Retzlaff et al., 2024 - Google Patents
Human-in-the-loop reinforcement learning: A survey and position on requirements, challenges, and opportunitiesRetzlaff et al., 2024
View PDF- Document ID
- 7865024740564774709
- Author
- Retzlaff C
- Das S
- Wayllace C
- Mousavi P
- Afshari M
- Yang T
- Saranti A
- Angerschmid A
- Taylor M
- Holzinger A
- Publication year
- Publication venue
- Journal of Artificial Intelligence Research
External Links
Snippet
Artificial intelligence (AI) and especially reinforcement learning (RL) have the potential to enable agents to learn and perform tasks autonomously with superhuman performance. However, we consider RL as fundamentally a Human-in-the-Loop (HITL) paradigm, even …
- 230000002787 reinforcement 0 title abstract description 44
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
- G09B7/02—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B19/00—Teaching not covered by other main groups of this subclass
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Retzlaff et al. | Human-in-the-loop reinforcement learning: A survey and position on requirements, challenges, and opportunities | |
Xing et al. | Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory | |
Essa et al. | Student success system: risk analytics and data visualization using ensembles of predictive models | |
Tao et al. | Testing and quality validation for ai software–perspectives, issues, and practices | |
Love et al. | Explainable artificial intelligence (XAI): Precepts, models, and opportunities for research in construction | |
Myllyaho et al. | Systematic literature review of validation methods for AI systems | |
Ali et al. | The effects of artificial intelligence applications in educational settings: Challenges and strategies | |
Reddivari et al. | Visual requirements analytics: a framework and case study | |
Goggins et al. | Learning Analytics at" Small" Scale: Exploring a Complexity-Grounded Model for Assessment Automation. | |
Rosa et al. | A framework for searching for general artificial intelligence | |
Sun et al. | A survey of reasoning with foundation models | |
Arumugam et al. | The Algorithmic Odyssey-A Comprehensive Guide to AI Research | |
Robert et al. | Reasoning under uncertainty: Towards collaborative interactive machine learning | |
Kovalerchuk et al. | Modelling phenomena and dynamic logic of phenomena | |
Khuat et al. | The roles and modes of human interactions with automated machine learning systems | |
Israelsen | Algorithmic assurances and self-assessment of competency boundaries in autonomous systems | |
Taghikhah et al. | Machine-assisted agent-based modeling: opening the black box | |
Cain et al. | Navigating design, data, and decision in an age of uncertainty | |
Eldrandaly et al. | Explainable and secure artificial intelligence: taxonomy, cases of study, learned lessons, challenges and future directions | |
Akshay et al. | Machine Learning: A Comprehensive Beginner's Guide | |
Fionda et al. | Tutorials at the web conference 2023 | |
Yan et al. | VizChat: enhancing learning analytics dashboards with contextualised explanations using multimodal generative AI chatbots | |
Kinger et al. | Demystifying the black box: an overview of explainability methods in machine learning | |
Kapler et al. | CauseWorks: A Framework for Transforming User Hypotheses into a Computational Causal Model. | |
Domfeh et al. | Human-Centered Artificial Intelligence, a review |