Modeling Human Behavior Part II -- Cognitive approaches and Uncertainty
Autor: | Fuchs, Andrew, Passarella, Andrea, Conti, Marco |
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Rok vydání: | 2022 |
Předmět: | |
Druh dokumentu: | Working Paper |
Popis: | As we discussed in Part I of this topic, there is a clear desire to model and comprehend human behavior. Given the popular presupposition of human reasoning as the standard for learning and decision-making, there have been significant efforts and a growing trend in research to replicate these innate human abilities in artificial systems. In Part I, we discussed learning methods which generate a model of behavior from exploration of the system and feedback based on the exhibited behavior as well as topics relating to the use of or accounting for beliefs with respect to applicable skills or mental states of others. In this work, we will continue the discussion from the perspective of methods which focus on the assumed cognitive abilities, limitations, and biases demonstrated in human reasoning. We will arrange these topics as follows (i) methods such as cognitive architectures, cognitive heuristics, and related which demonstrate assumptions of limitations on cognitive resources and how that impacts decisions and (ii) methods which generate and utilize representations of bias or uncertainty to model human decision-making or the future outcomes of decisions. Comment: This is Part 2 of our review (see Modeling Human Behavior Part I - Learning and Belief Approaches) relating to learning and modeling behavior. This work was partially funded by the following projects. European Union's Horizon 2020 research and innovation programme: HumaneAI-Net (No 952026). CHIST-ERA program: SAI project (grant CHIST-ERA-19-XAI-010, funded by MUR, grant number not yet available) |
Databáze: | arXiv |
Externí odkaz: |
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