AiDefinitionQuiz.cpp

#include <iostream>
#include <map>
#include <string>
#include <algorithm>
#include <vector>
using namespace std;


map<string,string> getAiMap(){
    map<string,string> m = {
        {"Physical symbol system hypothesis","symbolism"},
{" Neural network, connection mechanism","connectionism"},
{"Cybernetics and perception-action control system","actionism"},
{"AI is a technical science that studies and develops theories,","ai"},       
{"Machine learning specializes in how computers simulate or implement human learning behavior","machine learning"}, 
{"Deep learning is developed based on the study of artificial neural networks (ANNs).","deep learning" },
{"automatically and accurately transcribes human speeches.","speech signal processing"},
{"deals with how computers can be made to identify objects, scenes, and activities from images","computer vision"}, 
{"mainly involves knowledge acquisition and expression, natural language understanding, and natural language generation.","nlp"},
{"the reuse of a pre-trained model on a new related problem","transfer learning"},
{"It is the process of removing personally identifiable information from personal data, so that the people whom the data describes remain anonymous.","data anonymization"},
{"There are static knowledge and dynamic knowledge","knowledge"},
{"A theoretical or practical understanding of a subject or a domain.","knowledge"},
{"Skillful person who can do things other people cannot.","domain expert"}, 
{"Deals with the development of intelligent information systems in which knowledge and reasoning play pivotal role.","knowledge engineering (ke)"},     
{"A developed filed at the intersection of computer science and management science deals with knowledge as a key source in modern organizations","knowledge management (km)"},
{"Graphical representation that represent semantic relations between concepts.","semantic network"},
{"is computer program that emulates the decision making capability of a human expert.","expert system"},
{"contains five members: the domain expert, the knowledge engineer, the programmer, the project manager and the end-user","expert system development team"},
{"someone who is capable of designing, building and testing an expert system.","knowledge engineer"},
{"enable the user to ask the expert system how a particular conclusion is reached and why a specific fact is needed. An expert system must be able to explain its reasoning and justify its advice, analysis or conclusion.","explanation facilities"},
{"The means of communication between a user seeking a solution to the problem and an expert system.","user interface"},
{"A technique for gathering information and then inferring from it whatever can be inferred.","forward chaining"},{"goal-driven reasoning.","backward chaining"},
{"The lack of the exact knowledge that would enable us to reach a perfectly reliable conclusion.","uncertainty"}, 
{"permits only exact reasoning. It assumes that perfect knowledge always exists.","classical logic"},
{"is a mixture of knowledge and the control structure to process this knowledge.","conventional program"},
{"(including deep learning) is a study of learning algorithms.","machine learning"},
{"A computer program needs to specify which of the k categories some input belongs to.","classification"},        
{"A computer program predicts the output for the given input.","regression"},
{"A large amount of data from an unlabeled dataset is divided into multiple categories according to internal similarity of the data.","clustering"},    
{"Obtain an optimal model with required performance through training and learning based on the samples of known categories *e.g. Classification & regression*","supervised learning"},        
{"For unlabeled samples, the learning algorithms directly model the input datasets. *e.g. Clustering*","unsupervised learning"},
{"A machine learning model that automatically uses a large amount of unlabeled data to assist learning directly of a small amount of labeled data.","semisupervised learning"},
{"It is an area of machine learning concerned with how agents ought to take actions in an environment to maximize some notion of cumulative reward.","reinforcement learning"},
{"A collection of data used in machine learning tasks.","dataset"},
{"A dataset used in the training process, where each sample is referred to as a training sample.","training set"},{"Testing refers to the process of using the model obtained after learning for prediction.","test set"},
{"Fill in missing values and detect and eliminate causes of dataset exceptions.","data cleansing"},
{"Simplify data attributes to avoid dimension explosion.","data dimension reduction"},
{"Normalize data to reduce noise and improve model accuracy.","data normalization"},
{"Are independent of the model during feature selection.","filter methods"},
{"Use a prediction model to score feature subsets.","wrapper methods"},     
{"Consider feature selection as a part of model construction.","embedded methods"},
{"The goal of machine learning is that the model obtained","after learning should perform well on new samples"},  
{"The goal of machine learning is that the model obtained after learning should perform well on new samples","generalization capability"},
{"difference between the sample result predicted by the model obtained after learning and the actual sample result.","error"},
{"error that you get when you run the model on the training data.","training error"},
{"error that you get when you run the model on new samples. Obviously, we prefer a model with a smaller generalization error.","generalization error"}, 
{"occurs when the model or the algorithm does not fit the data well enough.","occurs when the model or the algorithm does not fit the data well enough."},
{"occurs when the training error of the model obtained after learning is small but the generalization error is large (poor generalization capability)","overfitting"},
{"Model's capability of fitting functions, which is also called model complexity","model capacity"},
{"Offset of the prediction result from the average value","variance"},      
{"Difference between the expected (or average) prediction value and the correct value we are trying to predict.","bias"},
{"Uses all training samples for training each time.","bgd"},
{"Uses one training sample for training each time.","sgd"},
{"Uses a certain number of training samples for training each time.","mbgd"},
{"It is a statistical analysis method used to validate the performance of a classifier","cross validation"},      
{"A statistical analysis method to determine the quantitative relationships between two or more variables through regression analysis in mathematical statistics","linear regression"},       
{"The logistic regression model is used to solve classification problems.","logistic regression"},
{"is a tree structure (a binary tree or a non-binary tree). Each non-leaf node represents a test on a feature attribute.","decision tree"},
{"Select a feature from the features of the training data as the split standard of the current node","feature selection"},
{"Generate internal node upside down based on the selected features and stop until the dataset can no longer be split.","decision tree generation"},
{"The decision tree may easily become overfitting unless necessary pruning (including pre-pruning and postpruning) is performed to reduce the tree size and optimize its node structure.","pruning"},
{"is a theoretically mature method and one of the simplest machine learning algorithms.","knn classification algorithm"},
{"a simple multi-class classification algorithm based on the Bayes theorem.","naive bayes algorithm"},
{"is a type of boosting algorithm.","gbdt"}
    };

    return m;
}

void prepMap(){
    string q,a;
    vector<string> qarr;
    vector<string> aarr;
    while(q != "exit"){
        cout<<"Enter Question: \n";
        getline(cin, q);
        if(q == "exit") break;
        cout<<"Enter Answer:\n";
        getline(cin,a);
        std::for_each(a.begin(), a.end(), [](char & c) {
        c = ::tolower(c); 
    });
        aarr.push_back(a);
        qarr.push_back(q);
         cout<<"Enter 'exit' in Question to exit\n";
    }
    for (int i = 0; i < qarr.size(); i++)
    {
        cout<<"{"<<"\""<<qarr[i]<<"\""<<","<<"\""<<aarr[i]<<"\""<<"},"<<endl;
    }   
}


void quiz(map<string,string> q){
    map<string,string> :: iterator it;
    vector<string> questions;
    for(it=q.begin(); it != q.end(); it++){
        questions.push_back(it->first);
    }
    string a;
    for (int i = 0; i < questions.size(); i++)
    {

        cout<<questions[i]<<endl<<"Answer: ";
        getline(cin, a);
        if(a == "exit") break;
        else if(a == q[questions[i]]){
            cout<<"true.\t";
        } else cout<<"false.\t";
        cout<<"The correct answer is: "<<q[questions[i]]<<endl<<endl;

    }
}
int main(){
    //prepMap();
    quiz(getAiMap());
    return 0;
}