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The Top MenChristian Dell, Joe Hall, Alex Reeser, Landon Rogge, Jason Todd, Jared Whitaker

A-mAIze-in

Slide2Abstract

MazesFind a particular location in a maze and discover a path to the location.Use different h(x) with A* to determine best of the code

Slide3Implementation

Java using awt/swingSelect which test maze to useSelect the heustric to useWill run the selected mode and output raw data on left

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A General PEAS

Agent: Maze Traversing Agent

Performance: Number of moves it takes to solve the maze or find cheese

Environment: Maze

Actuators:

performMovement

function

Sensors:

examineEnvironment

function

Slide8Rules

A* with various h(x)From a starting point, find a path to another pointCannot walk through walls

Slide9Maze Solving

Fully Observable: YesDeterministic: YesEpisodic: YesStatic: YesDiscrete: YesMulti-agent: No

Slide10A* Heuristics Implemented

EuclideanManhattanNumber of Walls (Method 1)Number of Walls (Method 2)Last in List (f(x) = 0; udlr)Dijkstra’s (h(x) = 0)Random Numbers (0-15)

Slide11Number of Walls (Method 1)

Take starting position x1 and y1, take ending position x2 and y2, set wall = 0

Repeat until x1 = x2 and y1 = y2

If x1 > x2, x1 – 1; if x1 < x2, x1 + 1; else x1

If y1 > y2, y1 – 1; if y1 < y2, y1 + 1; else y1

If a wall exists at the new (x1, y1), wall + 1

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X

Slide13Number of Walls (Method 2)

Take starting position x1 and y1, take ending position x2 and y2, set wall = 0

Repeat until x1 =

x2

If x1 > x2, x1

–

1; if x1 < x2, x1 + 1; else x1

If

a wall exists at the new (x1, y1), wall +

1

Repeat until

y1

= y2

If

y1 > y2, y1 – 1; if y1 < y2, y1 + 1; else y1

If a wall exists at the new (x1, y1), wall + 1

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Slide15Last in List

Chooses the last member of the closed set (most recently added)

Results in a unique order – find the most recent expanded node with adjacent unexpanded nodes and select the top, bottom, left, right node to continue down in that order.

Bizzare

, efficient results in some cases. Similar to depth first search. Accidental

mis

-implementation of

Dijkstra’s

which occurs when f(x) = 0 instead of h(x) = 0.

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Slide17Some Results

Euclidean

Manhattan

Walls

1

Walls 2

Last

in List

Dijkstra

Random

89+2

88+2

65+4

57+4

76+2

95+1

94 (100

Avg

)

85+5

87+3

82+8

87+6

59+5

97+4

95

57+2

56+2

40+5

58+4

52+5

75+2

74

Slide18Some images

Maze 1 Maze 1 Solution

Slide19Some images

Maze 1 Random Maze 1 Wall Method 2

Slide20Some images

Maze 2 Maze 2 Solution

Slide21Some images

Maze 2 Last in List Maze 2 Wall Method 2

Slide22Some images

Maze 3 Maze 3 Solution

Slide23Some images

Maze 3 Euclidean Maze 3 Wall

Method 1

Slide24A graph of some sort

Slide25A graph of some sort

Slide26Maze Conclusions

Random Heuristics are useless in generalDijkstra’s fared even worse than random heuristics?Euclidian/Manhattan performance depended on maze, usually not well, many misleading paths in mazesNumber of walls did relatively okayDepth first did well, but likely because of good random positioning of thestarting locationand the cheese

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