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Interaction Algorithm Effect on Human Experience with Reinforcement Learning

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Cobot in LambdaMOO: An Adaptive Social Statistics Agent
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Does the Design of a Robot Influence Its Animacy and Perceived Intelligence?
inferredcites →https://doi.org/10.1016/j.artint.2007.09.009openalex-snapshot
Teachable robots: Understanding human teaching behavior to build more effective robot learners
inferredcites →https://doi.org/10.1016/s0921-8890(02)00372-xopenalex-snapshot
A survey of socially interactive robots
inferredcites →https://doi.org/10.1016/s0921-8890(02)00374-3openalex-snapshot
Anthropomorphism and the social robot
inferredcites →https://doi.org/10.1037/a0021522openalex-snapshot
Blinded by the accent! The minor role of looks in ethnic categorization.
inferredcites →https://doi.org/10.1108/k.1998.27.9.1093.3openalex-snapshot
Reinforcement Learning:
inferredcites →https://doi.org/10.1109/icassp.2006.1659988openalex-snapshot
Pocketsphinx: A Free, Real-Time Continuous Speech Recognition System for Hand-Held Devices
inferredcites →https://doi.org/10.1109/ictai.2012.152openalex-snapshot
Object-Oriented Representation and Hierarchical Reinforcement Learning in Infinite Mario
inferredcites →https://doi.org/10.1109/iros.2008.4651020openalex-snapshot
Learning robot motion control with demonstration and advice-operators
inferredcites →https://doi.org/10.1109/tcds.2016.2628365openalex-snapshot
Learning From Explanations Using Sentiment and Advice in RL
inferredcites →https://doi.org/10.1126/science.7244649openalex-snapshot
Selection by Consequences
inferredcites →https://doi.org/10.1145/375735.376334openalex-snapshot
A social reinforcement learning agent
inferredcites →https://doi.org/10.1145/778712.778756openalex-snapshot
All robots are not created equal
inferredcites →https://doi.org/10.1177/1071181319631171openalex-snapshot
Proceedings of the Human Factors and Ergonomics Society 2019 Annual Meeting
inferredcites →https://doi.org/10.1177/154193120605000909openalex-snapshot
Nasa-Task Load Index (NASA-TLX); 20 Years Later
inferredcites →https://doi.org/10.1177/1541931218621371openalex-snapshot
Characteristics that Influence Perceived Intelligence in AI Design
inferredcites →https://doi.org/10.1518/001872097778543886openalex-snapshot
Humans and Automation: Use, Misuse, Disuse, Abuse
inferredcites →https://doi.org/10.15607/rss.2015.xi.018openalex-snapshot
Grounding English Commands to Reward Functions
inferredcites →https://doi.org/10.1561/1500000011openalex-snapshot
Opinion Mining and Sentiment Analysis
inferredcites →https://doi.org/10.1609/aaai.v25i1.7979openalex-snapshot
Understanding Natural Language Commands for Robotic Navigation and Mobile Manipulation
inferredcites →https://doi.org/10.18653/v1/2020.emnlp-mainopenalex-snapshot
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
inferredcites →https://doi.org/10.18653/v1/d13-1170openalex-snapshot
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
inferredcites →https://doi.org/10.1901/jeab.1990.54-155openalex-snapshot
BURRHUS FREDERIC SKINNER (1904–1990)
inferredcites →https://doi.org/10.2307/1416495openalex-snapshot
The Behavior of Organisms: An Experimental Analysis
inferredcites →https://doi.org/10.2460/ajvr.67.2.323openalex-snapshot
Pharmacokinetics of a novel formulation of ivermectin after administration to goats
inferredcites →https://doi.org/10.3115/1219840.1219855openalex-snapshot
Seeing stars
inferredcites →https://doi.org/10.3115/v1/p14-5010openalex-snapshot
The Stanford CoreNLP Natural Language Processing Toolkit
inferredcites →https://doi.org/10.5555/1838206.1838208openalex-snapshot
Combining manual feedback with subsequent MDP reward signals for reinforcement learning
inferredcites →https://doi.org/10.5555/2615731.2617416openalex-snapshot
An interactive approach for situated task specification through verbal instructions
inferredcites →https://doi.org/10.5555/2936924.2936990openalex-snapshot
Exploration from Demonstration for Interactive Reinforcement Learning
inferredcites →https://doi.org/10.5555/2936924.2937207openalex-snapshot
Policy Shaping in Domains with Multiple Optimal Policies: (Extended Abstract)
inferredcites →https://doi.org/10.65109/mfhl5502openalex-snapshot
Newtonian Action Advice: Integrating Human Verbal Instruction with Reinforcement Learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Toward System Theoretical Foundations for Human–Autonomy Teams
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Interactive Reinforcement Learning for Autonomous Behavior Design
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Interactive Robot Learning: An Overview
inferred← citeshttps://doi.org/10.1007/s10472-024-09956-4openalex-snapshot
Multi-trainer binary feedback interactive reinforcement learning
inferred← citeshttps://doi.org/10.1007/s43154-020-00013-6openalex-snapshot
From Humans and Back: a Survey on Using Machine Learning to both Socially Perceive Humans and Explain to Them Robot Behaviours
inferred← citeshttps://doi.org/10.1016/j.chb.2022.107617openalex-snapshot
Where is the human in human-centered AI? Insights from developer priorities and user experiences
inferred← citeshttps://doi.org/10.1016/j.ijhcs.2024.103277openalex-snapshot
Impact of example-based XAI for neural networks on trust, understanding, and performance
inferred← citeshttps://doi.org/10.1080/07370024.2020.1734931openalex-snapshot
Interactive machine teaching: a human-centered approach to building machine-learned models
inferred← citeshttps://doi.org/10.1109/access.2021.3096662openalex-snapshot
Human-Augmented Prescriptive Analytics With Interactive Multi-Objective Reinforcement Learning
inferred← citeshttps://doi.org/10.1109/ichms53169.2021.9582667openalex-snapshot
Human-Centered AI using Ethical Causality and Learning Representation for Multi-Agent Deep Reinforcement Learning
inferred← citeshttps://doi.org/10.1109/icra48891.2023.10161105openalex-snapshot
Using Learning Curve Predictions to Learn from Incorrect Feedback
inferred← citeshttps://doi.org/10.1109/iros51168.2021.9636515openalex-snapshot
Meta Preference Learning for Fast User Adaptation in Human-Supervisory Multi-Robot Deployments
inferred← citeshttps://doi.org/10.1109/ro-man63969.2025.11217604openalex-snapshot
CHARM: Considering Human Attributes for Reinforcement Modeling
inferred← citeshttps://doi.org/10.1109/ro-man72286.2026.11714706openalex-snapshot
Towards Realistic Oracles: Modeling Teacher Behavior in Human-Interactive Robot Learning
inferred← citeshttps://doi.org/10.1109/thms.2024.3467370openalex-snapshot
The Augmented Intelligence Perspective on Human-in-the-Loop Reinforcement Learning: Review, Concept Designs, and Future Directions
inferred← citeshttps://doi.org/10.1145/3322276.3322379openalex-snapshot
Effect of Interaction Design on the Human Experience with Interactive Reinforcement Learning
inferred← citeshttps://doi.org/10.1145/3357236.3395525openalex-snapshot
A Survey on Interactive Reinforcement Learning
inferred← citeshttps://doi.org/10.1145/3383668.3419938openalex-snapshot
MarioMix
inferred← citeshttps://doi.org/10.1145/3404197openalex-snapshot
Human Feedback as Action Assignment in Interactive Reinforcement Learning
inferred← citeshttps://doi.org/10.1145/3408876openalex-snapshot
Learning to Engage with Interactive Systems
inferred← citeshttps://doi.org/10.1145/3434074.3446361openalex-snapshot
Interactive Reinforcement Learning from Imperfect Teachers
inferred← citeshttps://doi.org/10.1145/3434074.3447189openalex-snapshot
When Oracles Go Wrong: Using Preferences as a Means to Explore
inferred← citeshttps://doi.org/10.1145/3610977.3634925openalex-snapshot
Modeling Variation in Human Feedback with User Inputs: An Exploratory Methodology
inferred← citeshttps://doi.org/10.1145/3640543.3645170openalex-snapshot
Comparing Teaching Strategies of a Machine Learning-based Prosthetic Arm
inferred← citeshttps://doi.org/10.1145/3779297openalex-snapshot
Human-Interactive Robot Learning: Definition, Challenges, and Recommendations
inferred← citeshttps://doi.org/10.1177/0278364920910802openalex-snapshot
Improving user specifications for robot behavior through active preference learning: Framework and evaluation
inferred← citeshttps://doi.org/10.1177/15553434261443032openalex-snapshot
Four Aspirations for Evaluating AI-Infused Technologies: A Narrative Review
inferred← citeshttps://doi.org/10.1177/21695067231192602openalex-snapshot
The Impact of Example-Based Xai on Trust in Highly-Technical Populations
inferred← citeshttps://doi.org/10.1177/2327857924131046openalex-snapshot
Early Investments for Teaming Dividends: A Human-Centered Approach to a Patient Decompensation Prediction Algorithm
inferred← citeshttps://doi.org/10.1609/aiide.v15i1.5237openalex-snapshot
Improving Deep Reinforcement Learning in Minecraft with Action Advice
inferred← citeshttps://doi.org/10.24963/ijcai.2021/599openalex-snapshot
Understanding the Relationship between Interactions and Outcomes in Human-in-the-Loop Machine Learning
inferred← citeshttps://doi.org/10.29007/bjvnopenalex-snapshot
Game-based learning - Teaching Artificial Intelligence to play Minecraft: a systematic literature review
inferred← citeshttps://doi.org/10.3390/s21072514openalex-snapshot
A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning
inferred← citeshttps://doi.org/10.4018/ijeucd.20190101.oa2openalex-snapshot
Analyzing the Effects of Reinforcement Learning to Develop Humanoid Robots
inferred← citeshttps://doi.org/10.48550/arxiv.1904.06764openalex-snapshot
Learning to Engage with Interactive Systems: A Field Study on Deep Reinforcement Learning in a Public Museum
inferred← citeshttps://doi.org/10.48550/arxiv.2105.12944openalex-snapshot
MarioMix: Creating Aligned Playstyles for Bots with Interactive Reinforcement Learning
inferred← citeshttps://doi.org/10.48550/arxiv.2105.12949openalex-snapshot
A Survey on Interactive Reinforcement Learning: Design Principles and Open Challenges
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A Bibliometric Review of Analyzing the Intellectual Structure of the Knowledge Based on AI Chatbot Application from 2005–2022
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Human-Machine Co-Learning : interactive curriculum generation for the acquisition of motor skills
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