Steepest – accent Hill Climbing→ Considers all moves from current state and selects best move.
Branch – and – bound → Keeps track of all partial paths which can be a candidate for further exploration
Constraint satisfaction → Discover problem state(s) that satisfy a set of constraints
Means – end – analysis → Detects difference between current state and goal state

Intelligence → Judgemental
Knowledge → Codifiable, endorsed with relevance and purpose
Information → Scattered facts, easily transferable
Data → Contextual, tacit, transfer needs learning

→ Classical planning environments that are fully observable, deterministic, finite, static and discrete (in time, action, objects and effects).

Hill climbing limitations:
1. Local Maxima: Hill-climbing algorithm reaching the vicinity a local maximum value, gets drawn towards the peak and gets stuck there, having no other place to go.
2. Ridges: These are sequences of local maxima, making it difficult for the algorithm to navigate.
3. Plateaux: This is a flat state-space region. As there is no uphill to go, algorithm often gets lost in the plateau.
To avoid above problems using 3 standard types of hill climbing algorithm is
1. Stochastic Hill Climbing selects at random from the uphill moves. The probability of selection varies with the steepness of the uphill move.
2. First-Choice Climbing implements the above one by generating successors randomly until a better one is found.
3. Random-restart hill climbing searches from randomly generated initial moves until the goal state is reached.

The classification learning algorithms are
1. Logistic regression
2. Back propagation
Note: They given spelling mistake in Logistic regression instead of “Logistic repression”.
According to final key, given marks to all.

Meaning analysis is not typically considered a distinct component of the natural language understanding (NLU) process. Instead, it is often encompassed within the broader category of semantic analysis. The key components of the NLU process include: Morphological Analysis: This component deals with the analysis of word structure, including breaking words into meaningful units (morphemes), inflections, and word forms. Semantic Analysis: This is the component responsible for understanding the meaning of words, phrases, and sentences. It involves determining the relationships between words and extracting the intended meaning. Pragmatic Analysis: Pragmatics focuses on the interpretation of language in context, including factors like speech acts, implicatures, and understanding the intentions and presuppositions of the speaker. So, "Meaning analysis" is usually encompassed within "Semantic analysis," and all the other components listed are integral parts of the natural language understanding process.

The property that is not typically considered a property of a good system for the representation of knowledge in a particular domain is "Presentation adequacy." Presentation adequacy refers to how well the system's knowledge representation can be presented and understood by humans. While it's important to have a representation that can be comprehended by humans, the primary properties often associated with a good knowledge representation system are: Inferential Adequacy: The system's ability to support reasoning and inference within the domain. It should be able to draw meaningful conclusions and make inferences based on the represented knowledge. Inferential Efficiency: How efficiently the system can perform reasoning and inference. A good system should allow for efficient processing and deduction of new knowledge from the existing representation. Acquisitional Efficiency: How efficiently the system can acquire or learn new knowledge and integrate it into the existing representation. This relates to the ease of updating and expanding the knowledge base. While presentation adequacy is important for human understanding, it's not traditionally considered one of the core properties of a knowledge representation system. Instead, it's often viewed as an interface or display issue, focusing on how well the representation can be communicated to users.

A genetic algorithm typically uses various mutation operators to introduce diversity into the population. Here's an explanation of each of the options: A. Random Resetting: Random resetting is a mutation operator where one or more genes in an individual's chromosome are randomly changed or reset to new random values. It is a valid mutation operator in genetic algorithms. B. Scramble: The scramble operator involves shuffling or permuting a subset of genes within a chromosome. It is a valid mutation operator in genetic algorithms. C. Inversion: The inversion operator reverses the order of a subset of genes within a chromosome. It is a valid mutation operator in genetic algorithms. D. Difference: "Difference" is not a standard mutation operator in genetic algorithms. While operators like "random resetting," "scramble," and "inversion" are commonly used, "difference" is not a recognized mutation operator in this context. So, the correct answer is D only because "Difference" is not a mutation operator commonly used in genetic algorithms.

Statement I correctly identifies that a "fuzzifier" is a component of a fuzzy system. A fuzzifier is responsible for converting crisp (non-fuzzy) inputs into fuzzy sets. Statement II is also correct because an "inference engine" is a crucial component of a fuzzy system. It's responsible for making decisions and performing reasoning based on fuzzy logic rules and inputs. Both statements are accurate, and there is no conflict between them.

Statement B is correct. PCA (Principal Component Analysis) is indeed used for dimension reduction in machine learning and data analysis. Statement C is also correct. Apriori is a frequent itemset mining algorithm used in association rule learning and is not a supervised technique in machine learning. Statements A and D are not correct: Statement A is incorrect. "C-fuzzy" is not a standard term in machine learning, and the statement doesn't accurately describe a supervised method of learning. Statement D is also incorrect. "Underfitting" is when a model is too simple and fails to capture the underlying patterns in data. It is the opposite of overfitting, which is when a model becomes too specialized to its training data.

"Combinations" is not typically a direct solution representation in a genetic algorithm. In genetic algorithms, the common solution representations include: Binary Valued: Where each gene in an individual is represented as a binary value (0 or 1). Real Valued: Where each gene in an individual is represented as a real number, often within a specific range. Permutation: Where the genes represent a permutation or ordering of elements. This is often used for problems like the Traveling Salesman Problem. "Combinations" as a direct representation is not commonly used in genetic algorithms. Instead, it might be implemented using other representations like binary, real-valued, or permutation depending on the specific problem being solved.

Alpha-beta pruning is a modified version of the minimax algorithm. It is an optimization technique for the minimax algorithm.

Alpha-beta Algorithm:
- Uses Depth first search
- only considers nodes along a single path from root at any time
α = highest-value choice found at any choice point of path for MAX (initially, α = −infinity)
β = lowest-value choice found at any choice point of path for MIN (initially, β = +infinity)
- Pass current values of α and β down to child nodes during search.
- Update values of α and β during search:
- MAX updates α at MAX nodes
- MIN updates β at MIN nodes

When to Prune:
- Prune whenever α ≥ β.
- Prune below a Max node whose alpha value becomes greater than or equal to the beta value of its ancestors.
- Max nodes update alpha based on children’s returned values. - Prune below a Min node whose beta value becomes less than or equal to the alpha value of its ancestors.
- Min nodes update beta based on children’s returned values.

Effectiveness of Alpha-Beta Search:
- Alpha/beta best case is O(b^(d/2)) rather than O(b^d)
- This is the same as having a branching factor of sqrt(b),
- (sqrt(b))^d/ = b^(d/2) (i.e., we have effectively gone from b to square root of b)
- In chess go from b ~ 35 to b ~ 6
- permitting much deeper search in the same amount of time
- In practice it is often b^(2d/3)

Branch-and-bound→ Keep track of all partial paths which can be a candidate for further exploration.
Steepest-ascent hill climbing → Considers all moves from current state and selects the best move.
Constraint satisfaction → Discovers problem state(s) that satisfy a set of constraints.
Means-end-analysis → Detects difference between current state and goal state.

Input:
f(x)=x is my friend
p(x) = x is perfect
So, they are asking about SOME. Finally, outer most parentheses will get SOME.
So, based on this we will eliminate 2 options.
They are given conditions like NOT perfect. So, we get ⌐p(x).
The final condition is ∃_x(f(x)∧⌐p(x))

Greedy best-first search algorithm always selects the path which appears best at that moment. It is the combination of depth-first search and breadth-first search algorithms.
Time Complexity: The worst case time complexity of Greedy best first search is O(b^m).
Space Complexity: The worst case space complexity of Greedy best first search is O(b^m). Where, m is the maximum depth of the search space.
Complete: Greedy best-first search is also incomplete, even if the given state space is finite.
Optimal: Greedy best first search algorithm is not optimal.
Note:Refer the corresponding algorithms from standard sources.

If the correlation between the variables V1 and V2 is 1, then all the data points will be in a straight line. So, all the three cluster centroids will form a straight line as well.

Statement A is false : The relationship of entailment between sentence is crucial to our understanding of reasoning. A sentence α entails another sentence β if β is true in all world where α is true. Equivalent definitions include the validity of the sentence α⇒β and the unsatisfiability of sentence α∧¬β.
Statement D is false:Propositional logic does not scale to environments of unbounded size because it lacks the expressive power to deal concisely with time, space and universal patterns of relationships among objects.
Statement B is true:
Refer the below link:
https://www.iiia.csic.es/~puyol/IAGA/Teoria/07-AgentsLogicsII.pdf Statement C is true:
Sound/truth preserving: An inference algorithm that derives only entailed sentences. Soundness is a highly desirable property. (e.g. model checking is a sound procedure when it is applicable.)

Minimax is a decision rule used in artificial intelligence, decision theory, game theory, statistics, and philosophy for minimizing the possible loss for a worst case (maximum loss) scenario.
Optimal decision in deterministic, perfect information games
Idea : choose the move resulting in the highest minimax value
Completeness: Yes if the tree is finite
Optimality: Yes, against an optimal opponent.
Time Complexity: O(b^m)
Space Complexity: O(bm) – depth first exploration.
Hence Statement (A) is true.
Statement (B):
Alpha Bound of J:
→ The max current The max current val of all MAX ancestors of J of all MAX ancestors of J
→ Exploration of a min node, J, Exploration of a min node, J, is stopped when its value is stopped when its value equals or falls below alpha. equals or falls below alpha.
→ In a min node, we n node, we update beta update beta
Beta Bound of J:
→ The min current The min current val of all MIN ancestors of J of all MIN ancestors of J
→ Exploration of a Exploration of a max node, J ax node, J, is stopped when its stopped when its value equals or exceeds beta equals or exceeds beta
→ In a max node, we update a ax node, we update alpha
Pruning does not affect the final result
Does ordering affect the pruning process?
Best case O(b^m/2)
Random (instead of best first search) - O(b^3m/4)
Hence statement (B) is false.
Statement C: This statement is true.
Statement D: This statement is false because past exploration information is used from transposition tables.

In a genetic algorithm, a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible.
In nondeterministic environments, percepts tell the agent which of the possible outcomes has actually occurred.Solutions for nondeterministic problems can contain nested if-then-else statements that create a tree rather than a sequence of actions

1. (R ∧E) ↔C
This is not equivalent. It says that all (and only) conservatives are radical and electable.
2. R →(E ↔C)
This one is equivalent. if a person is a radical then they are electable if and only if they are conservative.
3. R →((C →E) ∨¬E)
This one is vacuous. It’s equivalent to ¬R ∨ (¬C ∨ E ∨ ¬E), which is true in all interpretations.
4.R ⇒ (E ⇐⇒ C) ≡ R ⇒ ((E ⇒ C) ∧ (C ⇒ E))
≡ ¬R ∨ ((¬E ∨ C) ∧ (¬C ∨ E))
≡ (¬R ∨ ¬E ∨ C) ∧ (¬R ∨ ¬C ∨ E))

Affiliate Marketing: Vendors ask partners to place logos on partner’s site. If customers click, come to vendors and buy.
Viral Marketing: Spread your brand on the net by word-of-mouth. Receivers will send your information to their friends.
Group Purchasing: Aggregating the demands of small buyers to get a large volume. Then negotiate a price.
Bartering Online: Exchanging surplus products and services with the process administered completely online by an intermediary. Company receives “points” for its contribution.

Absurd→ Clearly impossible being contrary to some evident truth.
Ambiguous→ Capable of more than one interpretation or meaning.
Axiom→ An assertion that is accepted and used without a proof.
Conjecture→ An opinion preferably based on some experience or wisdom

Given P(m,n) ="m divides n"
Statement-A is ∃m ∀n P(m, n). Here, there exists some positive integer which divides every positive integer. It is true because there is positive integer 1 which divides every positive integer.
Statement-B is ∀n P(1, n). Here, 1 divided every positive integer. It is true.
Statement-C is ∀m ∀n P(m, n). Here, every positive integer divided every positive integer. It is false.

→ An intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it is "rational", as defined in economics).
→ Intelligent agents may also learn or use knowledge to achieve their goals. They may be very simple or very complex. A reflex machine, such as a thermostat, is considered an example of an intelligent agent.

A* search:
→ A* combines the value of the heuristic function h(n)and the cost to reach the node ‘n’, g(n).
→ Evaluation function f(n) = g(n) + h(n) thus estimates the cost of the cheapest solution through ‘n’.
→ A* tries the node with the lowest f(n) value first.
→ This leads to both complete and optimal search algorithm, provided that h(n) satisfies certain conditions.
Optimality of A*:
→ A* expands all nodes ‘n’ for which f(n) → However, all nodes n for which f(n) > C* get pruned.
→ It is clear that A* search is complete.
→ A* search is also optimally efficient for any given heuristic function, because any algorithm that does not expand all nodes with f(n) → Despite being complete, optimal, and optimally efficient, A* search also has its weaknesses.
→ The number of nodes for which f(n)< C* for most problems is exponential in the length of the solution.
Reference:
http://www.cs.tut.fi/~elomaa/teach/AI-2011-3.pdf

→ Deterministic AI environments are those on which the outcome can be determined based on a specific state. In other words, deterministic environments ignore uncertainty.
→ Most real world AI environments are not deterministic. Instead, they can be classified as stochastic. Self-driving vehicles are a classic example of stochastic AI processes.

→ Simple reflex agents act only on the basis of the current percept, ignoring the rest of the percept history. The agent function is based on the condition-action rule: "if condition, then action".
→ This agent function only succeeds when the environment is fully observable. Some reflex agents can also contain information on their current state which allows them to disregard conditions whose actuators are already triggered.
→ Infinite loops are often unavoidable for simple reflex agents operating in partially observable environments. Note: If the agent can randomize its actions, it may be possible to escape from infinite loops.

Heuristic Search Strategies:
A key component of an evaluation function is a heuristic function h(n), which estimates the cost of the cheapest path from node ‘n’ to a goal node.
→ In connection of a search problem “heuristics” refers to a certain (but loose) upper or lower bound for the cost of the best solution.
→ Goal states are nevertheless identified: in a corresponding node ‘n’ it is required that h(n)=0
E.g., a certain lower bound bringing no information would be to set h(n) ≅ 0
→ Heuristic functions are the most common form in which additional knowledge is imported to the search algorithm.
Generating admissible heuristics from relaxed problems:
→ To come up with heuristic functions one can study relaxed problems from which some restrictions of the original problem have been removed.
→ The cost of an optimal solution to a relaxed problem is an admissible heuristic for the original problem (does not overestimate).
→ The optimal solution in the original problem is, by definition, also a solution in the relaxed problem.
Example:
→ Heuristic h₁ for the 8-puzzle gives perfectly accurate path length for a simplified version of the puzzle, where a tile can move anywhere.
→ Similarly h₂ gives an optimal solution to a relaxed 8-puzzle, where tiles can move also to occupied squares.
→ If a collection of admissible heuristics is available for a problem, and none of them dominates any of the others, we can use the composite function.
h(n) = max { h₁(n), …, h_m(n) }
→ The composite function dominates all of its component functions and is consistent if none of the components overestimates. Reference:
http://www.cs.tut.fi/~elomaa/teach/AI-2011-3.pdf

→ Challenge-Response authentication is a family of protocols in which one party presents a question ("challenge") and another party must provide a valid answer ("response") to be authenticated.
→ The simplest example of a challenge–response protocol is password authentication, where the challenge is asking for the password and the valid response is the correct password.
→ A more interesting challenge–response technique works as follows. Say, Bob is controlling access to some resource. Alice comes along seeking entry. Bob issues a challenge, perhaps "52w72y". Alice must respond with the one string of characters which "fits" the challenge Bob issued. The "fit" is determined by an algorithm "known" to Bob and Alice. (The correct response might be as simple as "63x83z" (each character of response one more than that of challenge), but in the real world, the "rules" would be much more complex.) Bob issues a different challenge each time, and thus knowing a previous correct response (even if it isn't "hidden" by the means of communication used between Alice and Bob) is of no use.

→ A self-organizing map (SOM) or self-organizing feature map (SOFM) 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.
→ Kohonen map or network is self-organizing map
→ Hopfield nets serve as content-addressable ("associative") memory systems with binary threshold nodes. They are guaranteed to converge to a local minimum, but will sometimes converge to a false pattern (wrong local minimum) rather than the stored pattern (expected local minimum). Hopfield networks also provide a model for understanding human memory.
→ Backpropagation is a method used in artificial neural networks to calculate a gradient that is needed in the calculation of the weights to be used in the network

A Hopfield neural network is a type of artificial neural network invented by John Hopfield in 1982. It usually works by first learning a number of binary patterns and then returning the one that is the most similar to a given input. What defines a Hopfield network: It is composed of only one layer of nodes or units each of which is connected to all the others but not itself. It is therefore a feedback network, which means that its outputs are redirected to its inputs. Every unit also acts as an input and an output of the network. Thus the number of nodes, inputs, outputs of the network are equal. Additionally, each one of the neurons in a has a binary state or activation value, usually represented as 1 or -1, which is its particular output. The state of each node generally converges, meaning that the state of each node becomes fixed after a certain number of updates.

A self-organizing map or self-organizing feature map is a type of artificial neural network that is trained using unsupervised learning to produce a low-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 neighborhood function to preserve the topological properties of the input space.

Multilayer perceptron class of networks consists of multiple layers of computational units, usually interconnected in a feed-forward way. Each neuron in one layer has directed connections to the neurons of the subsequent layer. In many applications the units of these networks apply a sigmoid function as an activation function.
A feedforward neural network is an artificial neural network wherein connections between the nodes do not form a cycle. As such, it is different from recurrent neural networks. The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network.