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Exercise 2.2 asks you to design agents for these cases.įigure 2.2 A vacuum-cleaner world with just two locations.Your IP address has been temporarily blocked due to a large number of HTTP requests. If the geography of the environment is un- known, the agent will need to explore it rather than stick to squares A and B. If clean squares can become dirty again, the agent should occasionally check and re-clean them if needed. A better agent for this case would do nothing once it is sure that all the squares are clean. For example, once all the dirt is cleaned up, the agent will oscillate needlessly back and forth if the performance measure includes a penalty of one point for each movement left or right, the agent will fare poorly.
One can see easily that the same agent would be irrational under different circum- stances. We claim that under these circumstances the agent is indeed rational its expected perfor- mance is at least as high as any other agent's. The agent correctly perceives its location and whether that location contains dirt.The only available actions are Left, Right, and Suck.The Left and Right actions move the agent left and right except when this would take the agent outside the environment, in which case the agent remains where it is. Clean squares stay clean and sucking cleans the current square. The "geography" of the environment is known a priori (Figure 2.2) but the dirt distri- bution and the initial location of the agent are not. The performance measure awards one point for each clean square at each time step, over a "lifetime" of 1000 time steps. Is this a rational agent? That depends! First, we need to say what the performance measure is, what is known about the environment, and what sensors and actuators the agent has. Consider the simple vacuum-cleaner agent that cleans a square if it is dirty and moves to the other square if not this is the agent function tabulated in Figure 2.3.
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