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by Randy Coulman Ricochet Robots is a puzzle board game for any number of players. While being a very fun game to play with some fascinating properties, it is also interesting to think about writing a program to play the game. Let’s discuss a computerized player for Ricochet Robots that finds the optimal solution to any board in a reasonable amount of time. Along the way, we’ll learn about graph search techniques, data representation, algorithms, heuristics, pruning, and optimization. Help us caption & translate this video! http://amara.org/v/GVgi/
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In this video, Randy Coulman presents a detailed exploration of developing a solver for the board game Ricochet Robots, which involves using various computer science concepts to automate gameplay solutions. The primary aim is to create a computerized player that can efficiently determine optimal moves within the game. Key points discussed throughout the presentation include: - **Introduction to Ricochet Robots**: A brief overview of the game, which consists of navigating robots to specific colored target cells on a 16x16 grid, emphasizing the movement rules where robots only stop upon hitting obstacles. - **Problem Space Characterization**: Coulman outlines the staggering number of possible board states (over 976 billion) and the challenges posed by maximum branching factors in terms of potential robot moves. - **Data Representation**: He discusses how to represent board states effectively, combining the goal cell and robot positions into a manageable format. - **Graph vs. Tree Structures**: The presentation distinguishes between tree structures and graphs, highlighting the implications of cycles in state spaces when searching for solutions. - **Search Algorithms**: Coulman introduces basic algorithms such as Depth-First Search (DFS) and Breadth-First Search (BFS), explaining their implementation, advantages, and limitations. - **Heuristics and Optimizations**: He emphasizes the importance of heuristics in improving the solver's performance, such as prioritizing the active robot movement and pre-computing stopping points for rapid assessments. - **Performance Testing and Results**: The improvement of the solver's speed through various optimizations is discussed, backed by performance testing showcasing a significant reduction in solving time. - **Potential Future Enhancements**: Plans for refining the solver include exploring the A* algorithm, leveraging heuristics for better estimation, and optimizing object handling to improve processing times. - **Community Engagement**: Coulman encourages community involvement through the availability of the solver’s code on GitHub, inviting collaboration and further experimentation. In conclusion, the talk illustrates not only the complexities of programming a game solver but also the invaluable lessons learned in the process, highlighting the intersection of real-world applications and game theory in computer science. With insights into potential future directions, Coulman underscores the importance of continuous improvement in algorithm performance, while also fostering community interaction in the development of the solver.
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