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| public:t-720-atai:atai-19:final_project [2019/10/03 10:24] – created thorisson | public:t-720-atai:atai-19:final_project [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| Grading: 16% of final grade. | Grading: 16% of final grade. |
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| {{/public:t-720-atai:final_project_fuzzy_logic.pdf}} | {{/public:t-720-atai:final_project_diagnosis.pdf}} |
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| Make sure you have studied Xiang Li's {{/public:t-720-atai:nars-tutorial.pdf|NARS Tutorial}} notes. | Make sure you have studied Xiang Li's {{/public:t-720-atai:nars-tutorial.pdf|NARS Tutorial}} notes. |
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| Fuzzy logic is a form of many-valued logic in which the truth values may be any real number between 0 and 1. It is employed to handle concepts of graded membership and proposition of partial truth with completely true and completely false. | In this project you will generate a diagnostic system with OpenNARS. Use the medical diagnostic example and the description about the Car diagnostic system as hints to design a diagnostic system for Cars, test it with OpenNARS. Try to come up with good alternative examples, for instance the following: |
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| NAL, the representation language of NARS, is not a "fuzzy logic" in the conventional sense, but shares certain intuitions and ideas with it. In NAL, fuzziness typically comes from the diversity in the intension of a term, that is, an instance may have some, but not all, properties that are usually associated with the concept. For example, “Penguins are birds" is true to a degree, because penguins do not have all the common properties of birds, but only some of them. This distinguishes it from the other common forms of uncertainty, randomness, as randomness typically comes from the diversity in the extension. For example, “birds fly" is true to a degree, because some birds fly, and some do not. These two types of uncertainty are uniformly represented and processed in NAL, as different ways for a categorical statement to get (positive and negative) evidence. | 1. Different type of cars (Sedan, Sport, Truck) may have different possibility of having different problems when same symptoms show up, for instance, adults have a higher possibility to get HIV than children. If Dan has same symptom with John but Dan is a child, will Dan have same disease with John? |
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| | 2. In the medical diagnostic example, default truth values (f = 1.0, c = 0.9) are attached to all the statements of John’s symptoms, if we change those statements to statements where true to the degree. |
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| | 3. If we already know what disease that John has, and also some symptoms, try to generate a question to display what gender that John possibly is as well as the age group. |
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