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—We use case-injected genetic algorithms (CIGARs) to learn to competently play computer strategy games. CIGARs periodically inject individuals that were successful in past games into the population of the GA working on the current game, biasing search toward known successful strategies. Computer strategy games are fundamentally resource allocation games(More)
We use case-injected genetic algorithms for learning how to competently play computer strategy games. Case-injected genetic algorithms combine genetic algorithm search with a case-based memory of past problem solving attempts to improve performance on subsequent similar problems. The case-injected genetic algorithm improves performance on later problems in(More)
We describe an improved method for comparative modeling, RosettaCM, which optimizes a physically realistic all-atom energy function over the conformational space defined by homologous structures. Given a set of sequence alignments, RosettaCM assembles topologies by recombining aligned segments in Cartesian space and building unaligned regions de novo in(More)
— First-person shooter robot controllers (bots) are generally rule-based expert systems written in C/C++. As such, many of the rules are parameterized with values, which are set by the software designer and finalized at compile time. The effectiveness of parameter values is dependent on the knowledge the programmer has about the game. Furthermore,(More)
— We investigate the use of genetic algorithms to play real-time computer strategy games and focus on solving the complex spatial reasoning problems found within these games. To overcome the knowledge acquisition bottleneck found in using traditional expert systems, scripts, and decision trees as done in most game AI, we use genetic algorithms to evolve(More)
We investigate the use of genetic algorithms to play real-time computer strategy games. To overcome the knowledge acquisition bottleneck found in using traditional expert systems, scripts, or decision trees we use genetic algorithms to evolve game players. The spatial decision makers in our game players use influence maps as a basic building block from(More)
We investigate the use of genetic algorithms to evolve AI players for real-time strategy games. To overcome the knowledge acquisition bottleneck found in using traditional expert systems, scripts, or decision trees we evolve players through co-evolution. Our game players are implemented as resource allocation systems. Influence map trees are used to analyze(More)
We present a case-injected genetic algorithm player for Strike Ops, a real-time strategy game. Such strategy games are fundamentally resource allocation optimization problems and our previous work showed that genetic algorithms can play such games by solving the underlying resource allocation problem. This paper shows how we can learn to better respond to(More)
We use a genetic algorithm to explore the space of pathfinding algorithms in Lagoon, a 3D naval real-time strategy game and training simulation. To aid in training, Lagoon tries to provide a rich environment with many agents (boats) that maneuver realistically. A*, the traditional pathfinding algorithm in games is computationally expensive when run for many(More)