UGC NET Computer Science Artificial Intelligence (AI) Flashcards

24 sample cards from the Artificial Intelligence (AI) deck

Sampled from the end of the deck, so these are different cards from the ones shown on the syllabus page.

1. What is the generic structure of a multiagent system (MAS)?

   A collection of autonomous agents, each with limited local knowledge/viewpoint, situated in a shared environment, interacting via communication and coordination mechanisms. There is no global control; agents cooperate, negotiate, or compete to achieve individual/collective goals.

2. What is the Semantic Web?

   An extension of the Web (Tim Berners-Lee) where information is given well-defined, machine-understandable meaning using standards like RDF, RDFS, and OWL, enabling agents to process and integrate data automatically through ontologies and linked data.

3. What is an Agent Communication Language (ACL), and name two examples.

   A standardized language enabling agents to exchange knowledge and coordinate, based on speech-act theory (performatives like inform, request, query). Examples: KQML (Knowledge Query and Manipulation Language) and FIPA-ACL.

4. How are ontologies used for knowledge sharing among agents?

   A shared ontology provides a common vocabulary and agreed meanings for terms, so that messages exchanged between heterogeneous agents are interpreted consistently. This ensures semantic interoperability and reuse of knowledge across agents and systems.

5. Name some agent development tools/platforms.

   JADE (Java Agent Development Framework, FIPA-compliant), JACK, ZEUS, AgentBuilder, MadKit, and FIPA-OS. JADE is the most widely used, providing middleware and a runtime for building distributed multi-agent systems.

6. What is the notion of fuzziness, and how do fuzzy sets differ from classical sets?

   Fuzziness models vagueness where membership is a matter of degree. In a classical (crisp) set membership is binary (0 or 1); in a fuzzy set (Zadeh) an element has a membership degree μ(x) in the continuous interval [0,1].

7. What is a membership function in fuzzy logic, and name common shapes.

   A function μ_A(x): X → [0,1] that assigns each element a degree of membership in fuzzy set A. Common shapes: triangular, trapezoidal, Gaussian, sigmoidal, and singleton.

8. Define fuzzification and defuzzification.

   Fuzzification converts crisp input values into fuzzy membership degrees over linguistic terms. Defuzzification converts the aggregated fuzzy output back into a single crisp value (e.g., via centroid/center-of-gravity, mean-of-maxima, or weighted-average methods).

9. State the standard fuzzy set operations for union, intersection, and complement.

   For fuzzy sets A and B: Union μ_{A∪B}(x) = max(μ_A(x), μ_B(x)); Intersection μ_{A∩B}(x) = min(μ_A(x), μ_B(x)); Complement μ_{Ā}(x) = 1 − μ_A(x).

10. What is a linguistic variable in fuzzy systems?

    A variable whose values are words/terms (linguistic terms) rather than numbers, e.g., 'temperature' with values {cold, warm, hot}, each defined by a fuzzy set. It is characterized by a name, set of linguistic terms, universe of discourse, and membership functions.

11. What is a fuzzy relation, and how are fuzzy relations composed?

    A fuzzy relation R(X,Y) assigns a membership degree to each pair (x,y), generalizing crisp relations. Fuzzy relations are combined using max-min composition: μ_{R∘S}(x,z) = max_y [min(μ_R(x,y), μ_S(y,z))].

12. What is a fuzzy rule, and what are the steps of fuzzy inference?

    A fuzzy rule has the form IF x is A THEN y is B (antecedent and consequent are fuzzy sets). Inference (e.g., Mamdani): fuzzify inputs, apply rules (evaluate antecedents, implication), aggregate rule outputs, then defuzzify to a crisp output.

13. What are the main components of a fuzzy (rule-based) control system?

    A fuzzifier (crisp→fuzzy), a knowledge base/rule base (fuzzy IF-THEN rules and membership functions), an inference engine (applies rules), and a defuzzifier (fuzzy→crisp). Mamdani and Sugeno are the two common fuzzy inference models.

14. What are common encoding (representation) strategies in Genetic Algorithms?

    Binary encoding (bit strings), value/real-valued encoding, permutation encoding (for ordering problems like TSP), and tree encoding (for genetic programming). The encoding defines how candidate solutions are represented as chromosomes.

15. What are the three main genetic operators in a GA?

    Selection (choosing fitter individuals to reproduce, e.g., roulette-wheel, tournament, rank), Crossover/recombination (combining parent chromosomes, e.g., one-point, two-point, uniform), and Mutation (random small changes to maintain diversity).

16. What is a fitness function, and what are the steps of the GA cycle?

    A fitness function evaluates how good a candidate solution is (the objective to optimize). GA cycle: initialize population → evaluate fitness → selection → crossover → mutation → form new generation → repeat until a termination criterion is met.

17. How is a problem solved using a Genetic Algorithm?

    Encode candidate solutions as chromosomes, define a fitness function, initialize a random population, then iteratively select parents and apply crossover and mutation to evolve better solutions across generations, stopping when convergence or a fitness threshold is reached.

18. What is supervised learning? Give example tasks and algorithms.

    Learning a mapping from inputs to outputs using labelled training data (input-output pairs). Tasks: classification and regression. Algorithms: decision trees, SVM, k-NN, naive Bayes, linear/logistic regression, neural networks.

19. What is unsupervised learning? Give example tasks.

    Learning patterns/structure from unlabelled data without target outputs. Tasks: clustering (e.g., k-means, hierarchical), association rule mining, and dimensionality reduction (e.g., PCA). The system discovers hidden groupings or relationships.

20. What is reinforcement learning, and what are its key elements?

    Learning by interacting with an environment to maximize cumulative reward through trial and error. Key elements: agent, environment, states, actions, reward signal, policy, and value function. Examples include Q-learning and SARSA.

21. What is a single perceptron, and what is its key limitation?

    A single-layer neural unit computing output = activation(Σ wᵢxᵢ + b), used as a linear binary classifier trained by the perceptron learning rule. Limitation: it can only separate linearly separable data, so it cannot solve the XOR problem.

22. What is a Multi-Layer Perceptron (MLP) and how is it trained?

    A feedforward neural network with one or more hidden layers of nonlinear units between input and output, able to learn non-linearly separable functions (e.g., XOR). It is trained by the backpropagation algorithm using gradient descent to minimize error.

23. What is a Self-Organizing Map (SOM)?

    An unsupervised competitive-learning neural network (Kohonen) that maps high-dimensional input onto a low-dimensional (usually 2D) grid while preserving topology. The Best Matching Unit and its neighbours have weights adjusted toward each input, forming clusters.

24. What is a Hopfield network and what is it used for?

    A recurrent, fully connected single-layer network with symmetric weights and no self-connections, acting as associative (content-addressable) memory. It stores patterns as stable states and converges to the nearest stored pattern by minimizing an energy function.

What this deck covers

Artificial Intelligence (AI) flashcards FAQ