Alexander Repenning artificial intelligence chapter 1: Game AI.

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Presentation transcript:

Alexander Repenning artificial intelligence chapter 1: Game AI

Objectives learn about difference between AI and Game AI learn about a new AI approach called Collaborate Diffusion

Submission of Sokoban 3D groups: group3, abc, 5monkeys, discovery channel, denogginators if you’re not on this list give name to Jenny NOW! / /projects/sokoban3d/ Screendump.jpg Readme.txt (use links to more information).zip Zip complete folder containing.exe, resources,..

game AI single Agent ALife: agent acts intelligent: develops goals based on needs, pursues goals. path finding (e.g., A*): artificial opponents finds ways trough maze to get you Sims: find refrigerator in house and food inside learning: artificial opponents learn about your behavior making game play progressively harder multi Agents flocking, emergence collaboration

Computational: AI needs to “run” at 60 frames per second symbolic AI is (mostly) non-incremental Psychological: AI needs to “look” right often very simple, e.g., random, e.g. Mt. Vetro’s eyes challenges

more pointers: good site: new book: AI for Game Developers, David M. Bourg

how to track Pacman?

ideas Diffusion Search: combine the notion of diffusion (a formal conceptualization on how things spread) with Search, e.g., hill climbing Collaborate Diffusion: use Diffusion Search in a multi agent setting to express collaboration and competition

diffusion (physics) the process of diffusing; the intermingling of molecules in gases and liquids as a result of random thermal agitation the spread of social institutions (and myths and skills) from one society to another dissemination: the property of being diffused or dispersed dispersion: the act of dispersing or diffusing something; "the dispersion of the troops"; "the diffusion of knowledge" The movement of chemical species (ions or molecules ) under the influence of concentration difference. The species will move from the high concentration area to the low concentration area till the concentration is uniform in the whole phase. Diffusion in solutions is the most important phenomenon in electrochemistry, but diffusion will occur also in gases and solids. electrochem.cwru.edu/ed/dict.htm the movement of particles from an area of higher concentration to an area of lower concentration coris.noaa.gov/glossary/glossary_a_k.html

well suited for complex, multi-agent simulation game: path finding, ALife, flocking, emergence and collaboration new: developed at CU, started on Connection Machine computationally expensive but at the same time incremental: works well on current computers and as part of game engines traditional game AI (e.g., A* for pathfinding) approaches are not incremental Collaborative Diffusion

characteristics Spatial Extend: works for agents with spatial relationships (2D, 3D, connection machine: 12D) Simple to Program: algorithms are computationally expensive but relatively simple to built and tweak. Ecological traditional AI: AI in agent, e.g., robot distributed AI: AI in agents ⇒ flocking... ecological AI: AI everywhere: agents & environment Parallel: no chess-like turn taking Incremental: AI state is part of environment and continuously updated Robust: likely to work with situations not anticipated, e.g., soccer with n goals, m balls for n, m ≠ 2

1) Static Tracking: single agent, fixed goal 1a) no obstacles 1b) obstacles: e.g., Sims, Pacman 2) Dynamic Tracking: single agent, moving goal, no obstacles 3) Dynamic Path Finding: single agent, obstacles 4) Collaborative Problem Solving: multiple collaborating agents, multiple moving goals, changing goals, obstacles, competing agents Levels of Collaborate Diffusion

Goals: one static goal agent defining static goal value Trackers: one hill climbing agent Environment: backgrounds: agents diffusing values no obstracles 1) Static Tracking

u0 = D (u1 + u2 +u3 +u4 - 4u0) + u0 D: Diffusion coefficient [0..0.5] simple: D = 0.25 => u0 = 0.25 *(u1 + u2 + u3 + u4) diffusion equation u0u3 u2 u4 u1

multiple collaborative agents collaborating: soccer, players from the same team competing: soccer, players from the other team changing goals: first track ball, then kick ball into goal simple version: Collaboration trough Goal Obfuscation 4) Collaborative Problem Solving

World Cup

sample projects MySims: a version of the Sims The Madness of Crowds: how people behave in panic