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Found 8 papers, 2 papers with code
Environment Complexity and Nash Equilibria in a Sequential Social Dilemma
Multi-agent reinforcement learning (MARL) methods, while effective in zero-sum or positive-sum games, often yield suboptimal outcomes in general-sum games where cooperation is essential for achieving globally optimal outcomes.
Amortised Design Optimization for Item Response Theory
In education, IRT is used to infer student abilities and characteristics of test items from student responses.
Amortised Experimental Design and Parameter Estimation for User Models of Pointing
User models play an important role in interaction design, supporting automation of interaction design choices.
Online simulator-based experimental design for cognitive model selection
In this paper, we propose BOSMOS: an approach to experimental design that can select between computational models without tractable likelihoods.
Rediscovering Affordance: A Reinforcement Learning Perspective
Affordance refers to the perception of possible actions allowed by an object.
Likelihood-Free Inference in State-Space Models with Unknown Dynamics
Likelihood-free inference (LFI) has been successfully applied to state-space models, where the likelihood of observations is not available but synthetic observations generated by a black-box simulator can be used for inference instead.
Implications of Human Irrationality for Reinforcement Learning
Recent work in the behavioural sciences has begun to overturn the long-held belief that human decision making is irrational, suboptimal and subject to biases.
Inferring Cognitive Models from Data using Approximate Bayesian Computation
An important problem for HCI researchers is to estimate the parameter values of a cognitive model from behavioral data.
