Artificial Intelligence MCQ Quiz in मराठी - Objective Question with Answer for Artificial Intelligence - मोफत PDF डाउनलोड करा

Key Points

Reinforcement learning is a machine learning training method based on rewarding desired behaviors and/or punishing undesired ones. In general, a reinforcement learning agent is able to perceive and interpret its environment, take actions and learn through trial and error.

Reinforcement learning algorithms are mainly used in AI applications and gaming applications.

The main used algorithms are:

Q-Learning: Q-learning is an Off policy RL algorithm, which is used for temporal difference learning.

Hence the correct answer is Q-learning.

Additional Information

• Naive Bayes classification is a form of supervised learning. It is considered to be supervised since naive Bayes classifiers are trained using labeled data.
• Principal Component Analysis (PCA) is an unsupervised, non-parametric statistical technique primarily used for dimensionality reduction in machine learning.
• Fast Forest Quantile Regression keeps all the predicted labels in trees specified by the parameter Quantile sample count and outputs the distribution so that the user can view the quantile values for the given instance.

Key Points

• Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
• Decision trees can also be used to form clusters in the data but clustering often generates natural clusters and is not dependent on any objective function.
• Decision tree-based reinforcement learning provides good learning performance and meets our needs for more reliable convergence than the neural network approach. It also has lower memory requirements than the table lookup method and scales better to large input spaces.

Hence the correct answer is Clustering.

Additional Information

• A classification algorithm is a Supervised Learning technique that is used to identify the category of new observations on the basis of training data. In Classification, a program learns from the given dataset or observations and then classifies new observations into a number of classes or groups.
• Regression analysis consists of a set of machine learning methods that allow us to predict a continuous outcome variable (y) based on the value of one or multiple predictor variables (x). It assumes a linear relationship between the outcome and the predictor variables.

In artificial intelligence, environment is that surrounds an agent. Environment is where agent lives, do its work or we can say that surrounding in which agent is present.

Environment is clearly distinguishable from the agent, it is not the part of the agent. An environment can have various attributes from the point of view of the agent. These are :

1) Observable and partially observable: An agent is considered to be an agent only if it has the ability to observe its environment. In some cases, environment may be partially observable. Uncertain environment is not fully observable.

2) Deterministic and stochastic: A deterministic environment is one where any future state of the environment can be completely determines from preceding state and actions of agent. It is stochastic if there is some element of uncertainty.

3) Episodic and sequential: If each of agent’s tasks do not rely on past performance than episodic otherwise it is sequential.

Concept:

Agent represent the most important new paradigm for software development. An autonomous agent is a system situated within and a part of an environment that senses the environment and acts on it.

Explanation:

An agent is situated in environment and makes its own decisions. it perceives the environment through sensors and acts on the environment through actuators.

Simple reflex agents selects actions based on the agents current perception or the world and not based on past perceptions. They work when environment if fully observable. It is based on condition action rule which means it maps a state with an action.

Diagram:

In artificial intelligence, environment is that surrounds an agent. Environment is where agent lives, do its work or we can say that surrounding in which agent is present.

Environment is clearly distinguishable from the agent, it is not the part of the agent. An environment can have various attributes from the point of view of the agent. These are :

1) Observable and partially observable: An agent is considered to be an agent only if it has the ability to observe its environment. In some cases, environment may be partially observable. Uncertain environment is not fully observable.

2) Deterministic and stochastic: A deterministic environment is one where any future state of the environment can be completely determines from preceding state and actions of agent. It is stochastic if there is some element of uncertainty.

3) Episodic and sequential: If each of agent’s tasks do not rely on past performance than episodic otherwise it is sequential.

A self-organizing map:

• It is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map, and is therefore a method to do dimensionality reduction.
• Self-organizing maps differ from other artificial neural networks as they apply competitive learning as opposed to error-correction learning (such as backpropagation with gradient descent), and in the sense that they use a neighbourhood function to preserve the topological properties of the input space.