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--- courses:hcai:lab_agents2 [2026/09/29 21:24] – [Challenge] kkt
+++ courses:hcai:lab_agents2 [2026/09/29 21:34] (current) – removed kkt
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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:** [[https://colab.research.google.com/drive/17naExyctS-SRUIJFCKeel16GDaDNNUfa?usp=sharing|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 [[https://www.codingame.com/multiplayer/bot-programming/code4life|Code4Life]].
-Create an agent that controls a laboratory robot collecting samples and molecules to produce medicines. Start with a baseline state-machine workflow:
-<code>collect samples -> diagnose -> collect molecules -> produce medicine</code>
-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.
-==== Learn more! ====
-  * FIXME