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

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

Outline Sample projects from this course Sample projects from this course Challenges Challenges

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

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

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

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

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

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

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