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Found 7 papers, 1 papers with code
Mitigating Negative Side Effects via Environment Shaping
The human shapes the environment through minor reconfiguration actions so as to mitigate the impacts of the agent's side effects, without affecting the agent's ability to complete its assigned task.
Learning to Generate Fair Clusters from Demonstrations
Empirical evaluation on three real-world datasets demonstrates the effectiveness of our approach in quickly identifying the underlying fairness and interpretability constraints, which are then used to generate fair and interpretable clusters.
Avoiding Negative Side Effects due to Incomplete Knowledge of AI Systems
Learning to recognize and avoid such negative side effects of an agent's actions is critical to improve the safety and reliability of autonomous systems.
Balancing the Tradeoff Between Clustering Value and Interpretability
The interpretability of the clusters is complemented by generating simple explanations denoting the feature values of the nodes in the clusters, using frequent pattern mining.
Minimizing the Negative Side Effects of Planning with Reduced Models
To that end, we propose planning using a portfolio of reduced models, a planning paradigm that minimizes the negative side effects of planning using reduced models by alternating between different outcome selection approaches.
Lexicographically Ordered Multi-Objective Clustering
We introduce a rich model for multi-objective clustering with lexicographic ordering over objectives and a slack.
Planning in Stochastic Environments with Goal Uncertainty
The framework extends the stochastic shortest path (SSP) model to dynamic environments in which it is impossible to determine the exact goal states ahead of plan execution.
