Explain the concepts of partially and fully observable environments, factors of agent rationality, FOL sentences, BFS vs DFS, and simple reflex agents.

PART-III

a) A partially observable environment is one where the agent's sensors do not provide access to the complete state of the environment at any given time. A fully observable environment is one where the agent's sensors give it access to the complete state of the environment at every instant.

b) The factors that affect the rationality of an agent include the performance measure, the agent's prior knowledge of the environment, the actions the agent can perform, and the agent's percept sequence to date.

c) The following sentences in First-Order Logic (FOL):

• i) Some students watch all types of sports. $$\exists x (\text{Student}(x) \land \forall y (\text{Sport}(y) \implies \text{Watches}(x, y)))$$
• ii) All the students register in AI subject. $$\forall x (\text{Student}(x) \implies \text{RegistersIn}(x, \text{AI}))$$

d) Breadth-First Search (BFS) explores all nodes at the current depth level before moving to the next. It uses a queue and guarantees the shortest path in terms of edges. Depth-First Search (DFS) explores as far as possible along each branch before backtracking. It uses a stack and does not guarantee the shortest path.

e) The structure of a simple reflex agent consists of condition-action rules. It selects actions based solely on the current percept, ignoring any past percept history. It essentially maps percepts directly to actions.

f) A production system is a rule-based system that uses "if-then" rules to model problem-solving. Its components are:

• Production Rules: A set of "if-then" rules.
• Working Memory: A database of facts or beliefs.
• Interpreter: A control strategy that decides which rules to apply and when.

h) Hill climbing search is a local search algorithm that continuously moves in the direction of increasing value (uphill) to find a local optimum.
Steps: 1. Start with an arbitrary state. 2. Evaluate the current state. 3. Generate neighbor states. 4. Select the neighbor with the highest value. 5. If the neighbor's value is higher, move to it and repeat. Otherwise, stop.

i) Unification is a process of finding substitutions for variables that make two logical expressions