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Found 10 papers, 6 papers with code
Explaining Reinforcement Learning Agents Through Counterfactual Action Outcomes
Explainable reinforcement learning (XRL) methods aim to help elucidate agent policies and decision-making processes.
ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents
As reinforcement learning methods increasingly amass accomplishments, the need for comprehending their solutions becomes more crucial.
Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents
Methods that help users understand the behavior of such agents can roughly be divided into local explanations that analyze specific decisions of the agents and global explanations that convey the general strategy of the agents.
Dataset and Case Studies for Visual Near-Duplicates Detection in the Context of Social Media
The massive spread of visual content through the web and social media poses both challenges and opportunities.
"I Don't Think So": Summarizing Policy Disagreements for Agent Comparison
In this paper, we propose a novel method for generating dependent and contrastive summaries that emphasize the differences between agent policies by identifying states in which the agents disagree on the best course of action.
VoterFraud2020: a Multi-modal Dataset of Election Fraud Claims on Twitter
The wide spread of unfounded election fraud claims surrounding the U. S. 2020 election had resulted in undermining of trust in the election, culminating in violence inside the U. S. capitol.
Social and Information Networks
Learning to Characterize Matching Experts
Matching is a task at the heart of any data integration process, aimed at identifying correspondences among data elements.
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps
Specifically, we augment strategy summaries that extract important trajectories of states from simulations of the agent with saliency maps which show what information the agent attends to.
Exploring Computational User Models for Agent Policy Summarization
We introduce an imitation learning-based approach to policy summarization; we demonstrate through computational simulations that a mismatch between the model used to extract a summary and the model used to reconstruct the policy results in worse reconstruction quality; and we demonstrate through a human-subject study that people use different models to reconstruct policies in different contexts, and that matching the summary extraction model to these can improve performance.
Frustratingly Easy Truth Discovery
Truth discovery is a general name for a broad range of statistical methods aimed to extract the correct answers to questions, based on multiple answers coming from noisy sources.
