested; you may use it any way you want. 3. Comment your code, especially the parts where it is not obvious what you're doing. Argumentation concepts The figure below shows an example of a simplified version of a Wigmorean probabilistic inference network used to model an argumentation. This type of network has features required by models developed in the 3rd wave of AI: transparency, explanations, and trust. To read more: - The evidence-based reasoning: Tecuci, Gheorghe, et al. "Toward a Computational Theory of Evidence-Based Reasoning for Instructable Cognitive Agents." arXiv preprint arXiv:1910.03990 (2019). (https://arxiv.org/pdf/1910.03990) - Wigmore Chart: https://en.wikipedia.org/wiki/Wigmore_chart 3 Probability: for this model, we will use a probability scale from 0 to 5. This scale is also used to quantify the relevance level and the inferential force. - 0: Extremely Unlikely - 1: Very Unlikely - 2: Unlikely - 3: Likely - 4: Very Likely - 5: Certain This will be used to quantify the probability scale of the: - Hypothesis or Evidence (in red) - Relevance (in blue) - Inferential Force (in green) The interpretation is the following: Evidence A probability comes from the user. The user set the probability of Evidence A to certain. It makes sense since he gets attendance data from a reliable measurement system. So, he estimates that the probability of this Evidence being True is certain. He could assign a lower probability level if he had some doubts about the accuracy of those data. Evidence B probability is also set to certain by the user. Now the Relevance of Evidence B to Hypothesis 1 is set by the user to likely. This value reflects how relevant the item of evidence is to the hypothesis being estimated. For instance, it is not certain that “The Student who sends a notice when he will be late would attend class regularly”. But the user thinks that it is likely that this student is likely to be present regularly. However, the Relevance of Evidence A to Hypothesis 1 is set by the user to certain. Assuming that the item of evidence is true, it would be certain that this student attends the class regularly. The Inferential Force is computed by the formula provided in this document. It reflects the probability of Hypothesis 1 to be true if it depended only on a given item of evidence (or a Sub-Hypothesis). For instance, Hypothesis 1 would be likely to be true if it depended only upon Evidence B. Also, Hypothesis 1 would be certain to be true if it depended only upon Evidence A. Consequently, the Probability of Hypothesis 1 is likely. This value is computed by the formula provided in this document. It depends on the 2 inferential forces computed earlier. 4 Instructions 1. Implement the five java classes described below in five separate, appropriately named, .java files. 2. You must do your own testing. Feel free to create a main method in the driver class to test your code. 3. Submit the files on Gradescope. Make sure that you submit the correct files. The Evidence class: This class represents an item of evidence that can be used to support or disfavor a Hypothesis. Assume that we will use only favoring pieces of evidence and hypotheses. To evaluate the probability of an item of evidence being true, we need to know how ambiguous it is and how credible its source is. Out of the 5 well-known characteristics of an item of evidence, we will use 4 of them for simplicity. This relation depends on the type of evidence. The table below shows how this value should be estimated a priori: / Feature Type Unambiguity {0, ..., 5} Credibility {0, …, 5} Completeness {0, …, 5} Conclusiveness {0, …, 5} 1-Real Evidence 0.4 0.3 0.2 0.1 2-Testimonial Statement 0.2 0.4 0.2 0.2 3-Demonstrative Evidence 0.3 0.2 0.4 0.1 4-Documentary Evidence 0.2 0.6 0.1 0.1 5-Not Specified 0.25 0.25 0.25 0.25 For instance, if the evidence type is 1 (i.e Real Evidence), its probability of being true would be: 0.4(unambiguity) + 0.3(credibility) + 0.2(completeness) + 0.1(conclusiveness). The result will be a float value. You will use type casting to convert float to byte. This will truncate the float value. You need to implement constructors and methods to instantiate the fields of an Evidence. ➔ Fields: ◆ description: a String that represents the description of the evidence. ◆ type: a byte that represents the type of evidence. The default value is 5. ◆ unambiguity: a byte that represents the unambiguity of evidence. ◆ credibility: a byte that represents the credibility of evidence. ◆ completeness: a byte that represents the completeness of evidence. ◆ conclusiveness: a byte that represents the conclusiveness of evidence. ◆ probability: a byte that represents the probability of this evidence being true. 5 All fields are private. ➔ Getters and Setters: The getters will be public and will have the following signature: ◆ String getDescription() ◆ byte getType() ◆ byte getUnambiguity() ◆ byte getCredibility() ◆ byte getCompleteness() ◆ byte getConclusiveness() ◆ byte getProbability() The setters will be public and will have the following signature: ◆ void setDescription(String description) ◆ void setType(byte type): ● You must check if the value type is between 1 and 5. Otherwise set the value to 5. ◆ void setUnambiguity(byte unambiguity) ◆ void setCredibility(byte credibility) ◆ void setCompleteness(byte completeness) ◆ void setConclusiveness(byte conclusiveness) N.B- The value of the attributes unambiguity, credibility, completeness, and conclusiveness must be between 0 and 5. If the user tries to set the value to an invalid number, choose the valid value that is closer to the argument. ➔ Constructors: Create two constructors for this class. See the signature of the constructors below: public Evidence (String description, byte type, byte unambiguity, byte credibility, byte completeness, byte conclusiveness) public Evidence (String description, byte unambiguity, byte credibility, byte completeness, byte conclusiveness) The constructors should instantiate the variables using the provided arguments and will compute the probability based on the weights and factors in the table above. For the second constructor, set the type to its default value and compute the probability of the evidence. 6 Business rules: - The value of the above-mentioned factor needs to be between 0 and 5. - The value of probability needs to be between 0 and 5 inclusively. - If the argument is outside of this range, choose the valid value that is closer to the argument. ➔ Other methods: You need to define the following methods: - public byte computeProbability(): This method can be used to compute the probability of an item of evidence. - public static String probability2String(byte probability): This method can be used to return the String representation of the probability level. Eg. For input = 1, the function will return the String “Very Unlikely”. For 0 or invalid input, the function returns “Extremely Unlikely”. - public String toString(): This method can be called to return the object in a human-friendly manner. It needs to return the following information: Evidence: This is the description. ** Type of evidence: Real Evidence ** Probability: 3 -> likely - public String printFullDescription(): This method can be called to return the object in a human-friendly manner. It re-uses the toString() method described above and needs to return the following information: Evidence: This is the description. ** Type of evidence: Real Evidence ** Probability: 3 -> likely ** Evaluated based on those characteristics: ** >> Unambiguity: 5 ** >> Credibility: 3 ** >> Completeness: 4 ** >> Conclusiveness: 2 The Relevance class: This class will represent the relevance of an Evidence or another Hypothesis (Sub- Hypothesis) to a specific Hypothesis of interest. Here is the detailed specification: ➔ Fields: ◆ evidence: an Evidence object representing the item of evidence whose relevance is represented by the current object. ◆ subHypothesis: a Hypothesis object representing the sub-hypothesis whose relevance is represented by the current object. 7 ◆ level: a byte representing the probability of the hypothesis being true if the underlying sub-hypothesis or the evidence was certain. This value must be between 0 and 5. ◆ inferentialForce: a byte representing the probability of the Hypothesis of interest to be true considering only the underlying evidence or sub- hypothesis. All fields are private. ➔ Getters and Setters: The getters will be public and will have the following signature: ◆ Evidence getEvidence() ◆ Hypothesis getSubHypothesis() ◆ byte getLevel() ◆ byte getInferentialForce() The setters will be public and will have the following signature: ◆ void setEvidence(Evidence evidence) ● This setter method will also set the subHypothesis attribute to null. And will set the inferential force calling the function setInferentialForce() described below. ◆ void setSubHypothesis(Hypothesis subHypothesis) ● This setter method will also set the evidence attribute to null. And will set the inferential force calling the function setInferentialForce() described below. ◆ void setLevel(byte level): ● The level must be between 0 and 5. Otherwise, use the valid value that is closer to the argument provided. This method will also set the inferential force by calling the function setInferentialForce() described below. ◆ void setInferentialForce(): ● This function takes no argument. It will use the following formula to compute