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Lab: Agents
Before the lab
- Reading:
- AIMA: Chapter 2 - Intelligent Agents. Focus on agents and environments, rationality and performance measures, PEAS descriptions, properties of task environments, and the differences between simple reflex, model-based, goal-based, and utility-based agents.
- Technical preparation:
- Open today's notebook and run all setup cells and the first code cell in Segment 1 to verify that the notebook works in your environment. You should see a two-location Vacuum World.
Q&A
- Consider a robotic vacuum cleaner. What belongs to the agent, what belongs to the environment, and what are its percepts and actions?
- A vacuum agent moves left even though the right location is dirty. Does this prove that the agent is irrational? What information would you need before deciding?
- What changes when the vacuum can perceive only whether its current location is clean or dirty, but cannot perceive its location or the condition of other locations?
- Suppose the vacuum receives one point whenever it removes dirt. How might it exploit this performance measure, and how should the measure be redesigned?
Notebook
Notebook: HCAI_Lab_Agents.ipynb (Work in Google Colab or download the .ipynb file to local environment)
Work in groups of 2-4. Every group member must be able to explain the results.
Challenge
Work in groups of 2-4 on Code4Life.
Create an agent that controls a laboratory robot collecting samples and molecules to produce medicines. Start with a baseline state-machine workflow:
collect samples -> diagnose -> collect molecules -> produce medicine
Then improve the agent by considering sample feasibility, storage capacity, available molecules, travel time, expertise, and the competing robot.
Consider which agent type best describes your solution: simple reflex, model-based, goal-based, or utility-based.
The challenge should be started during the lab and completed as part of the self-study regarding classes.
Selected groups may briefly present their strategies at the beginning of the next lab, and particularly effective solutions may receive bonus points.
