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.

Total input to neuron f(x)=(W^TXX)+Ⲑ
=(0.2*0.2)+(-0.1*0.4)+(0.1*0.2)
=0.04-0.04+0.02
=0.02

→ Heuristic search is the best method to go for the game playing problem
→ A heuristic is a method that might not always find the best solution but is guaranteed to find a good solution in reasonable time.
Example: Hill Climbing, Simulated Annealing, Best first search,A* algorithm,etc..,

f(n)=c(n)+h(n)
where
f(n)= Best path to reach the destination node
c(n)=Cost of path
h(n)= heuristic value of a node
Cost of choosing B or C is
=(22+3)+(24+2)
=51
Note: We add cost of B and C because they belongs to AND graph.
Cost of choosing A =42+4=46
Since cost of choosing node choosing B or C that’s why we will expand ‘A’.

In Artificial Intelligence (AI), Sequence of levels is present in the planning graph.
→ A planning graph consists of a sequence of levels that correspond to time steps in the LEVELS plan, where level 0 is the initial state.
→ Each level contains a set of literals and a set of actions.
→ The literals are all those that could be true at that time step, depending on the actions executed at preceding time steps.
→ The actions are all those actions that could have their preconditions satisfied at that time step, depending on which of the literals actually hold.
→ The planning graph records only a restricted subset of the possible negative interactions among actions i.e.., it might be optimistic about the minimum number of time steps required for a literal to become true.
→ This number of steps in the planning graph provides a good estimate of how difficult it is to achieve a given literal from the initial state.
→ The planning graph is defined in such a way that it can be constructed very efficiently.
→ Planning graphs work only for propositional planning problems-ones with no variables.

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))

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

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.