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Found 8 papers, 2 papers with code
Inter-annotator agreement is not the ceiling of machine learning performance: Evidence from a comprehensive set of simulations
It is commonly claimed that inter-annotator agreement (IAA) is the ceiling of machine learning (ML) performance, i. e., that the agreement between an ML system’s predictions and an annotator can not be higher than the agreement between two annotators.
A Domain-Independent Agent Architecture for Adaptive Operation in Evolving Open Worlds
Model-based reasoning agents are ill-equipped to act in novel situations in which their model of the environment no longer sufficiently represents the world.
Heuristic Search For Physics-Based Problems: Angry Birds in PDDL+
This paper studies how a domain-independent planner and combinatorial search can be employed to play Angry Birds, a well established AI challenge problem.
Characterizing Human Explanation Strategies to Inform the Design of Explainable AI for Building Damage Assessment
Explainable AI (XAI) is a promising means of supporting human-AI collaborations for high-stakes visual detection tasks, such as damage detection tasks from satellite imageries, as fully-automated approaches are unlikely to be perfectly safe and reliable.
COVID-19 India Dataset: Parsing COVID-19 Data in Daily Health Bulletins from States in India
While India has been one of the hotspots of COVID-19, data about the pandemic from the country has proved to be largely inaccessible at scale.
Model Elicitation through Direct Questioning
We show how to generate questions to refine the robot's understanding of the teammate's model.
Plan Explanations as Model Reconciliation -- An Empirical Study
Recent work in explanation generation for decision making agents has looked at how unexplained behavior of autonomous systems can be understood in terms of differences in the model of the system and the human's understanding of the same, and how the explanation process as a result of this mismatch can be then seen as a process of reconciliation of these models.
Texture Synthesis with Recurrent Variational Auto-Encoder
A novel loss function, FLTBNK, is used for training the texture synthesizer.
