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38
Proceedings of the Seventh AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Behavior Learning-Based Testing of Starcraft Competition Entries
"... In this paper, we apply the idea of testing games by learning interactions with them that cause unwanted behavior of the game to test the competition entries for some of the scenarios of the 2010 StarCraft AI competition. By extending the previously published macro action concept to include macro ac ..."
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In this paper, we apply the idea of testing games by learning interactions with them that cause unwanted behavior of the game to test the competition entries for some of the scenarios of the 2010 StarCraft AI competition. By extending the previously published macro action concept to include macro
Proceedings of the Sixth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Learning Companion Behaviors Using Reinforcement Learning in Games
"... Our goal is to enable Non Player Characters (NPC) in computer games to exhibit natural behaviors. The quality of behaviors affects the game experience especially in storybased games, which rely on player-NPC interactions. We used Reinforcement Learning to enable NPC companions to develop preferences ..."
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Our goal is to enable Non Player Characters (NPC) in computer games to exhibit natural behaviors. The quality of behaviors affects the game experience especially in storybased games, which rely on player-NPC interactions. We used Reinforcement Learning to enable NPC companions to develop
Proceedings, The Eighth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment On Case Base Formation in Real-Time Heuristic Search
"... Real-time heuristic search algorithms obey a constant limit on planning time per move. Agents using these algorithms can execute each move as it is computed, suggesting a strong potential for application to real-time video-game AI. Recently, a breakthrough in real-time heuristic search performance w ..."
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Real-time heuristic search algorithms obey a constant limit on planning time per move. Agents using these algorithms can execute each move as it is computed, suggesting a strong potential for application to real-time video-game AI. Recently, a breakthrough in real-time heuristic search performance was achieved through the use of case-based reasoning. In this framework, the agent optimally solves a set of problems and stores their solutions in a case base. Then, given any new problem, it seeks a similar case in the case base and uses its solution as an aid to solve the problem at hand. A number of ad hoc approaches to the case base formation problem have been proposed and empirically shown to perform well. In this paper, we investigate a theoretically driven approach to solving the problem. We mathematically relate properties of a case base to the suboptimality of the solutions it produces and subsequently develop an algorithm that addresses these properties directly. An empirical evaluation shows our new algorithm outperforms the existing state of the art on contemporary video-game pathfinding benchmarks. 1
Proceedings, The Eighth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment If Not Now, Where? Time and Space Equivalency in Strategy Games
"... Spatiotemporal reasoning is a fundamental contributor to effective problem solving. In an effort to design better problemsolving agents, we examined and evaluated the strategies that humans use to solve Tower Defense puzzles, a complex and popular class of real-time strategy games. A consistent and ..."
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Spatiotemporal reasoning is a fundamental contributor to effective problem solving. In an effort to design better problemsolving agents, we examined and evaluated the strategies that humans use to solve Tower Defense puzzles, a complex and popular class of real-time strategy games. A consistent and unexpected finding was that humans frequently treated time and space as equivalent. Players stated temporal goals but solved spatial problems. An analysis of human data and computer simulations showed that re-representing temporal problems as spatial problems was beneficial, but treating the two separately can lead to higher scores. The work presented here holds several possibilities for level designers and others who design and analyze maps and spatial arrangements for domains requiring strategic reasoning.
Proceedings of the Sixth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment A Monte Carlo Approach for Football Play Generation
"... Learning effective policies in multi-agent adversarial games is a significant challenge since the search space can be prohibitively large when the actions of all the agents are considered simultaneously. Recent advances in Monte Carlo search methods have produced good results in single-agent games l ..."
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Learning effective policies in multi-agent adversarial games is a significant challenge since the search space can be prohibitively large when the actions of all the agents are considered simultaneously. Recent advances in Monte Carlo search methods have produced good results in single-agent games like Go with very large search spaces. In this paper, we propose a variation on the Monte Carlo method, UCT (Upper Confidence Bound Trees), for multi-agent, continuousvalued, adversarial games and demonstrate its utility at generating American football plays for Rush Football 2008. In football, like in many other multi-agent games, the actions of all of the agents are not equally crucial to gameplay success. By automatically identifying key players from historical game play, we can focus the UCT search on player groupings that have the largest impact on yardage gains in a particular formation.
A Comparative Analysis of Story Representations for Interactive Narrative Systems. Third Annual Artificial Intelligence for Interactive Digital Entertainment Conference. Marina del Rey
, 2007
"... Interactive narrative is a blossoming field, with new systems and approaches being developed each year. However, despite this burgeoning array of designs, little work has been done in empirically evaluating and comparing them (Weyhrauch 1997; Nelson and Mateas 2005; Magerko 2007). Such a comparison ..."
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Cited by 7 (1 self)
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Interactive narrative is a blossoming field, with new systems and approaches being developed each year. However, despite this burgeoning array of designs, little work has been done in empirically evaluating and comparing them (Weyhrauch 1997; Nelson and Mateas 2005; Magerko 2007). Such a comparison
Proceedings of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference Active and Interactive Discovery of Goal Selection Knowledge
"... If given manually-crafted goal selection knowledge, goal reasoning agents can dynamically determine which goals they should achieve in complex environments. These agents should instead learn goal selection knowledge through expert interaction. We describe T-ARTUE, a goal reasoning agent that perform ..."
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that performs case-based active and interactive learning to discover goal selection knowledge. We also report tests of its performance in a complex environment. We found that, under some conditions, T-ARTUE can quickly learn goal selection knowledge. 1.
Conference Report: 4th Workshop on Dynamic Perception
"... by the GI section on image understanding in Hamburg in 1996 and continued biennially in Bielefeld and Ulm. It fo-cuses in an interdisciplinary manner on dynamic aspects of biological and machine perception. Special emphasis lies on the promotion of scientific exchange between computer science (neuro ..."
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(neurocomputing and artificial intelligence), psychol-ogy, and the neurosciences. The fourth workshop in this series was chaired by Markus Lappe (University of Münster) and myself and took place at the University of Bochum on November 14 and 15,
Modeling and Augmenting Game Entertainment through . . .
, 2007
"... This paper presents quantitative measurements/metrics of qualitative entertainment features within computer game environments and proposes artificial intelligence (AI) techniques for optimizing entertainment in such interactive systems. A human-verified metric of interest (i.e. player entertainment ..."
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Cited by 7 (1 self)
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This paper presents quantitative measurements/metrics of qualitative entertainment features within computer game environments and proposes artificial intelligence (AI) techniques for optimizing entertainment in such interactive systems. A human-verified metric of interest (i.e. player entertainment
Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10) Design and Implementation of Two-Level Synchronization for an Interactive Music Robot
"... Our goal is to develop an interactive music robot, i.e., a robot that presents a musical expression together with humans. A music interaction requires two important functions: synchronization with the music and musical expression, such as singing and dancing. Many instrument-performing robots are on ..."
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Our goal is to develop an interactive music robot, i.e., a robot that presents a musical expression together with humans. A music interaction requires two important functions: synchronization with the music and musical expression, such as singing and dancing. Many instrument-performing robots
Results 1 - 10
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38