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Creating efficient and beneficial user agent interaction is a challenging problem. Challenges include improving performance and trust and reducing over and under reliance. We investigate
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operators for these notions. Over the past fifty years, such non-classical logics have proved vital in computer science and logic-based artificial intelligence: after all, any intelligent agent must be able
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operators for these notions. Over the past fifty years, such non-classical logics have proved vital in computer science and logic-based artificial intelligence: after all, any intelligent agent must be able
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) Explainability of Reinforcement Learning Policies for Human-Robot Interaction Decision AI for biodiversity Development of a GIS-Based Model for Active Citizenry Street-Level Environment Recognition On Moving
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Agent-based computational simulations are now widely employed to study the evolution of behaviour, e.g., predator-prey simulations, the evolution of cooperation and altruism, the evolution of niches
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everyone adopts such a free-riding strategy the public good collapses [1]. In this project we study how boundedly rational agents can learn to coordinate their actions for successful collective action. We
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Login Recently added Indigenous (Energy) Explainability of Reinforcement Learning Policies for Human-Robot Interaction Decision AI for biodiversity Development of a GIS-Based Model for Active Citizenry
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on game theoretical modelling, usually employing populations of software agents to emulate the behaviour of human populations. Researchers construct models, usually based on known games, and empirical data
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eat through young crop plants. Wasps sometimes sting humans, but some species make excellent bio-control agents that hunt for caterpillars on our crops, saving the crop's leaves from being eaten
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may hold the ultimate key to understanding task allocation in depth. Beyond biology, the insights gained will also be a key to novel bio-inspired technologies, for example in autonomous multi-agent