Auto Topic: master
auto_master | topic
Coverage Score
1
Mentioned Chunks
19
Mentioned Docs
6
Required Dimensions
definitionpros_cons
Covered Dimensions
definitionpros_cons
Keywords
master
Relations
| Source | Type | Target | W |
|---|---|---|---|
| Auto Topic: master | CO_OCCURS | Auto Topic: self | 9 |
| Auto Topic: master | CO_OCCURS | Auto Topic: tkinter | 9 |
| Auto Topic: def | CO_OCCURS | Auto Topic: master | 9 |
| Auto Topic: master | CO_OCCURS | Auto Topic: padx | 7 |
| Auto Topic: master | CO_OCCURS | Auto Topic: rows | 6 |
| Auto Topic: col | CO_OCCURS | Auto Topic: master | 6 |
| Auto Topic: cols | CO_OCCURS | Auto Topic: master | 6 |
| Auto Topic: master | CO_OCCURS | Auto Topic: row | 5 |
| Auto Topic: master | CO_OCCURS | Auto Topic: pack | 5 |
| Auto Topic: fill | CO_OCCURS | Auto Topic: master | 5 |
| Auto Topic: master | CO_OCCURS | Inference | 4 |
| Auto Topic: master | CO_OCCURS | Auto Topic: menu | 4 |
| Auto Topic: canvas | CO_OCCURS | Auto Topic: master | 4 |
| Auto Topic: master | CO_OCCURS | Propositional Logic | 3 |
| Auto Topic: master | CO_OCCURS | Logical Agents | 3 |
| Auto Topic: master | CO_OCCURS | Auto Topic: scene | 3 |
| Auto Topic: master | CO_OCCURS | Auto Topic: start_and_goal | 3 |
| Auto Topic: master | CO_OCCURS | Auto Topic: perform_move | 3 |
| Auto Topic: import | CO_OCCURS | Auto Topic: master | 3 |
| Auto Topic: get_board | CO_OCCURS | Auto Topic: master | 3 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
assignments CIS5210-Assignments/M4/homework4_dominoes_game_gui.py | 0.63 | 5 | ... t sys import tkinter import homework4 class Square(tkinter.Canvas): COLOR_EMPTY = "white" COLOR_FILLED = "gray50" def __init__(self, master, size=50): tkinter.Canvas.__init__(self, master, height=size, width=size, background=Square.COLOR_EMPTY, highlightthickness=2, highlightback ... |
assignments CIS5210-Assignments/M5/homework5_sudoku_gui.py | 0.63 | 5 | ... me): """ The Tkinter UI, responsible for drawing the board and accepting user input. """ def __init__(self, master, game): self.game = game self.master = master Frame.__init__(self, master) self.startUI() def startUI(self): self.master.title("Sudoku") self.pack(fill=BOTH, expand= ... |
assignments CIS5210-Assignments/M3/homework3_tile_puzzle_gui.py | 0.61 | 4 | ... mport tkinter import homework3 class Tile(tkinter.Canvas): BACKGROUND_NORMAL = "white" BACKGROUND_EMPTY = "black" def __init__(self, master, tile, size=60): tkinter.Canvas.__init__(self, master, height=size, width=size, highlightthickness=2, highlightbackground="black") self.text ... |
assignments CIS5210-Assignments/M4/homework4_dominoes_game_gui.py | 0.59 | 3 | ... row, col, self.vertical): self.game.perform_move(row, col, self.vertical) self.vertical = not self.vertical self.update_squares() self.master.update_status() def update_squares(self): game_board = self.game.get_board() for row in range(self.rows): for col in range(self.cols): sel ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... ess milestones have been marked by successive winners of the Fredkin Prize: B ELLE (Condon and Thompson, 1982), the first program to achieve master status; DEEP THOUGHT (Hsu et al., 1990), the first to reach international master status; and Deep Blue (Campbell et al., 2002; Hsu, 20 ... |
assignments CIS5210-Assignments/M3/homework3_grid_navigation_gui.py | 0.57 | 2 | import sys import tkinter import homework3 class Grid(tkinter.Canvas): def __init__(self, master, scene, start_and_goal): self.rows, self.cols = len(scene), len(scene[0]) self.square_size = min(40, 500 / self.rows, 500 / self.cols) tkinter.Canvas.__init__(self, master, height=sel ... |
assignments CIS5210-Assignments/M3/homework3_grid_navigation_gui.py | 0.57 | 2 | ... ="path") def clear_paths(self): self.delete("path") class GridNavigationGUI(tkinter.Frame): def __init__(self, master, scene): tkinter.Frame.__init__(self, master) self.scene = scene self.start_and_goal = [None, None] self.grid = Grid(self, scene, self.start_and_goal) self.grid.p ... |
assignments CIS5210-Assignments/M3/homework3_tile_puzzle_gui.py | 0.57 | 2 | ... f.animate_moves(moves[1:], delay=delay) stage_1() class TilePuzzleGUI(tkinter.Frame): def __init__(self, master, rows, cols): tkinter.Frame.__init__(self, master) self.rows = rows self.cols = cols self.puzzle = homework3.create_tile_puzzle(rows, cols) |
assignments CIS5210-Assignments/M3/homework3_tile_puzzle_gui.py | 0.57 | 2 | ePuzzleGUI(tkinter.Frame): def __init__(self, master, rows, cols): tkinter.Frame.__init__(self, master) self.rows = rows self.cols = cols self.puzzle = homework3.create_tile_puzzle(rows, cols) self.board = Board(self, self.puzzle, rows, cols) self.board.pack(side=tkinter.LEFT, pa ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... sus-king (KRK) endgame by following a few simple rules. Other endings, such as king, bishop, and knight versus king (KBNK), are difficult to master and have no succinct strategy description. A computer, on the other hand, can completelysolve the endgame by producing a policy, whic ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... s of the world that each approximate the dynamics in a region of the state space, an approach that has been successful in getting robots to master complex dynamic tasks like juggling. A model of the world can also be useful in reducing the sample complexity of model-free reinforc ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... l, when ordered, or even of its own accord, could do the work that befits it. . . then there would be no need either of apprentices for the master workers or of slaves for the lords.” In the 3rd century BCE an actual humanoid robot called the Servant of Philon could pour wine or w ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... ions in Psy- chological Science, 23, 60–66. Agerbeck, C. and Hansen, M. O. (2008). A multi- agent approach to solving NP-complete problems. Master’s thesis, Technical Univ. of Denmark. Aggarwal, G., Goel, A., and Motwani, R. (2006). Truthful auctions for pricing search keywords. ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... 29, 103–30. Domingos, P. (2012). A few useful things to know about machine learning. Commun. ACM, 55(10), 78– 87. Domingos, P. (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. Dong, X., Gabrilovich, E., Heitz, G., ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... ciety of America A, 8, 377–385. Koenig, S. (1991). Optimal probabilistic and decision- theoretic planning using Markovian decision theory. Master’s report, Computer Science Division, Univer- sity of California, Berkeley. Koenig, S. (2000). Exploring unknown environments with real ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | an decision theory. Master’s report, Computer Science Division, Univer- sity of California, Berkeley. Koenig, S. (2000). Exploring unknown environments with real-time search or reinforcement learning. In NeurIPS 12. Koenig, S. (2001). Agent-centered search. AIMag, 22, 109–131. Ko ... |
assignments CIS5210-Assignments/M3/homework3_grid_navigation_gui.py | 0.55 | 1 | f, master) self.scene = scene self.start_and_goal = [None, None] self.grid = Grid(self, scene, self.start_and_goal) self.grid.pack(side=tkinter.LEFT, padx=1, pady=1) menu = tkinter.Frame(self) tkinter.Label(menu, text="Left click to specify the start point.").pack( padx=1, pady=1 ... |
module_resources Module Resources/07-Logical-Agents (1).pdf | 0.55 | 1 | ... at eats any agent that enters its room. Some rooms contain bottomless pitsthat trap any agent that wanders into the room. In one room is master Luke.The goal is:•collect Luke•exit the world •without being eaten |