Assessment due date is $2024-09-25,23$:$59$ IST

Last recorded submission : $2024-09-13,16$:$42$ IST

$1$ point

Assertion A : In real$-$world domains, agents have to deal with both incomplete and incorrect information.

Reason R : Incompleteness arises because the world does not necessarily match the agent's model of it$.$

A$.$ Both A and R are true and R is the correct explanation for A

B$.$ Both A and R are true but R is not the correct explanation for A

C$.$ A is True but R is False

D$.$ A is false but R is True

$1$ point

$\_\_\_\_\_\_\_$ provides ways and means of weighing up the desirability of goals and the likelihood of achieving them.

A$.$ Decision Theory

B$.$ Utility Theory

C$.$ Probability Theory

D$.$ Bayesian Networks

$1$ point

$\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_$ provides a general, concise representation for large POMDP, so they can be used as inputs for any POMDP algorithm including value and policy iteration methods.

A$.$ Bayesian Networks

B$.$ Decision Networks

C$.$ Dynamic Decision Networks

D$.$ Dynamic Belief Networks

$1$ point

STRIPS is an alternative representation to the pure situation calculus for planning.

I. Hierarchical Plans cannot be expressed in STRIPS.

II$.$ STRIPS operators are essentially propositional.

III. Real world projects need a better model of time than that in STRIPS.

Which of the above statements are correct?

A$.$ Only Statements I and II

B$.$ Statements I, II and III

C$.$ Only Statements II and III

D$.$ None.

$1$ point

A Markov Decision Process is a four tuple $,$ where S is the finite set of states, A is the finite set of actions and R is the cost or reward being in state s$.$ T is the transition model which specifies.

A$.$ Probability of executing action a in state s at time

B$.$ Probability of at time t$+1,$ given action a in state s at time t$.$

C$.$ Probability of s at time t$,$ given actions in states upto time t$-1$

D$.$ Probability of executing action a in state at time t$+1$

$1$ point

Sequential decision problems, which include utilities, uncertainty, and sensing$,$ generalize the search and planning problems. Transition model refers to the following:

A$.$ Set of probabilities associated with the possible transitions between states after any given action.

B$.$ A complete mapping from states to actions.

C$.$ Function be specified for the agent in order to determine the value of an action.

D$.$ Specification of the outcome probabilities for each action in each possible state.

$1$ point

Value Iteration is an algorithm for computing an optimal policy. The basic idea includes

A$.$ Computing the utility of each state.

B$.$ Use state utilities to select an optimal action in each state.

C$.$ Calculate a new Maximum Expected Utility policy based on computed utilities.

D$.$ Start with a random policy and calculate utilities based on if that policy were executed.

$1$ point

Assertion A: In complex real$-$world projects, it is common to use scheduling tools from Operations Research.

Reason R : Scheduling tools essentially take a hand constructed complete partial$-$order plan and generate an optimal schedule for it$.$

A$.$ Both A and R are true and R is the correct explanation for A

B$.$ Both A and R are true but R is not the correct explanation for A

C$.$ A is True but R is False

D$.$ A is false but R is True

$1$ point

Policy Iteration is an alternate algorithm for computing an optimal policy. The basic idea includes

A$.$ Computing the utility of each state.

B$.$ Use state utilities to select an optimal action in each state.

C$.$ Calculate a new Maximum Expected Utility policy based on computed utilities.

D$.$ Start with a random policy and calculate utilities based on if that policy were executed.

$1$ point

Assertion A : The sensor model in a belief network is the Conditional Probability Table $($CPT$)$ associated with the percept node.

Reason R : If the sensor gives a perfect report of the state, then the sensor model $($the CPT$)$ will be purely deterministic.

A$.$ Both A and R are true and R is the correct explanation for A

B$.$ Both A and R are true but R is not the correct explanation for A

C$.$ A is True but R is False

D$.$ A is false but R is True

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