Decision Theory

Decision theory, the study of making optimal choices under uncertainty, aims to develop frameworks for selecting actions that maximize expected utility or minimize risk. Current research emphasizes robust decision-making in complex scenarios, including multi-agent systems and situations with imperfect predictions, employing techniques like conformal decision theory and Bayesian optimization to improve decision quality and safety. These advancements are crucial for developing reliable autonomous systems and improving decision-making in various fields, from robotics and AI to healthcare and economics, by providing principled methods for handling uncertainty and optimizing outcomes.

Papers

April 25, 2023

Using Intent Estimation and Decision Theory to Support Lifting Motions with a Quasi-Passive Hip Exoskeleton
Thomas Callens, Vincent Ducastel, Joris De Schutter, Erwin Aertbeliën
Optimal Controller Exoskeleton Technology Decision Theory Intention Estimation Lifting Task Passive Exoskeleton

December 13, 2022

Multi-Target Decision Making under Conditions of Severe Uncertainty
Christoph Jansen, Georg Schollmeyer, Thomas Augustin
Decision Making Pareto Optimal Adverse Condition Multi Criterion Decision Decision Theory Probabilistic Information Human Uncertainty

August 9, 2022

Generalized Reinforcement Learning: Experience Particles, Action Operator, Reinforcement Field, Memory Association, and Decision Concepts
Po-Hsiang Chiu, Manfred Huber
Reinforcement Learning Associative Memory Action Model Decision Theory Policy Learning Method

July 12, 2022

Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation
Desi R. Ivanova, Joel Jennings, Cheng Zhang, Adam Foster
Decision Making Experimental Design Contextual Information Decision Theory Causal Machine Treatment Assignment Context Optimization Real World Testing

May 11, 2022

Is calibration a fairness requirement? An argument from the point of view of moral philosophy and decision theory
Michele Loi, Christoph Heitz
Calibration Performance Group Fairness Algorithmic Fairness Argument Quality Decision Theory Moral Philosophy Statistical Fairness False Negative Rate Fairness Condition

March 29, 2022

On Reinforcement Learning, Effect Handlers, and the State Monad
Ugo Dal Lago, Francesco Gavazzo, Alexis Ghyselen
Reinforcement Learning Reinforcement Learning Algorithm Functional Programming Decision Theory Lambda Calculus Monad Transformer

February 10, 2022

Closure operators: Complexity and applications to classification and decision-making
Hamed Hamze Bajgiran, Federico Echenique
Machine Learning Classification Code Financial Application Decision Making Complexity Matter Simple Classifier Decision Theory Compression Operator

December 9, 2021

Machine Learning for Utility Prediction in Argument-Based Computational Persuasion
Ivan Donadello, Anthony Hunter, Stefano Teso, Mauro Dragoni
Machine Learning Spoken Argumentation Target Argument Decision Theory Bayesian Persuasion Utility Assessment Persuasive Technology

Decision Theory

Papers

Using Intent Estimation and Decision Theory to Support Lifting Motions with a Quasi-Passive Hip Exoskeleton

Multi-Target Decision Making under Conditions of Severe Uncertainty

Generalized Reinforcement Learning: Experience Particles, Action Operator, Reinforcement Field, Memory Association, and Decision Concepts

Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation

Is calibration a fairness requirement? An argument from the point of view of moral philosophy and decision theory

On Reinforcement Learning, Effect Handlers, and the State Monad

Closure operators: Complexity and applications to classification and decision-making

Machine Learning for Utility Prediction in Argument-Based Computational Persuasion