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May 30, 2019 · In this article, we are going to study about the vacuum cleaner problem in AI. What it is, what type of agent acts in this problem, what goals the agent in this problem has and how all the working takes place in solving this problem?
Developed a simple reflex agent program in Python for the vacuum-cleaner world problem. This program defines the States, Goal State, Goal Test, Actions, Transition Model, and Path Cost.
Intelligent Agents. Philipp Koehn. 8 February 2024. Agents and Environments. Agents include humans, robots, softbots, thermostats, etc. The agent function maps from percept histories to actions: f : P ! The agent program runs on the physical architecture to produce f. Vacuum Cleaner World. Percepts: location and contents, e.g., [A; Dirty]
Example 3. Vacuum World. We have a vacuum cleaner and two rooms. Each room may or may not be dirty. The vacuum cleaner can move left or right (the action has no effect if there is no room to the right/left). The vacuum cleaner can suck; this cleans the room (even if the room was already clean). Physical states
Vacuum Cleaner World. AB. CISC4/681 Introduction to Artificial Intelligence 6. • Percepts: which square (A or B); dirt? • Actions: move right, move left, suck, do nothing • Agent function: maps percept sequence into actions • Agent program: function’s implementation • How should the program act? 2 Rational Agent – does the right thing.
Oct 19, 2021 · 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 as follows. Percept sequence Action. [A,Clean] Right. [A,Dirty] Suck. [B,Clean] Left.
(University of Freiburg) Foundations of AI April 27, 2012 10 / 47 The Vacuum Cleaner Problem If the environment is completely observable, the vacuum cleaner always knows where it is and where the dirt is. The solution then is reduced to searching for a path from the initial state to the goal state. R L S S S R L R L R L S S S S L L L L R R R R
