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Carlos Barboza Kenny Barron Kevin Cherry Tung Le Daniel Lorio.

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Presentation on theme: "Carlos Barboza Kenny Barron Kevin Cherry Tung Le Daniel Lorio."— Presentation transcript:

1 Carlos Barboza Kenny Barron Kevin Cherry Tung Le Daniel Lorio

2  Avoid enemy ships as they chase after you  Every second alive adds points (10)  Killing an enemy adds points (100)  Player can wrap around screen, enemies can’t  Enemy can only propel forward, player can move forward and in reverse

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4  Maximize agent’s performance in fully- dynamic, multi-agent environment with limited knowledge of environment  Determine performance through the use of different AI algorithms and parameters  Performance is gauged by score at end of game

5  User controlled player  Limited ship following  Disorganized in pursuit of player ship

6  Partially Observable – Limited to set regions  Strategic – Moves based on location of enemy  Episodic – Experience based on perception of enemy  Dynamic – Enemy constantly moving/regenerating  Discrete – Agent responds with set action based on perception of enemies in viewed regions  Multi-Agent – Steering algorithm applied to enemies, and pathfinding for player P – ScoreE – Grid A – Moves S - Grid Regions

7  Adapted open source project OpenSteer in C#  Enemy determines velocity and direction based on players location  Steer for seek allows enemies to converge around player  Steer for flee allows enemies to distance themselves away from player in any direction

8  Loosely based on A* Pathfinding  Combines heuristic, actual cost, and utility function to quantitate each move choice  Agent chooses maximum move value

9  1 st Agent was a simple reflex agent, not partially observable, random movements and actions, and it was not rational  2 nd Environment was partially observable, agent was a simple reflex agent, and partially rational  3 rd Agent was goal based agent, partially observable environment, and partially rational

10  Creates grid to discretize the game world  Each grid cell has bitmask that holds information on cell contents  If enemy is in cell  If cell is part of a region

11  Creates 5 regions from grid  Multiplies enemy presence with proximity to player in each region using a cubic scale.  A – Accelerates Forward  F – Flees Backward  L – Turns Left  R – Turns Right  S – Shoots L R S F A F A

12  Concept of look ahead implemented as a move tree for actual score  1 point per move simulates survival time  Prune on dead state (count dead states)  Total dead states counted for each move’s subtree  Final move score:  MaxDescendentScore * W 1 - TotalDescendentDeadStates * W 2 root L L A A S S F F R R L L A A S S F F R R...

13  Acts as a multiplier for move values, and tilts the behavior of the player towards passive or aggressive  A = 1.4  B = 1.0  F = 1.2  L = 0.8  R = 0.8  S = 1.2

14  f(m) = (h(m) + g(m)) * u(m)  where m = move  Overview:  h(m): Evaluate for each move and choose the one with the largest value  g(m): Simulate gameState for each child, prune dead states, and select move with highest  u(m): Utility function adds custom factor to each value

15 AVGBEST H,U(8) H(8) H,U - EC(4) H,U - Double(8) H - Double(8) H - Double(4)

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