Researchers at MIT and the University of Washington have developed a model that can predict an agent's potential computational limitations, and therefore their decision-making process, simply by observing past behaviour. Referred to as an "inference budget," this could enable AI systems to better predict human behaviour. The research paper demonstrates this modelling method within the…
Researchers at MIT and the University of Washington have developed a new way to model human behaviour by accounting for unknown computational constraints that may impact problem-solving abilities. This new model enables an agent, human or machine, to infer another agent's computational constraints from their previous actions. The resulting 'inference budget' can be used to…
Researchers at MIT and the University of Washington have developed a computational model that can predict an intelligent agent's behaviors based on its "inference budget" (i.e. the limits on its computational resources). This was accomplished by using an algorithm that recorded all the decisions made by the agent within a given period of time. They…
Researchers at MIT and the University of Washington have devised a model to predict the behaviour of AI systems and humans. The model factors in the indefinite computational constraints which may hinder an agent's problem-solving skills. By analysing only a few instances of previous actions, the model can predict an agent's future behaviour. The findings…
To build an Artificial Intelligence (AI) system that can work effectively with humans, it's critical to have an accurate model of human behavior. However, humans often act less optimally when making decisions, and these irrational behaviors are challenging to imitate. This is due to computational constraints - a person cannot dedicate decades to finding an…
Researchers from MIT and the University of Washington have developed a computational model to predict human behavior while taking into account the suboptimal decisions humans often make due to computational constraints. The researchers believe such a model could help AI systems anticipate and counterbalance human-derived errors, enhancing the efficacy of AI-human collaboration.
Suboptimal decision-making is characteristic…
Researchers at MIT and the University of Washington have successfully developed a model that can infer an agent's computational constraints from observing a few samples of their past actions. The findings could potentially enhance the ability of AI systems to collaborate more effectively with humans. The scientists found that human decision-making often deviates from optimal,…
MIT and University of Washington researchers have created a method to model both human and machine behaviours, taking into account unknown computational constraints which can limited problem-solving skills. The model infers an "inference budget" from previous actions. The inference budget can then predict the agent's future behaviour. Their technique can be used to predict navigation…
Researchers at MIT and the University of Washington have developed a model that accounts for the sub-optimal decision-making processes in humans, potentially improving the way artificial intelligence can predict human behavior.
Named 'inference budget,' the model infers an agent's computational constraints, whether human or machine, after observing a few traces of their past actions. It…
Researchers from MIT and the University of Washington have developed a method to model the behaviour of an agent, including its computational limitations, predicting future behaviours by examining prior actions. The method applies to both humans and AI, and has a wide range of potential applications, including predicting navigation goals from past routes and forecasting…
In an effort to improve AI systems and their ability to collaborate with humans, scientists are trying to better understand human decision-making, including its suboptimal aspects, and model it in AI. A model for human or AI agent behaviour, developed by researchers at MIT and the University of Washington, takes into account an agent’s unknown…
Machine learning (ML) models are increasingly used by organizations to allocate scarce resources or opportunities, such as for job screening or determining priority for kidney transplant patients. To avoid bias in a model's predictions, users may adjust the data features or calibrate the model's scores to ensure fairness. However, researchers at MIT and Northeastern University…