1,721,107 research outputs found
Automation for Regulated Issue Tracking Activities
No full textWe describe the application of automated support for issue tracking and related software engineering activities of development teams at the world's largest medical device manufacturer. We present some challenges and classes of defects found in product software, related artifacts, and the issues which track them. We describe enhanced means for defect detection, data mining and analysis, and other novel support we provide at the time of issue review. Finally, we describe evidence of the positive impact of this support, its adoption, lessons learned and potential next steps.Drew, Touby; Gini, Maria. (2012). Automation for Regulated Issue Tracking Activities. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215888
Episode 123: Artificial Intelligence in Education
No full textRuntime 10:12The Minnesota Daily sat down with CSE professors to explore the rising trend of ChatGPTand its implications in the field of education.Sirovy, Kaylie; Gini, Maria; Chancellor, Stevie. (2023). Episode 123: Artificial Intelligence in Education. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/260784
Lazy max-sum for allocation of tasks with growing costs
No full textWe propose a model for the allocation of agents to tasks when the tasks have a cost which grows over time. Our model accounts for both the natural growth of tasks and the effort of the agents at containing such growth. The objective is to produce solutions that minimize the growth of tasks (potentially stopping such growth) by efficiently coordinating the operations of the agents. This problem has strong spatial and temporal components, as the agents require time not only to work on the tasks but also to move between tasks and during that time the costs of completing the tasks continue to grow. We propose a novel distributed coordination algorithm, called Lazy max-sum, which works well even when the model of the environment has errors. The algorithm handles homogeneous as well as heterogeneous agents, which can do different amounts of work per time unit and have different travel speeds. We show experimentally that the algorithm outperforms other methods in both a simple simulation and the RoboCup Rescue agent simulation. (C) 2018 Elsevier B.V. All rights reserved
Repeated Auctions for Robust Task Execution by a Robot Team
No full textWe present empirical results of an auction-based algorithm for dynamic allocation of tasks to robots. The results have been obtained both in simulation and using real robots. A distinctive feature of our algorithm is its robustness to uncertainties and to robot malfunctions that happen during task execution, when unexpected obstacles, loss of communication, and other delays may prevent a robot from completing its allocated tasks. Therefore tasks not yet achieved are resubmitted for bids every time a task has been completed. This provides an opportunity to improve the allocation of the remaining tasks, enabling the robots to recover from failures and reducing the overall time for task completion.Nanjanath, Maitreyi; Gini, Maria. (2008). Repeated Auctions for Robust Task Execution by a Robot Team. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215775
An Overview of XRobots: A Hierarchical State Machine-Based Language
No full textAssociated research group: Minnesota Extensible Language ToolsThis paper introduces a prototype domain-specific language for programming mobile robots that is based on hierarchical state machines. A novelty of this language is that states are treated as first class entities in the language and thus they can be passed as arguments to other parameterized states. The structure and behavior of the language is presented, along with an example program. Further work and language design challenges are also discussed.Tousignant, Steve; Van Wyk, Eric; Gini, Maria. (2011). An Overview of XRobots: A Hierarchical State Machine-Based Language. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/217405
Improving Play of Monte-Carlo Engines in the Game of Go
No full textWe explore the effects of using a system similar to an opening book to improve the capabilities of computer Go software based on Monte Carlo Tree Search methods. This system operates by matching the board against clusters of board configurations from games played by experts. It does not require an exact match of the current board to be present in the expert games. Experimentation included results from over 120,000 games in tournaments using the open source Go engines Fuego, Orego, Pachi, and Gnugo. The parameters of operating our matching system were explored in over thirty different combinations to find the best results. We find that this system through its filtering or biasing the choice of a next move to a small subset of possible moves can improve play even though this can only be applied effectively to the initial moves of a game.Steinmetz, Erik; Gini, Maria. (2014). Improving Play of Monte-Carlo Engines in the Game of Go. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215956
A Minimally Constrained Environment for the Study of Cooperation
No full textWe describe a simple environment to study cooperation between two agents and a method of achieving cooperation in that environment. The environment consists of randomly generated normal form games with uniformly distributed payoffs. Agents play multiple games against each other, each game drawn independently from the random distribution. This environment provides a good model of the difficulties of cooperating in an ever changing world. Tit-for-Tat cannot be used because moves are not labeled as "cooperate" or "defect", fictitious play cannot be used because the agent never sees the same game twice, and approaches suitable for stochastic games cannot be used because the set of states is not finite. Our agent identifies cooperative moves by assigning an attitude to its opponent and to itself. The attitude determines how much a player values its opponents payoff, i.e how much the player is willing to deviate from strictly self-interested behavior. To cooperate, our agent estimates the attitude of its opponent by observing its moves and reciprocates by setting its own attitude accordingly. We show how the opponent's attitude can be estimated using a particle filter, even when the opponent is changing its attitude.Damer, Steven; Gini, Maria. (2008). A Minimally Constrained Environment for the Study of Cooperation. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215756
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