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Applications for Gaming in AI

Applications for Gaming in AI. Sample Projects from Computational Intelligence Course at Washburn University. Outline. Sample projects from this course Challenges. Applications of Informed Search.

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Applications for Gaming in AI

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  1. Applications for Gaming in AI Sample Projects from Computational Intelligence Course at Washburn University

  2. Outline • Sample projects from this course • Challenges

  3. Applications of Informed Search • Build Game Board where Predator is Searching a matrix looking for least cost path to Prey • Task Environment is fully observable • Both Single and Multi-Agent Implementations [i.e. both predator and prey are moving] • A* • Idea: avoid expanding paths that are already expensive • Evaluation function f(n) = g(n) + h(n)

  4. Applications of Informed Search • Build a Corn Maze where agent finds its way through the maze • LRTA* • Used to solve problems where planning and action are interleaved and environment is safely-explorable • Search to find the optimal solution to a randomly selected scrambled Rubik's Cube • Iterative Deepening A* (IDA*)

  5. Applications of Optimization Algorithms • N-Queens • Place n=8 queens on board with no attacking queens • Hill Climbing • Successor function generates 64 new boards • Pick the best new board • Beam Search - Pick best k moves • Genetic Algorithms • Successor function applies Fitness Function, Cross-Over, and Mutation to generate new population of moves

  6. Applications of Adversarial Search • Tic-Tac-Toe • MiniMax [with Alpha-Beta Pruning] • Setting a cutoff where levels can be novice through Master Level • Mastermind • Please don’t ask me questions about this game… student is currently researching

  7. Applications of Machine Learning • You enter how you would vote on a set of legislative bills and I [the computer] will predict your political party • Naïve Bayes • Guess your Cartoon Character based on the answer to twenty questions • Nearest Neighbor

  8. Challenges • Understanding is not necessarily trivial • Significant career opportunities in emerging fields that are not just related to gaming • [e.g. Learning Science and Web Science]. • The challenge • Develop the proper pedagogy and scaffolding that will support student learning of these concepts. • Course needs to be adaptable to meet the needs of many types of students

  9. References • [1] American Association for Artificial Intelligence, 2006, Games and Puzzles, http://www.aaai.org/AITopics/html/games.html, retrieved December 6, 2006 • [2] Russell S. and Norvig R., Artificial Intelligence a Modern Approach, 2ed., 2003, Pearson Education, Inc. • [3] Bourg D. M. and Seemann G., AI For Game Developers, 2004, O’Reilly Media, Inc

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