Artificial Intelligence And Expert Systems 1st Edition I. Gupta , G. Nagpal - Solutions

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Exact inference can be carried out efficiently for sparse graphs (with low treewidth) using recursive conditioning or variable elimination.
A Bayesian belief network is a representation of conditional independence of random variables.
Probability is a measure of belief in a proposition.
It can be dangerous to make decisions based on data of dubious quality;large quantity and high quality are difficult to achieve together.
Neural networks are very successful for applications where there are large training sets, or where training data can be generated from a model.
Neural networks are used for generative AI, for the generation of images, text, code, molecules, and other structured output.
Transformers, using layers of linear transformation and attention, are the workhorse for modern language processing, computer vision, and biology.
Attention for text is used to compute the expected embedding of words based on their relationship with other words. Attention is also used for speech, vision, and other tasks.
Recurrent neural networks can be used for sequences. An LSTM is a type of RNN that solves the vanishing gradients problem.
Convolutional neural networks apply learnable filters to multiple positions on a grid.
Gradient descent is used to train by making steps proportional to the negation of the gradient; many variants improve the basic algorithm by adjusting the step size and adding momentum.
Backpropagation can be used for training parameters of differentiable (almost everywhere) functions.
Neural networks that use ReLU for all hidden units define piecewise linear functions if they have a linear output, or piecewise linear separators if they have a sigmoid output.
The output is typically a linear function for a real-valued prediction, with a sigmoid for a Boolean prediction, or with a softmax for a categorical prediction. Other outputs, such as sequences or structured predictions, use specialized methods.
Artificial neural networks are parametrized models for predictions, typically made of multiple layers of parameterized linear functions and nonlinear activation functions.
Data is invariably about the past. If data is used for acting in the future, the predictor may no longer work as well, and we might want the future to be different from the past.
Gradient-boosted trees are an efficient and effective representation for many learning problems.
Linear models and decision trees are representations which are the basis for more sophisticated models.
The speed is improved; the agent can do things faster.
The accuracy on tasks is improved; the agent can do things better.
The range of behaviors is expanded; the agent can do more.
The video game industry has successfully exploited AI planning techniques.
SAT solvers play a critical role in many important applications.
In a scheduling task, it is important to consider the preferences and constraints of everyone involved in the task, and to consider any tradeoffs required.
Instead of reasoning explicitly in terms of states, it is almost always much more efficient for an agent to reason in terms of variables or features.
When designing a search problem for the real world, you should ensure that there are no unintended social consequences,
A∗ search can use a heuristic function that estimates the cost from a node to a goal. If a graph satisfies some reasonable condition (see Proposition 3.2 (page 112)) and the heuristic is admissible, A∗ is guaranteed to find a lowest-cost path to a goal if one exists.
Many problems can be abstracted to the problem of searching to find paths in graphs.
An agent’s decisions implicitly convey the preferences of the agent. It is debatable whether it should be up to the owners or designers of agents(such as autonomous vehicles) to set the preferences, as they have different preferences than the rest of the population.
The social impacts, both beneficial and harmful, of pervasive AI applications are significant, leading to calls for ethical and human-centered AI, certification and regulation.
To solve a task by computer, the computer must have an effective representation with which to reason.
A designer of an intelligent agent should be concerned about modularity, how to describe the world, how far ahead to plan, uncertainty in both perception and the effects of actions, the structure of goals or preferences, other agents, how to learn from experience, how the agent can reason while
A physical symbol system manipulates symbols to determine what to do.
An agent acts in an environment and only has access to its abilities, its prior knowledge, its history of stimuli, and its goals and preferences.
Artificial intelligence is the study of computational agents that act intelligently.
Why is Prolog used in artificial intelligence?
What are the advantages and disadvantages of Prolog?
Why are cut and fail used in Prolog?
What are the disadvantages of backtracking?
What is backtracking?
Write a program in Prolog using any type of predicate.
Differentiate between file input output and character input output.
What are input output predicates in Prolog?
Write a program in Prolog using arithmetic operations.
Define a data structure for Prolog.
Write a program in Prolog using lists.
Write a program in Prolog using structures.
How does Prolog satisfy goals?
What are the notations for building blocks in Prolog?
How many types of rules are there? Explain them.
Define facts and rules.
How do we differentiate between a Prolog and a simple English sentence?
What is Prolog? How it is different from other languages?
What are some practical applications of fuzzy controllers?
Define “defuzzification.”
What are two types of knowledge in a fuzzy systems knowledge base?
Provide examples of how fuzzy systems are used?
What is entailment?
Explain fuzzy reasoning.
What are two ways of interpreting fuzzy relations?
What are fuzzy relations, and does fuzzy logic support them?
What is a fuzzy rule?
Differentiate between a fuzzy set and a crisp set.
What types of operations can be performed on or with fuzzy sets?
What is the main difference between probability and fuzzy logic?
What is a linguistic variable?
Name three strengths and three weaknesses of fuzzy expert systems.
Can a fuzzy membership be true and false at the same time?
What is a membership function of a fuzzy set?
Differentiate between fuzzy and Boolean logic.
Discuss the various applications of neural networks.
What are the pros and cons of neural networks?
Write the back propagation algorithm in detail.
Discuss the learning process in a neural network.
What is the transfer function in an artificial neural network? Explain the types of ANNs.
Define complicated neural networks using a suitable diagram.
Explain all the types of architecture of neural networks.
Differentiate between a biological neural network and an artificial neural network.
How does information flow in an artificial neural network?
Explain the workings of a biological neural network.
What are the types of neural networks?
Discuss a neuron using a diagram.
Explain the neural network in detail.
What are the advantages of neural networks over conventional computers?
What is the use of neural networks in AI?
What is meant by Conceptual Dependency?
What is a script and what are its components?
Explain the different types of frames.
What is unification used for?
What is a validity-maintaining procedure for deriving sentences from other sentences in first-order logic?
Name the relationships that categories may have. How may several category relationships be organized?
How is knowledge given to and received from expert systems?
What do semantic networks represent? How?
How would an agent use the expression Dog(fido) to solve a problem?
Distinguish predicate logic from propositional logic.
What are the quantifiers in predicate logic and their meanings?
What does first-order logic (predicate logic) consist of?
What is the relationship between belief and knowledge?
Explain the various knowledge acquisition techniques.
Explain the knowledge acquisition role in knowledge engineering.

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