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AI · Theory

Agent and Environment

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Theory

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Agent and Environment

The environment is everything outside the agent. Classify it: fully vs partially observable, deterministic vs stochastic, episodic vs sequential, static vs dynamic, discrete vs continuous, single vs multi-agent. Chess is deterministic, discrete, fully observable, multi-agent. Driving is stochastic, continuous, partially observable.

Viva — pick chess vs taxi. Tick the six labels. That table is the whole topic.

Output — idea / do / check for Agent and Environment. Fill those three in the viva.

Diagram
sensors  →  agent  →  actuators
                │
                ▼
            environment
Exam tip

Chess vs taxi on observability + determinism.

Example

# Agent and Environment
print("idea :", "Agent and Environment")
print("do   : one tiny example on paper")
print("check: one failure case")

Output: idea / do / check for Agent and Environment. Fill those three in the viva.

Short notes

  • DefObservable / deterministic / episodic / static / discrete / agents
  • RuleChess vs driving

Questions

1

Explain Agent and Environment as if you are teaching a junior — definition, then one example.

2

What does the example print, and what does that prove?

3

What mistake do freshers make with Agent and Environment?

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